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Distilled News

Data liquidity in the age of inference

It’s a special time in the evolutionary history of computing. Oft-used terms like big data, machine learning, and artificial intelligence have become popular descriptors of a broader underlying shift in information processing. While traditional rules-based computing isn’t going anywhere, a new computing paradigm is emerging around probabilistic inference, where digital reasoning is learned from sample data rather than hardcoded with boolean logic. This shift is so significant that a new computing stack is forming around it with emphasis on data engineering, algorithm development, and even novel hardware designs optimized for parallel computing workloads, both within data centers and at endpoints.


Some Social Network Analysis with Python

The following problems appeared in the programming assignments in the coursera course Applied Social Network Analysis in Python. The descriptions of the problems are taken from the assignments. The analysis is done using NetworkX.


Instacart Market Basket Analysis, Winner’s Interview: 2nd place, Kazuki Onodera

Our recent Instacart Market Basket Analysis competition challenged Kagglers to predict which grocery products an Instacart consumer will purchase again and when. Imagine, for example, having milk ready to be added to your cart right when you run out, or knowing that it’s time to stock up again on your favorite ice cream. This focus on understanding temporal behavior patterns makes the problem fairly different from standard item recommendation, where user needs and preferences are often assumed to be relatively constant across short windows of time. Whereas Netflix might be fine assuming you want to watch another movie similar to the one you just watched, it’s less clear that you’ll want to reorder a fresh batch of almond butter or toilet paper if you bought them yesterday. We interviewed Kazuki Onodera (aka ONODERA on Kaggle), a data scientist at Yahoo! JAPAN, to understand how he used complex feature engineering, gradient boosted tree models, and special modeling of the competition’s F1 evaluation metric to win 2nd place.


A Solution to Missing Data: Imputation Using R

Handling missing values is one of the worst nightmares a data analyst dreams of. In situations, a wise analyst ‘imputes’ the missing values instead of dropping them from the data.


How to escape saddlepoints efficiently

Michael Jordan discusses recent results in gradient-based optimization for large-scale data analysis.


Exploratory Data Analysis of Tropical Storms in R

The disastrous impact of recent hurricanes, Harvey and Irma, generated a large influx of data within the online community. I was curious about the history of hurricanes and tropical storms so I found a data set on data.world and started some basic Exploratory data analysis (EDA). EDA is crucial to starting any project. Through EDA you can start to identify errors & inconsistencies in your data, find interesting patterns, see correlations and start to develop hypotheses to test. For most people, basic spreadsheets and charts are handy and provide a great place to start. They are an easy-to-use method to manipulate and visualize your data quickly. Data scientists may cringe at the idea of using a graphical user interface (GUI) to kick-off the EDA process but those tools are very effective and efficient when used properly. However, if you’re reading this, you’re probably trying to take EDA to the next level. The best way to learn is to get your hands dirty, let’s get started.


Multi-Dimensional Reduction and Visualisation with t-SNE

t-SNE is a very powerful technique that can be used for visualising (looking for patterns) in multi-dimensional data. Great things have been said about this technique. In this blog post I did a few experiments with t-SNE in R to learn about this technique and its uses. Its power to visualise complex multi-dimensional data is apparent, as well as its ability to cluster data in an unsupervised way. What’s more, it is also quite clear that t-SNE can aid machine learning algorithms when it comes to prediction and classification. But the inclusion of t-SNE in machine learning algorithms and ensembles has to be ‘crafted’ carefully, since t-SNE was not originally intended for this purpose. All in all, t-SNE is a powerful technique that merits due attention.


30 Essential Data Science, Machine Learning & Deep Learning Cheat Sheets

This collection of data science cheat sheets is not a cheat sheet dump, but a curated list of reference materials spanning a number of disciplines and tools.
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Document worth reading: “A Survey of Neural Network Techniques for Feature Extraction from Text”

This paper aims to catalyze the discussions about text feature extraction techniques using neural network architectures. The research questions discussed in the paper focus on the state-of-the-art neural network techniques that have proven to be useful tools for language processing, language generation, text classification and other computational linguistics tasks. A Survey of Neural Network Techniques for Feature Extraction from Text

Document worth reading: “Image Segmentation Algorithms Overview”

The technology of image segmentation is widely used in medical image processing, face recognition pedestrian detection, etc. The current image segmentation techniques include region-based segmentation, edge detection segmentation, segmentation based on clustering, segmentation based on weakly-supervised learning in CNN, etc. This paper analyzes and summarizes these algorithms of image segmentation, and compares the advantages and disadvantages of different algorithms. Finally, we make a prediction of the development trend of image segmentation with the combination of these algorithms. Image Segmentation Algorithms Overview

Whats new on arXiv

varbvs: Fast Variable Selection for Large-scale Regression

We introduce varbvs, a suite of functions written in R and MATLAB for regression analysis of large-scale data sets using Bayesian variable selection methods. We have developed numerical optimization algorithms based on variational approximation methods that make it feasible to apply Bayesian variable selection to very large data sets. With a focus on examples from genome-wide association studies, we demonstrate that varbvs scales well to data sets with hundreds of thousands of variables and thousands of samples, and has features that facilitate rapid data analyses. Moreover, varbvs allows for extensive model customization, which can be used to incorporate external information into the analysis. We expect that the combination of an easy-to-use interface and robust, scalable algorithms for posterior computation will encourage broader use of Bayesian variable selection in areas of applied statistics and computational biology. The most recent R and MATLAB source code is available for download at Github (https://…/varbvs ), and the R package can be installed from CRAN (https://…/package=varbvs ).


Unsupervised Machine Learning for Networking: Techniques, Applications and Research Challenges

While machine learning and artificial intelligence have long been applied in networking research, the bulk of such works has focused on supervised learning. Recently there has been a rising trend of employing unsupervised machine learning using unstructured raw network data to improve network performance and provide services such as traffic engineering, anomaly detection, Internet traffic classification, and quality of service optimization. The interest in applying unsupervised learning techniques in networking emerges from their great success in other fields such as computer vision, natural language processing, speech recognition, and optimal control (e.g., for developing autonomous self-driving cars). Unsupervised learning is interesting since it can unconstrain us from the need of labeled data and manual handcrafted feature engineering thereby facilitating flexible, general, and automated methods of machine learning. The focus of this survey paper is to provide an overview of the applications of unsupervised learning in the domain of networking. We provide a comprehensive survey highlighting the recent advancements in unsupervised learning techniques and describe their applications for various learning tasks in the context of networking. We also provide a discussion on future directions and open research issues, while also identifying potential pitfalls. While a few survey papers focusing on the applications of machine learning in networking have previously been published, a survey of similar scope and breadth is missing in literature. Through this paper, we advance the state of knowledge by carefully synthesizing the insights from these survey papers while also providing contemporary coverage of recent advances.


