The increase of the amount of misinformation spread every day online is a huge threat to the
society. Organizations and researchers are working to contrast this misinformation plague. In …

Hierarchical feature extractors such as Convolutional Networks (ConvNets) have achieved
impressive performance on a variety of classification tasks using purely feedforward …

The rise of online misinformation is posing a threat to the functioning of democratic
processes. The ability to algorithmically spread false information through online social …

We propose a multimodal, decomposable model for articulated human pose estimation in
monocular images. A typical approach to this problem is to use a linear structured model …

Abstract urrently, the most successful learning models in computer vision are based on
learning successive representations followed by a decision layer. This is usually actualized …

Training higher-order conditional random fields is prohibitive for huge tag sets. We present
an approximated conditional random field using coarse-to-fine decoding and early updating …

There is a fundamental tradeoff between effectiveness and efficiency when designing
retrieval models for large-scale document collections. Effectiveness tends to derive from …

We address the problem of articulated human pose estimation by learning a coarse-to-fine
cascade of pictorial structure models. While the fine-level state-space of poses of individual …

We introduce a method for constructing skills capable of solving tasks drawn from a
distribution of parameterized reinforcement learning problems. The method draws example …

A major part of natural language processing now depends on the use of text data to build
linguistic analyzers. We consider statistical, computational approaches to modeling linguistic …