Meta-Unsupervised-Learning google
We introduce a new paradigm to investigate unsupervised learning, reducing unsupervised learning to supervised learning. Specifically, we mitigate the subjectivity in unsupervised decision-making by leveraging knowledge acquired from prior, possibly heterogeneous, supervised learning tasks. We demonstrate the versatility of our framework via comprehensive expositions and detailed experiments on several unsupervised problems such as (a) clustering, (b) outlier detection, and (c) similarity prediction under a common umbrella of meta-unsupervised-learning. We also provide rigorous PAC-agnostic bounds to establish the theoretical foundations of our framework, and show that our framing of meta-clustering circumvents Kleinberg’s impossibility theorem for clustering. …

Kitematic google
Kitematic is an open source project built to simplify and streamline using Docker on a Mac or Windows (coming soon) PC. Kitematic automates the Docker installation and setup process and provides an intuitive graphical user interface (GUI) for running Docker containers. Kitematic integrates with Docker Machine to provision a VirtualBox VM and install the Docker Engine locally on your machine. Once installed, the Kitematic GUI launches and from the home screen you will be presented with curated images that you can run instantly. You can search for any public images on Docker Hub from Kitematic just by typing in the search bar. You can use the GUI to create, run and manage your containers just by clicking on buttons. Kitematic allows you to switch back and forth between the Docker CLI and the GUI. Kitematic also automates advanced features such as managing ports and configuring volumes. You can use Kitematic to change environment variables, stream logs, and single click terminal into your Docker container all from the GUI. …

Adaptive Thouless-Anderson-Palmer Mean Field Approach (ADATAP) google
We develop an advanced mean field method for approximating averages in probabilistic data models that is based on the TAP approach of disorder physics. In contrast to conventional TAP, where the knowledge of the distribution of couplings between the random variables is required, our method adapts to the concrete couplings. We demonstrate the validity of our approach, which is sofar restricted to models with non-glassy behaviour, by replica calculations for a wide class of models as well as by simulations for a real data set.