On Collaborative Compressive Sensing Systems: The Framework, Design and Algorithm

We propose a collaborative compressive sensing (CCS) framework consisting of a bank of K compressive sensing (CS) systems that share the same sensing matrix but have different sparsifying dictionaries. This CCS system is guaranteed to yield better performance than each individual CS system in a statistical sense, while with the parallel computing strategy, it requires the same time as that needed for each individual CS system to conduct compression and signal recovery. We then provide an approach to designing optimal CCS systems by utilizing a measure that involves both the sensing matrix and dictionaries and hence allows us to simultaneously optimize the sensing matrix and all the K dictionaries under the same scheme. An alternating minimization-based algorithm is derived for solving the corresponding optimal design problem. We provide a rigorous convergence analysis to show that the proposed algorithm is convergent. Experiments with real images are carried out and show that the proposed CCS system significantly improves on existing CS systems in terms of the signal recovery accuracy.


Deep Reinforcement Learning for Event-Driven Multi-Agent Decision Processes

The incorporation of macro-actions (temporally extended actions) into multi-agent decision problems has the potential to address the curse of dimensionality associated with such decision problems. Since macro-actions last for stochastic durations, multiple agents executing decentralized policies in cooperative environments must act asynchronously. We present an algorithm that modifies Generalized Advantage Estimation for temporally extended actions, allowing a state-of-the-art policy optimization algorithm to optimize policies in Dec-POMDPs in which agents act asynchronously. We show that our algorithm is capable of learning optimal policies in two cooperative domains, one involving real-time bus holding control and one involving wildfire fighting with unmanned aircraft. Our algorithm works by framing problems as ‘event-driven decision processes,’ which are scenarios where the sequence and timing of actions and events are random and governed by an underlying stochastic process. In addition to optimizing policies with continuous state and action spaces, our algorithm also facilitates the use of event-driven simulators, which do not require time to be discretized into time-steps. We demonstrate the benefit of using event-driven simulation in the context of multiple agents taking asynchronous actions. We show that fixed time-step simulation risks obfuscating the sequence in which closely-separated events occur, adversely affecting the policies learned. Additionally, we show that arbitrarily shrinking the time-step scales poorly with the number of agents.


Curriculum Learning of Visual Attribute Clusters for Multi-Task Classification

Visual attributes, from simple objects (e.g., backpacks, hats) to soft-biometrics (e.g., gender, height, clothing) have proven to be a powerful representational approach for many applications such as image description and human identification. In this paper, we introduce a novel method to combine the advantages of both multi-task and curriculum learning in a visual attribute classification framework. Individual tasks are grouped after performing hierarchical clustering based on their correlation. The clusters of tasks are learned in a curriculum learning setup by transferring knowledge between clusters. The learning process within each cluster is performed in a multi-task classification setup. By leveraging the acquired knowledge, we speed-up the process and improve performance. We demonstrate the effectiveness of our method via ablation studies and a detailed analysis of the covariates, on a variety of publicly available datasets of humans standing with their full-body visible. Extensive experimentation has proven that the proposed approach boosts the performance by 4% to 10%.


A textual transform of multivariate time-series for prognostics

Prognostics or early detection of incipient faults is an important industrial challenge for condition-based and preventive maintenance. Physics-based approaches to modeling fault progression are infeasible due to multiple interacting components, uncontrolled environmental factors and observability constraints. Moreover, such approaches to prognostics do not generalize to new domains. Consequently, domain-agnostic data-driven machine learning approaches to prognostics are desirable. Damage progression is a path-dependent process and explicitly modeling the temporal patterns is critical for accurate estimation of both the current damage state and its progression leading to total failure. In this paper, we present a novel data-driven approach to prognostics that employs a novel textual representation of multivariate temporal sensor observations for predicting the future health state of the monitored equipment early in its life. This representation enables us to utilize well-understood concepts from text-mining for modeling, prediction and understanding distress patterns in a domain agnostic way. The approach has been deployed and successfully tested on large scale multivariate time-series data from commercial aircraft engines. We report experiments on well-known publicly available benchmark datasets and simulation datasets. The proposed approach is shown to be superior in terms of prediction accuracy, lead time to prediction and interpretability.


OptionGAN: Learning Joint Reward-Policy Options using Generative Adversarial Inverse Reinforcement Learning

Reinforcement learning has shown promise in learning policies that can solve complex problems. However, manually specifying a good reward function can be difficult, especially for intricate tasks. Inverse reinforcement learning offers a useful paradigm to learn the underlying reward function directly from expert demonstrations. Yet in reality, the corpus of demonstrations may contain trajectories arising from a diverse set of underlying reward functions rather than a single one. Thus, in inverse reinforcement learning, it is useful to consider such a decomposition. The options framework in reinforcement learning is specifically designed to decompose policies in a similar light. We therefore extend the options framework and propose a method to simultaneously recover reward options in addition to policy options. We leverage adversarial methods to learn joint reward-policy options using only observed expert states. We show that this approach works well in both simple and complex continuous control tasks and shows significant performance increases in one-shot transfer learning.


Contrastive Principal Component Analysis

We present a new technique called contrastive principal component analysis (cPCA) that is designed to discover low-dimensional structure that is unique to a dataset, or enriched in one dataset relative to other data. The technique is a generalization of standard PCA, for the setting where multiple datasets are available — e.g. a treatment and a control group, or a mixed versus a homogeneous population — and the goal is to explore patterns that are specific to one of the datasets. We conduct a wide variety of experiments in which cPCA identifies important dataset-specific patterns that are missed by PCA, demonstrating that it is useful for many applications: subgroup discovery, visualizing trends, feature selection, denoising, and data-dependent standardization. We provide geometrical interpretations of cPCA and show that it satisfies desirable theoretical guarantees. We also extend cPCA to nonlinear settings in the form of kernel cPCA. We have released our code as a python package and documentation is on Github.


SBG-Sketch: A Self-Balanced Sketch for Labeled-Graph Stream Summarization

Applications in various domains rely on processing graph streams, e.g., communication logs of a cloud-troubleshooting system, road-network traffic updates, and interactions on a social network. A labeled-graph stream refers to a sequence of streamed edges that form a labeled graph. Label-aware applications need to filter the graph stream before performing a graph operation. Due to the large volume and high velocity of these streams, it is often more practical to incrementally build a lossy-compressed version of the graph, and use this lossy version to approximately evaluate graph queries. Challenges arise when the queries are unknown in advance but are associated with filtering predicates based on edge labels. Surprisingly common, and especially challenging, are labeled-graph streams that have highly skewed label distributions that might also vary over time. This paper introduces Self-Balanced Graph Sketch (SBG-Sketch, for short), a graphical sketch for summarizing and querying labeled-graph streams that can cope with all these challenges. SBG-Sketch maintains synopsis for both the edge attributes (e.g., edge weight) as well as the topology of the streamed graph. SBG-Sketch allows efficient processing of graph-traversal queries, e.g., reachability queries. Experimental results over a variety of real graph streams show SBG-Sketch to reduce the estimation errors of state-of-the-art methods by up to 99%.


