JSON-stat google
JSON-stat is a simple lightweight JSON dissemination format best suited for data visualization, mobile apps or open data initiatives, that has been designed for all kinds of disseminators. JSON-stat also proposes an HTML microdata schema to enrich HTML tables and put the JSON-stat vocabulary in the browser. Fortunately, there are already tools that ease the use of JSON-stat, like the JSON-stat Javascript Toolkit, a library to process JSON-stat responses. …

Decision Tree Learning / Classification and Regression Trees (CART) google
Decision tree learning uses a decision tree as a predictive model which maps observations about an item to conclusions about the item’s target value. It is one of the predictive modelling approaches used in statistics, data mining and machine learning. More descriptive names for such tree models are classification trees or regression trees. In these tree structures, leaves represent class labels and branches represent conjunctions of features that lead to those class labels. …

FusedGAN google
We present FusedGAN, a deep network for conditional image synthesis with controllable sampling of diverse images. Fidelity, diversity and controllable sampling are the main quality measures of a good image generation model. Most existing models are insufficient in all three aspects. The FusedGAN can perform controllable sampling of diverse images with very high fidelity. We argue that controllability can be achieved by disentangling the generation process into various stages. In contrast to stacked GANs, where multiple stages of GANs are trained separately with full supervision of labeled intermediate images, the FusedGAN has a single stage pipeline with a built-in stacking of GANs. Unlike existing methods, which requires full supervision with paired conditions and images, the FusedGAN can effectively leverage more abundant images without corresponding conditions in training, to produce more diverse samples with high fidelity. We achieve this by fusing two generators: one for unconditional image generation, and the other for conditional image generation, where the two partly share a common latent space thereby disentangling the generation. We demonstrate the efficacy of the FusedGAN in fine grained image generation tasks such as text-to-image, and attribute-to-face generation. …

Advertisements