VCExplorer: A Interactive Graph Exploration Framework Based on Hub Vertices with Graph Consolidation

Graphs have been widely used to model different information networks, such as the Web, biological networks and social networks (e.g. Twitter). Due to the size and complexity of these graphs, how to explore and utilize these graphs has become a very challenging problem. In this paper, we propose, VCExplorer, a new interactive graph exploration framework that integrates the strengths of graph visualization and graph summarization. Unlike existing graph visualization tools where vertices of a graph may be clustered into a smaller collection of super/virtual vertices, VCExplorer displays a small number of actual source graph vertices (called hubs) and summaries of the information between these vertices. We refer to such a graph as a HA-graph (Hub-based Aggregation Graph). This allows users to appreciate the relationship between the hubs, rather than super/virtual vertices. Users can navigate through the HA- graph by ‘drilling down’ into the summaries between hubs to display more hubs. We illustrate how the graph aggregation techniques can be integrated into the exploring framework as the consolidated information to users. In addition, we propose efficient graph aggregation algorithms over multiple subgraphs via computation sharing. Extensive experimental evaluations have been conducted using both real and synthetic datasets and the results indicate the effectiveness and efficiency of VCExplorer for exploration.


Temporal Pattern Mining from Evolving Networks

Recently, evolving networks are becoming a suitable form to model many real-world complex systems, due to their peculiarities to represent the systems and their constituting entities, the interactions between the entities and the time-variability of their structure and properties. Designing computational models able to analyze evolving networks becomes relevant in many applications. The goal of this research project is to evaluate the possible contribution of temporal pattern mining techniques in the analysis of evolving networks. In particular, we aim at exploiting available snapshots for the recognition of valuable and potentially useful knowledge about the temporal dynamics exhibited by the network over the time, without making any prior assumption about the underlying evolutionary schema. Pattern-based approaches of temporal pattern mining can be exploited to detect and characterize changes exhibited by a network over the time, starting from observed snapshots.


Distributed Lance-William Clustering Algorithm

One important tool is the optimal clustering of data into useful categories. Dividing similar objects into a smaller number of clusters is of importance in many applications. These include search engines, monitoring of academic performance, biology and wireless networks. We first discuss a number of clustering methods. We present a parallel algorithm for the efficient clustering of objects into groups based on their similarity to each other. The input consists of an n by n distance matrix. This matrix would have a distance ranking for each pair of objects. The smaller the number, the more similar the two objects are to each other. We utilize parallel processors to calculate a hierarchal cluster of these n items based on this matrix. Another advantage of our method is distribution of the large n by n matrix. We have implemented our algorithm and have found it to be scalable both in terms of processing speed and storage.


Doctoral Advisor or Medical Condition: Towards Entity-specific Rankings of Knowledge Base Properties [Extended Version]

In knowledge bases such as Wikidata, it is possible to assert a large set of properties for entities, ranging from generic ones such as name and place of birth to highly profession-specific or background-specific ones such as doctoral advisor or medical condition. Determining a preference or ranking in this large set is a challenge in tasks such as prioritisation of edits or natural-language generation. Most previous approaches to ranking knowledge base properties are purely data-driven, that is, as we show, mistake frequency for interestingness. In this work, we have developed a human-annotated dataset of 350 preference judgments among pairs of knowledge base properties for fixed entities. From this set, we isolate a subset of pairs for which humans show a high level of agreement (87.5% on average). We show, however, that baseline and state-of-the-art techniques achieve only 61.3% precision in predicting human preferences for this subset. We then analyze what contributes to one property being rated as more important than another one, and identify that at least three factors play a role, namely (i) general frequency, (ii) applicability to similar entities and (iii) semantic similarity between property and entity. We experimentally analyze the contribution of each factor and show that a combination of techniques addressing all the three factors achieves 74% precision on the task. The dataset is available at http://www.kaggle.com/srazniewski/wikidatapropertyranking.


ProbeSim: Scalable Single-Source and Top-k SimRank Computations on Dynamic Graphs

Single-source and top-k SimRank queries are two important types of similarity search in graphs with numerous applications in web mining, social network analysis, spam detection, etc. A plethora of techniques have been proposed for these two types of queries, but very few can efficiently support similarity search over large dynamic graphs, due to either significant preprocessing time or large space overheads. This paper presents ProbeSim, an index-free algorithm for single-source and top-k SimRank queries that provides a non-trivial theoretical guarantee in the absolute error of query results. ProbeSim estimates SimRank similarities without precomputing any indexing structures, and thus can naturally support real-time SimRank queries on dynamic graphs. Besides the theoretical guarantee, ProbeSim also offers satisfying practical efficiency and effectiveness due to several non-trivial optimizations. We conduct extensive experiments on a number of benchmark datasets, which demonstrate that our solutions significantly outperform the existing methods in terms of efficiency and effectiveness. Notably, our experiments include the first empirical study that evaluates the effectiveness of SimRank algorithms on graphs with billion edges, using the idea of pooling.


Interplay of Coulomb interactions and disorder in three dimensional quadratic band crossings without time-reversal or particle-hole symmetry
Orbits for eighteen visual binaries and two double-line spectroscopic binaries observed with HRCAM on the CTIO SOAR 4m telescope, using a new Bayesian orbit code based on Markov Chain Monte Carlo
A Driven Tagged Particle in Asymmetric Simple Exclusion Processes
Orthogonal Series Density Estimation for Complex Surveys
Time-Optimal Collaborative Guidance using the Generalized Hopf Formula
On Upper Approximations of Pareto Fronts
Queuing with Heterogeneous Users: Block Probability and Sojourn times
A Destroying Driven Tagged Particle in Symmetric Simple Exclusion Processes
Derivation of Network Reprogramming Protocol with Z3
Optimal projection of observations in a Bayesian setting
High-dimensional posterior consistency for hierarchical non-local priors in regression
Yaglom limits for R-transient chains with non-trivial Martin boundary
A Server-based Approach for Predictable GPU Access with Improved Analysis
A Memristive Neural Network Computing Engine using CMOS-Compatible Charge-Trap-Transistor (CTT)
A PAC-Bayesian Analysis of Randomized Learning with Application to Stochastic Gradient Descent
Learning of Coordination Policies for Robotic Swarms
Localization in the Disordered Holstein model
Secure Beamforming in Full-Duplex SWIPT Systems
Dynamic Cross-Layer Beamforming in Hybrid Powered Communication Systems With Harvest-Use-Trade Strategy
Multilevel mixed effects parametric survival analysis
Estimating model evidence using ensemble-based data assimilation with localization – The model selection problem
An Attention-based Collaboration Framework for Multi-View Network Representation Learning
Construction C*: an improved version of Construction C
On Graphs and the Gotsman-Linial Conjecture for d = 2
Distributed event-triggered control for multi-agent formation stabilization and tracking
Unique Information via Dependency Constraints
Verifying Properties of Binarized Deep Neural Networks
Think Globally, Embed Locally — Locally Linear Meta-embedding of Words
An Optimality Proof for the PairDiff operator for Representing Relations between Words
Controllability and data-driven identification of bipartite consensus on nonlinear signed networks
Deep Lattice Networks and Partial Monotonic Functions
Random matrices: repulsion in spectrum
Concentration of distances in Wigner matrices
Sieve: Actionable Insights from Monitored Metrics in Microservices
Property Testing in High Dimensional Ising models
A Voting-Based System for Ethical Decision Making
Higher Distance Energies and Expanders with Structure
Blind Estimation of Sparse Broadband Massive MIMO Channels with Ideal and One-bit ADCs
Subset Testing and Analysis of Multiple Phenotypes (STAMP)
Reversible Joint Hilbert and Linear Canonical Transform Without Distortion
Online Learning of a Memory for Learning Rates
Empowering In-Memory Relational Database Engines with Native Graph Processing
Covering Numbers for Semicontinuous Functions
Some inequalities for $k$-colored partition functions
The cohomology of abelian Hessenberg varieties and the Stanley-Stembridge conjecture
Measuring Player Retention and Monetization using the Mean Cumulative Function
Equilibrium fluctuations for the weakly asymmetric discrete Atlas model
SegFlow: Joint Learning for Video Object Segmentation and Optical Flow
The Fourth Characteristic of a Semimartingale
A shared latent space matrix factorisation method for recommending new trial evidence for systematic review updates
Exponential concentration for zeroes of stationary Gaussian processes
Transfer learning from synthetic to real images using variational autoencoders for robotic applications
Real-time Semantic Segmentation of Crop and Weed for Precision Agriculture Robots Leveraging Background Knowledge in CNNs
Latent Embeddings for Collective Activity Recognition
A Central Limit Theorem for Fleming-Viot Particle Systems with Hard Killing
Information-Coupled Turbo Codes for LTE Systems
Careful prior specification avoids incautious inference for log-Gaussian Cox point processes
Stochastic Channel Modeling for Diffusive Mobile Molecular Communication Systems
A stencil scaling approach for accelerating matrix-free finite element implementations
Complexity of Finding Perfect Bipartite Matchings Minimizing the Number of Intersecting Edges
The Life in 1-Consensus
Block-Diagonal Solutions to Lyapunov Inequalities and Generalisations of Diagonal Dominance
Efficient Graph Edit Distance Computation and Verification via Anchor-aware Lower Bound Estimation
On $2$-chains inside thin subsets of $\mathbb{R}^d$ and product of distances
Affordable and Energy-Efficient Cloud Computing Clusters: The Bolzano Raspberry Pi Cloud Cluster Experiment
Updating the silent speech challenge benchmark with deep learning
Miscorrection-free Decoding of Staircase Codes
Time-dependent reflection at the localization transition
Differential transcendence & algebraicity criteria for the series counting weighted quadrant walks
Atomic Norm Denoising-Based Joint Channel Estimation and Faulty Antenna Detection for Massive MIMO
Higher Order Concentration of Measure
UnDeepVO: Monocular Visual Odometry through Unsupervised Deep Learning
Towards a better understanding of the matrix product function approximation algorithm in application to quantum physics
Berry-Esseen Bounds for typical weighted sums
Bandits with Delayed Anonymous Feedback
A note on the 4-girth-thickness of K_{n,n,n}
Specification tests in semiparametric transformation models
On Energy Efficient Uplink Multi-User MIMO with Shared LNA Control
Using marginal structural models to adjust for treatment drop-in when developing clinical prediction models
Learning quadrangulated patches for 3D shape parameterization and completion
Open Source Dataset and Deep Learning Models for Online Digit Gesture Recognition on Touchscreens
Integrating hyper-parameter uncertainties in a multi-fidelity Bayesian model for the estimation of a probability of failure
Forbidden Subgraphs for Chorded Pancyclicity
The random pinning model with correlated disorder given by a renewal set
De-identification of medical records using conditional random fields and long short-term memory networks
EMR-based medical knowledge representation and inference via Markov random fields and distributed representation learning
Linear Quadratic Games with Costly Measurements
Using Parameterized Black-Box Priors to Scale Up Model-Based Policy Search for Robotics
Constructing a Hierarchical User Interest Structure based on User Profiles
Bayesian Optimization with Automatic Prior Selection for Data-Efficient Direct Policy Search
A Byzantine Fault-Tolerant Ordering Service for the Hyperledger Fabric Blockchain Platform
Stock-out Prediction in Multi-echelon Networks
Spatial features of synaptic adaptation affecting learning performance
NOMA Assisted Wireless Caching: Strategies and Performance Analysis
Iterated Stochastic Integrals in Infinite Dimensions – Approximation and Error Estimates
Stochastic Burgers’ Equation on the Real Line: Regularity and Moment Estimates
Synchronization in Kuramoto-Sakaguchi ensembles with competing influence of common noise and global coupling
An Expectation Conditional Maximization approach for Gaussian graphical models
New Examples of Dimension Zero Categories
Deep Reinforcement Learning for Dexterous Manipulation with Concept Networks
Characterization and enumeration of 3-regular permutation graphs
Error-tolerant Multisecant Method for Nonlinearly Constrained Optimization
Equilibrium-Independent Dissipativity with Quadratic Supply Rates
Text Compression for Sentiment Analysis via Evolutionary Algorithms

If you did not already know

Pattern Sequence based Forecasting (PSF) google
This paper discusses about PSF, an R package for Pattern Sequence based Forecasting (PSF) algorithm used for univariate time series future prediction. The PSF algorithm consists of two major parts: clustering and prediction techniques. Clustering part includes selection of cluster size and then labeling of time series data with reference to various clusters. Whereas, the prediction part include functions like optimum window size selection for specific patterns and prediction of future values with reference to past pattern sequences. The PSF package consists of various functions to implement PSF algorithm. It also contains a function, which automates all other functions to obtain optimum prediction results. The aim of this package is to promote PSF algorithm and to ease its implementation with minimum efforts. This paper describe all the functions in PSF package with their syntax and simple examples. Finally, the usefulness of this package is discussed by comparing it with auto.arima, a well known time series forecasting function available on CRAN repository. …

Spinnaker google
Spinnaker is an open source, multi-cloud continuous delivery platform for releasing software changes with high velocity and confidence. …

Spreadmart google
A spreadmart (spreadsheet data mart) is a situation in which a company’s employees has inconsistent views of corporate data because each department relies on the data from their own spreadsheets. …

R Packages worth a look

Core Inflation (Inflation)
Provides access to core inflation functions. Four different core inflation functions are provided. The well known trimmed means, exclusion and double weighing methods, alongside the new Triple Filter method introduced in Ferreira et al. (2016) <https://goo.gl/UYLhcj>.

Give your Dependencies Stars on GitHub! (ThankYouStars)
A tool for starring GitHub repositories.

pinp’ is not ‘PNAS’ (pinp)
A ‘PNAS’-alike style for ‘rmarkdown’, derived from the ‘Proceedings of the National Academy of Sciences of the United States of America’ (PNAS, see <https://www.pnas.org> ) LaTeX style, and adapted for use with ‘markdown’ and ‘pandoc’.

Transitive Index Numbers for Cross-Sections and Panel Data (multilaterals)
Computing transitive (and non-transitive) index numbers (Coelli et al., 2005 <doi:10.1007/b136381>) for cross-sections and panel data. For the calculation of transitive indexes, the EKS (Coelli et al., 2005 <doi:10.1007/b136381>; Rao et al., 2002 <doi:10.1007/978-1-4615-0851-9_4>) and Minimum spanning tree (Hill, 2004 <doi:10.1257/0002828043052178>) methods are implemented. Traditional fixed-base and chained indexes, and their growth rates, can also be derived using the Paasche, Laspeyres, Fisher and Tornqvist formulas.

Spatial Floor Simulation (Isotropic) (SpatialFloor)
Spatial floor simulation with exponential/Gaussian variance-covariance function (isotropic), with specification of distance function, nugget, sill, range. The methodology follows Nole A.C. Cressie (2015) <doi:10.1002/9781119115151>.

Distilled News

Introduction to Pseudo-Labelling : A Semi-Supervised learning technique

We have made huge progress in solving Supervised machine learning problems. That also means that we need a lot of data to build our image classifiers or sales forecasters. The algorithms search patterns through the data again and again. But, that is not how human mind learns. A human brain does not require millions of data for training with multiple iterations of going through the same image for understanding a topic. All it needs is a few guiding points to train itself on the underlying patterns. Clearly, we are missing something in current machine learning approaches. Thankfully, there is a line of research which specifically caters to this question. Can we build a system capable of requiring minimal amount of supervision which can learn majority of the tasks on its own. In this article, I would like to cover one such technique called pseudo-labelling. I will give an intuitive explanation of what pseudo-labelling is and then provide a hands-on implementation of it.


Introducing: Unity Machine Learning Agents

Our two previous blog entries implied that there is a role games can play in driving the development of Reinforcement Learning algorithms. As the world’s most popular creation engine, Unity is at the crossroads between machine learning and gaming. It is critical to our mission to enable machine learning researchers with the most powerful training scenarios, and for us to give back to the gaming community by enabling them to utilize the latest machine learning technologies. As the first step in this endeavor, we are excited to introduce Unity Machine Learning Agents.


GAN-Collection: Collection of various GAN models implemented in torch7

Torch implementation of various types of GANs (e.g. DCGAN, ALI, Context-encoder, DiscoGAN, CycleGAN).


AI industry overview: Revisiting Big Data, Big Model, and Big Compute in the AI era

AI Conference chairs Ben Lorica and Roger Chen reveal the current AI trends they’ve observed in industry.


Monte Carlo Simulations & the “SimDesign” Package in R

Monte Carlo simulations (MCSs) provide important information about statistical phenomena that would be impossible to assess otherwise. This article introduces MCS methods and their applications to research and statistical pedagogy using a novel software package for the R Project for Statistical Computing constructed to lessen the often steep learning curve when organizing simulation code. A primary goal of this article is to demonstrate how well-suited MCS designs are to classroom demonstrations, and how they provide a hands-on method for students to become acquainted with complex statistical concepts. In this article, essential programming aspects for writing MCS code in R are overviewed, multiple applied examples with relevant code are provided, and the benefits of using a generate-analyze-summarize coding structure over the typical “for-loop” strategy are discussed.


Answer probability questions with simulation (part-2)

This is the second exercise set on answering probability questions with simulation. Finishing the first exercise set is not a prerequisite. The difficulty level is about the same – thus if you are looking for a challenge aim at writing up faster more elegant algorithms. As always, it pays off to read the instructions carefully and think about what the solution should be before starting to code. Often this helps you weed out irrelevant information that can otherwise make your algorithm unnecessarily complicated and slow.


Enterprise-ready dashboards with Shiny and databases

Inside the enterprise, a dashboard is expected to have up-to-the-minute information, to have a fast response time despite the large amount of data that supports it, and to be available on any device. An end user may expect that clicking on a bar or column inside a plot will result in either a more detailed report, or a list of the actual records that make up that number. This article will cover how to use a set of R packages, along with Shiny, to meet those requirements.


Networks with R

In order to practice with network data with R, we have been playing with the Padgett (1994) Florentine’s wedding dataset (discussed in the lecture).


Comparing Trump and Clinton’s Facebook pages during the US presidential election, 2016

R has a lot of packages for users to analyse posts on social media. As an experiment in this field, I decided to start with the biggest one: Facebook. I decided to look at the Facebook activity of Donald Trump and Hillary Clinton during the 2016 presidential election in the United States. The winner may be more famous for his Twitter account than his Facebook one, but he still used it to great effect to help pick off his Republican rivals in the primaries and to attack Hillary Clinton in the general election. For this work we’re going to be using the Rfacebook package developed by Pablo Barbera, plus his excellent how-to guide.

Book Memo: “Visual Analytics of Movement”

Many important planning decisions in society and business depend on proper knowledge and a correct understanding of movement, be it in transportation, logistics, biology, or the life sciences. Today the widespread use of mobile phones and technologies like GPS and RFID provides an immense amount of data on location and movement. What is needed are new methods of visualization and algorithmic data analysis that are tightly integrated and complement each other to allow end-users and analysts to extract useful knowledge from these extremely large data volumes. This is exactly the topic of this book. As the authors show, modern visual analytics techniques are ready to tackle the enormous challenges brought about by movement data, and the technology and software needed to exploit them are available today. The authors start by illustrating the different kinds of data available to describe movement, from individual trajectories of single objects to multiple trajectories of many objects, and then proceed to detail a conceptual framework, which provides the basis for a fundamental understanding of movement data. With this basis, they move on to more practical and technical aspects, focusing on how to transform movement data to make it more useful, and on the infrastructure necessary for performing visual analytics in practice. In so doing they demonstrate that visual analytics of movement data can yield exciting insights into the behavior of moving persons and objects, but can also lead to an understanding of the events that transpire when things move. Throughout the book, they use sample applications from various domains and illustrate the examples with graphical depictions of both the interactive displays and the analysis results. In summary, readers will benefit from this detailed description of the state of the art in visual analytics in various ways. Researchers will appreciate the scientific precision involved, software technologists will find essential information on algorithms and systems, and practitioners will profit from readily accessible examples with detailed illustrations for practical purposes.

Whats new on arXiv

How intelligent are convolutional neural networks?

Motivated by the Gestalt pattern theory, and the Winograd Challenge for language understanding, we design synthetic experiments to investigate a deep learning algorithm’s ability to infer simple (at least for human) visual concepts, such as symmetry, from examples. A visual concept is represented by randomly generated, positive as well as negative, example images. We then test the ability and speed of algorithms (and humans) to learn the concept from these images. The training and testing are performed progressively in multiple rounds, with each subsequent round deliberately designed to be more complex and confusing than the previous round(s), especially if the concept was not grasped by the learner. However, if the concept was understood, all the deliberate tests would become trivially easy. Our experiments show that humans can often infer a semantic concept quickly after looking at only a very small number of examples (this is often referred to as an ‘aha moment’: a moment of sudden realization), and performs perfectly during all testing rounds (except for careless mistakes). On the contrary, deep convolutional neural networks (DCNN) could approximate some concepts statistically, but only after seeing many (x10^4) more examples. And it will still make obvious mistakes, especially during deliberate testing rounds or on samples outside the training distributions. This signals a lack of true ‘understanding’, or a failure to reach the right ‘formula’ for the semantics. We did find that some concepts are easier for DCNN than others. For example, simple ‘counting’ is more learnable than ‘symmetry’, while ‘uniformity’ or ‘conformance’ are much more difficult for DCNN to learn. To conclude, we propose an ‘Aha Challenge’ for visual perception, calling for focused and quantitative research on Gestalt-style machine intelligence using limited training examples.


PrivyNet: A Flexible Framework for Privacy-Preserving Deep Neural Network Training with A Fine-Grained Privacy Control

Massive data exist among user local platforms that usually cannot support deep neural network (DNN) training due to computation and storage resource constraints. Cloud-based training schemes can provide beneficial services, but rely on excessive user data collection, which can lead to potential privacy risks and violations. In this paper, we propose PrivyNet, a flexible framework to enable DNN training on the cloud while protecting the data privacy simultaneously. We propose to split the DNNs into two parts and deploy them separately onto the local platforms and the cloud. The local neural network (NN) is used for feature extraction. To avoid local training, we rely on the idea of transfer learning and derive the local NNs by extracting the initial layers from pre-trained NNs. We identify and compare three factors that determine the topology of the local NN, including the number of layers, the depth of output channels, and the subset of selected channels. We also propose a hierarchical strategy to determine the local NN topology, which is flexible to optimize the accuracy of the target learning task under the constraints on privacy loss, local computation, and storage. To validate PrivyNet, we use the convolutional NN (CNN) based image classification task as an example and characterize the dependency of privacy loss and accuracy on the local NN topology in detail. We also demonstrate that PrivyNet is efficient and can help explore and optimize the trade-off between privacy loss and accuracy.


DropoutDAgger: A Bayesian Approach to Safe Imitation Learning

While imitation learning is becoming common practice in robotics, this approach often suffers from data mismatch and compounding errors. DAgger is an iterative algorithm that addresses these issues by continually aggregating training data from both the expert and novice policies, but does not consider the impact of safety. We present a probabilistic extension to DAgger, which uses the distribution over actions provided by the novice policy, for a given observation. Our method, which we call DropoutDAgger, uses dropout to train the novice as a Bayesian neural network that provides insight to its confidence. Using the distribution over the novice’s actions, we estimate a probabilistic measure of safety with respect to the expert action, tuned to balance exploration and exploitation. The utility of this approach is evaluated on the MuJoCo HalfCheetah and in a simple driving experiment, demonstrating improved performance and safety compared to other DAgger variants and classic imitation learning.


Learning Low-Dimensional Metrics

This paper investigates the theoretical foundations of metric learning, focused on three key questions that are not fully addressed in prior work: 1) we consider learning general low-dimensional (low-rank) metrics as well as sparse metrics; 2) we develop upper and lower (minimax)bounds on the generalization error; 3) we quantify the sample complexity of metric learning in terms of the dimension of the feature space and the dimension/rank of the underlying metric;4) we also bound the accuracy of the learned metric relative to the underlying true generative metric. All the results involve novel mathematical approaches to the metric learning problem, and lso shed new light on the special case of ordinal embedding (aka non-metric multidimensional scaling).


Robustness of Neural Networks against Storage Media Errors

We study the trade-offs between storage/bandwidth and prediction accuracy of neural networks that are stored in noisy media. Conventionally, it is assumed that all parameters (e.g., weight and biases) of a trained neural network are stored as binary arrays and are error-free. This assumption is based upon the implementation of error correction codes (ECCs) that correct potential bit flips in storage media. However, ECCs add storage overhead and cause bandwidth reduction when loading the trained parameters during the inference. We study the robustness of deep neural networks when bit errors exist but ECCs are turned off for different neural network models and datasets. It is observed that more sophisticated models and datasets are more vulnerable to errors in their trained parameters. We propose a simple detection approach that can universally improve the robustness, which in some cases can be improved by orders of magnitude. We also propose an alternative binary representation of the parameters such that the distortion brought by bit flips is reduced and even theoretically vanishing when the number of bits to represent a parameter increases.


A Survey of Machine Learning for Big Code and Naturalness

Research at the intersection of machine learning, programming languages, and software engineering has recently taken important steps in proposing learnable probabilistic models of source code that exploit code’s abundance of patterns. In this article, we survey this work. We contrast programming languages against natural languages and discuss how these similarities and differences drive the design of probabilistic models. We present a taxonomy based on the underlying design principles of each model and use it to navigate the literature. Then, we review how researchers have adapted these models to application areas and discuss cross-cutting and application-specific challenges and opportunities.


Aspect-Based Relational Sentiment Analysis Using a Stacked Neural Network Architecture

Sentiment analysis can be regarded as a relation extraction problem in which the sentiment of some opinion holder towards a certain aspect of a product, theme or event needs to be extracted. We present a novel neural architecture for sentiment analysis as a relation extraction problem that addresses this problem by dividing it into three subtasks: i) identification of aspect and opinion terms, ii) labeling of opinion terms with a sentiment, and iii) extraction of relations between opinion terms and aspect terms. For each subtask, we propose a neural network based component and combine all of them into a complete system for relational sentiment analysis. The component for aspect and opinion term extraction is a hybrid architecture consisting of a recurrent neural network stacked on top of a convolutional neural network. This approach outperforms a standard convolutional deep neural architecture as well as a recurrent network architecture and performs competitively compared to other methods on two datasets of annotated customer reviews. To extract sentiments for individual opinion terms, we propose a recurrent architecture in combination with word distance features and achieve promising results, outperforming a majority baseline by 18% accuracy and providing the first results for the USAGE dataset. Our relation extraction component outperforms the current state-of-the-art in aspect-opinion relation extraction by 15% F-Measure.


Aspect-Based Sentiment Analysis Using a Two-Step Neural Network Architecture

The World Wide Web holds a wealth of information in the form of unstructured texts such as customer reviews for products, events and more. By extracting and analyzing the expressed opinions in customer reviews in a fine-grained way, valuable opportunities and insights for customers and businesses can be gained. We propose a neural network based system to address the task of Aspect-Based Sentiment Analysis to compete in Task 2 of the ESWC-2016 Challenge on Semantic Sentiment Analysis. Our proposed architecture divides the task in two subtasks: aspect term extraction and aspect-specific sentiment extraction. This approach is flexible in that it allows to address each subtask independently. As a first step, a recurrent neural network is used to extract aspects from a text by framing the problem as a sequence labeling task. In a second step, a recurrent network processes each extracted aspect with respect to its context and predicts a sentiment label. The system uses pretrained semantic word embedding features which we experimentally enhance with semantic knowledge extracted from WordNet. Further features extracted from SenticNet prove to be beneficial for the extraction of sentiment labels. As the best performing system in its category, our proposed system proves to be an effective approach for the Aspect-Based Sentiment Analysis.


Nonnegative matrix factorization with side information for time series recovery and prediction

Motivated by the reconstruction and the prediction of electricity consumption, we extend Nonnegative Matrix Factorization~(NMF) to take into account side information (column or row features). We consider general linear measurement settings, and propose a framework which models non-linear relationships between features and the response variables. We extend previous theoretical results to obtain a sufficient condition on the identifiability of the NMF in this setting. Based the classical Hierarchical Alternating Least Squares~(HALS) algorithm, we propose a new algorithm (HALSX, or Hierarchical Alternating Least Squares with eXogeneous variables) which estimates the factorization model. The algorithm is validated on both simulated and real electricity consumption datasets as well as a recommendation dataset, to show its performance in matrix recovery and prediction for new rows and columns.


MetaLDA: a Topic Model that Efficiently Incorporates Meta information

Besides the text content, documents and their associated words usually come with rich sets of meta informa- tion, such as categories of documents and semantic/syntactic features of words, like those encoded in word embeddings. Incorporating such meta information directly into the generative process of topic models can improve modelling accuracy and topic quality, especially in the case where the word-occurrence information in the training data is insufficient. In this paper, we present a topic model, called MetaLDA, which is able to leverage either document or word meta information, or both of them jointly. With two data argumentation techniques, we can derive an efficient Gibbs sampling algorithm, which benefits from the fully local conjugacy of the model. Moreover, the algorithm is favoured by the sparsity of the meta information. Extensive experiments on several real world datasets demonstrate that our model achieves comparable or improved performance in terms of both perplexity and topic quality, particularly in handling sparse texts. In addition, compared with other models using meta information, our model runs significantly faster.


Inference in Graphical Models via Semidefinite Programming Hierarchies

Maximum A posteriori Probability (MAP) inference in graphical models amounts to solving a graph-structured combinatorial optimization problem. Popular inference algorithms such as belief propagation (BP) and generalized belief propagation (GBP) are intimately related to linear programming (LP) relaxation within the Sherali-Adams hierarchy. Despite the popularity of these algorithms, it is well understood that the Sum-of-Squares (SOS) hierarchy based on semidefinite programming (SDP) can provide superior guarantees. Unfortunately, SOS relaxations for a graph with n vertices require solving an SDP with n^{\Theta(d)} variables where d is the degree in the hierarchy. In practice, for d\ge 4, this approach does not scale beyond a few tens of variables. In this paper, we propose binary SDP relaxations for MAP inference using the SOS hierarchy with two innovations focused on computational efficiency. Firstly, in analogy to BP and its variants, we only introduce decision variables corresponding to contiguous regions in the graphical model. Secondly, we solve the resulting SDP using a non-convex Burer-Monteiro style method, and develop a sequential rounding procedure. We demonstrate that the resulting algorithm can solve problems with tens of thousands of variables within minutes, and outperforms BP and GBP on practical problems such as image denoising and Ising spin glasses. Finally, for specific graph types, we establish a sufficient condition for the tightness of the proposed partial SOS relaxation.


BIOS ORAM: Improved Privacy-Preserving Data Access for Parameterized Outsourced Storage

Algorithms for oblivious random access machine (ORAM) simulation allow a client, Alice, to obfuscate a pattern of data accesses with a server, Bob, who is maintaining Alice’s outsourced data while trying to learn information about her data. We present a novel ORAM scheme that improves the asymptotic I/O overhead of previous schemes for a wide range of size parameters for client-side private memory and message blocks, from logarithmic to polynomial. Our method achieves statistical security for hiding Alice’s access pattern and, with high probability, achieves an I/O overhead that ranges from O(1) to O(\log^2 n/(\log\log n)^2), depending on these size parameters, where n is the size of Alice’s outsourced memory. Our scheme, which we call BIOS ORAM, combines multiple uses of B-trees with a reduction of ORAM simulation to isogrammic access sequences.


Triangle Generative Adversarial Networks

A Triangle Generative Adversarial Network (\Delta-GAN) is developed for semi-supervised cross-domain joint distribution matching, where the training data consists of samples from each domain, and supervision of domain correspondence is provided by only a few paired samples. \Delta-GAN consists of four neural networks, two generators and two discriminators. The generators are designed to learn the two-way conditional distributions between the two domains, while the discriminators implicitly define a ternary discriminative function, which is trained to distinguish real data pairs and two kinds of fake data pairs. The generators and discriminators are trained together using adversarial learning. Under mild assumptions, in theory the joint distributions characterized by the two generators concentrate to the data distribution. In experiments, three different kinds of domain pairs are considered, image-label, image-image and image-attribute pairs. Experiments on semi-supervised image classification, image-to-image translation and attribute-based image generation demonstrate the superiority of the proposed approach.


A Note on Tight Lower Bound for MNL-Bandit Assortment Selection Models
Bayesian detection of piecewise linear trends in replicated time-series with application to growth data modelling
Crossing Patterns in Nonplanar Road Networks
Geometric Semantic Genetic Programming Algorithm and Slump Prediction
An approximate fractional Gaussian noise model with ${\mathcal O}(n)$ computational cost
Fiber-Flux Diffusion Density for White Matter Tracts Analysis: Application to Mild Anomalies Localization in Contact Sports Players
A Probabilistic Framework for Nonlinearities in Stochastic Neural Networks
Diluting the Scalability Boundaries: Exploring the Use of Disaggregated Architectures for High-Level Network Data Analysis
When is a Convolutional Filter Easy To Learn?
Gallai-Ramsey numbers of $C_9$ with multiple colors
Reserve Requirements in Ancillary Markets Using Consensus-Based Cooperative Model Considering Renewable Resources
Discrete Dynamic Causal Modeling and Its Relationship with Directed Information
Iterative Policy Learning in End-to-End Trainable Task-Oriented Neural Dialog Models
Geographically Coordinated Frequency Control technical report
Model-Powered Conditional Independence Test
Viscosity Solutions of Stochastic Hamilton-Jacobi-Bellman Equations
White Matter Fiber Segmentation Using Functional Varifolds
Character tables and the problem of existence of finite projective planes
Multi-modal analysis of genetically-related subjects using SIFT descriptors in brain MRI
The bimodal Ising spin glass in dimension two : the anomalous dimension $η$
A dissipativity theorem for p-dominant systems
Distributed Estimation Under Sensor Attacks
Matterport3D: Learning from RGB-D Data in Indoor Environments
On Dynamic Precision Scaling
Paraphrasing verbal metonymy through computational methods
Many Triangles with Few Edges
Improving spliced alignment for identification of ortholog groups and multiple CDS alignment
On the Complexity of Robust Stable Marriage
Blocking Versus Non-Blocking Halo Exchange
Zooming in on NYC taxi data with Portal
A Fast Algorithm Based on a Sylvester-like Equation for LS Regression with GMRF Prior
On the Opportunities and Pitfalls of Nesting Monte Carlo Estimators
Bias Correction with Jackknife, Bootstrap, and Taylor Series
Enumeration on Trees under Relabelings
Compressed Representations of Conjunctive Query Results
On Bergeron’s positivity problem for $q$-binomial coefficients
Connecting Width and Structure in Knowledge Compilation
Synchronization Patterns in Networks of Kuramoto Oscillators: A Geometric Approach for Analysis and Control
POMCPOW: An online algorithm for POMDPs with continuous state, action, and observation spaces
Human Understandable Explanation Extraction for Black-box Classification Models Based on Matrix Factorization
A Comparative Quantitative Analysis of Contemporary Big Data Clustering Algorithms for Market Segmentation in Hospitality Industry
Protest Activity Detection and Perceived Violence Estimation from Social Media Images
Algorithm and Hardware Design of Discrete-Time Spiking Neural Networks Based on Back Propagation with Binary Activations
Chromatic polynomials of random graphs
Bridging observational studies and randomized experiments by embedding the former in the latter
Estimating Mutual Information for Discrete-Continuous Mixtures
Deterministic rendezvous with detection using beeps
A note on quasi-equilibrium problems
Time-Dependent Generalized Nash Equilibrium Problem
Deterministic meeting of sniffing agents in the plane
Fast Discrete Linear Canonical Transform Based on CM-CC-CM Decomposition and FFT
Integrable stochastic dualities and the deformed Knizhnik-Zamolodchikov equation
CISRDCNN: Super-resolution of compressed images using deep convolutional neural networks
Truncated Cramér-von Mises test of normality
Discretized conformal prediction for efficient distribution-free inference
Inter-Operator Base Station Coordination in Spectrum-Shared Millimeter Wave Cellular Networks
Training Better CNNs Requires to Rethink ReLU
Look Wider to Match Image Patches with Convolutional Neural Networks
BeSS: An R Package for Best Subset Selection in Linear, Logistic and CoxPH Models
Finite Sample Guarantees for PCA in Non-Isotropic and Data-Dependent Noise
Uniform Consistency of the Highly Adaptive Lasso Estimator of Infinite Dimensional Parameters
Deep-Learnt Classification of Light Curves
Compressing Low Precision Deep Neural Networks Using Sparsity-Induced Regularization in Ternary Networks
Maximum of an Airy process plus Brownian motion and memory in KPZ growth
Dynamic Oracle for Neural Machine Translation in Decoding Phase
A Novel Quasigroup Substitution Scheme for Chaos Based Image Encryption
Incorrigibility in the CIRL Framework
Random Caching in Backhaul-Limited Multi-Antenna Networks: Analysis and Area Spectrum Efficiency Optimization
A Proof Technique for Skewness of Graphs
Coded Caching in Partially Cooperative D2D Communication Networks
Conjugate generalized linear mixed models for clustered data
The Critical Radius in Sampling-Based Motion Planning
Multifractal characteristics of external anal sphincter based on sEMG signals
Sparse Markov Decision Processes with Causal Sparse Tsallis Entropy Regularization for Reinforcement Learning
Physical Layer Security in Heterogeneous Networks with Jammer Selection and Full-Duplex Users
Steepest descent algorithm on orthogonal Stiefel manifolds
MuseGAN: Symbolic-domain Music Generation and Accompaniment with Multi-track Sequential Generative Adversarial Networks
Tilt Assembly: Algorithms for Micro-Factories That Build Objects with Uniform External Forces
Colour Terms: a Categorisation Model Inspired by Visual Cortex Neurons
F-index of graphs based on new operations related to the join of graphs
Scalable Estimation of Dirichlet Process Mixture Models on Distributed Data
A Fast and Accurate Vietnamese Word Segmenter
Exploring Human-like Attention Supervision in Visual Question Answering
Hybrid, Frame and Event based Visual Inertial Odometry for Robust, Autonomous Navigation of Quadrotors
Asymptotics for relative frequency when population is driven by arbitrary evolution
Analytic model of thermalization: Quantum emulation of classical cellular automata
Predicting Video Saliency with Object-to-Motion CNN and Two-layer Convolutional LSTM
Improving Opinion-Target Extraction with Character-Level Word Embeddings
Fitting Generalized Essential Matrices from Generic 6×6 Matrices and its Applications
An Algebra Associated with a Flag in a Subspace Lattice over a Finite Field and the Quantum Affine Algebra $U_q(\widehat{\mathfrak{sl}}_2)$
On the exit time from open sets of some semi-Markov processes
3D Reconstruction in Canonical Co-ordinate Space from Arbitrarily Oriented 2D Images
The bail-out optimal dividend problem under the absolutely continuous condition
Double-distance frameworks and mixed sparsity graphs
Minimax lower bounds for function estimation on graphs
An Improved Primal-Dual Interior Point Solver for Model Predictive Control
An Adaptive Algorithm for Precise Pupil Boundary Detection using Entropy of Contour Gradients
Selfish Jobs with Favorite Machines: Price of Anarchy vs Strong Price of Anarchy
Central limit theorem associated to Gaussian operators of type B
A General Framework for the Recognition of Online Handwritten Graphics
Analogical-based Bayesian Optimization
Human Action Forecasting by Learning Task Grammars
Equilibration in the Nosé-Hoover isokinetic ensemble: Effect of inter-particle interactions
Interactive Music Generation with Positional Constraints using Anticipation-RNNs
A propos de l’algèbre de Hopf des mots tassés WMat
The Brownian Motion on Aff(R) and Quasi-Local Theorems
Rate of convergence to equilibrium for discrete-time stochastic dynamics with memory
Neural Networks for Text Correction and Completion in Keyboard Decoding
Language Modeling with Highway LSTM
Automatic Leaf Extraction from Outdoor Images
A Recorded Debating Dataset
Scalable Support Vector Clustering Using Budget
Human Activity Recognition Using Robust Adaptive Privileged Probabilistic Learning
3D Reconstruction with Low Resolution, Small Baseline and High Radial Distortion Stereo Images
On Björner and Lovász’s conjecture
Uncoded Placement Optimization for Coded Delivery
Comparison of the Kim-Milman and Brenier maps
Evaluation of the Rate of Convergence in the PIA
On dihedral flows in embedded graphs
Restricted-Boltzmann-Machine Learning for Solving Strongly Correlated Quantum Systems
Image operator learning coupled with CNN classification and its application to staff line removal
A Simple and Efficient Algorithm for Nonlinear Model Predictive Control
Accurate Genomic Prediction Of Human Height
Learning to update Auto-associative Memory in Recurrent Neural Networks for Improving Sequence Memorization
Convolutional Long Short-Term Memory Networks for Recognizing First Person Interactions
SalNet360: Saliency Maps for omni-directional images with CNN
On the monotone and primal-dual active set schemes for $\ell^p$-type problems, $p \in (0,1]$
Geometric inequalities, stability results and Kendall’s problem in spherical space
Photoacoustic Imaging using Combination of Eigenspace-Based Minimum Variance and Delay-Multiply-and-Sum Beamformers: Simulation Study
Learning to Detect Violent Videos using Convolutional Long Short-Term Memory
When 3D-Aided 2D Face Recognition Meets Deep Learning: An extended UR2D for Pose-Invariant Face Recognition
Summable Reparameterizations of Wasserstein Critics in the One-Dimensional Setting
Unimodal Category and the Monotonicity Conjecture