649 citations
- Carnegie Mellon UniversityUS23 papers
- Stanford UniversityUS20 papers
- Google (United States)US14 papers
- Georgia Institute of TechnologyUS13 papers
- Tel Aviv UniversityIL12 papers
- Cornell UniversityUS11 papers
- University of California, BerkeleyUS11 papers
- University College LondonGB10 papers
- Harvard University PressUS9 papers
- Johns Hopkins UniversityUS9 papers
- Massachusetts Institute of TechnologyUS9 papers
- The University of Texas at AustinUS9 papers
8 papers · 1 filter
Improved Stereo Matching with Constant Highway Networks and Reflective Confidence Learning
Amit Shaked, Lior Wolf
We present an improved three-step pipeline for the stereo matching problem and introduce multiple novelties at each stage. We propose a new highway network architecture for computi…
CLEVR: A Diagnostic Dataset for Compositional Language and Elementary Visual Reasoning
Justin Johnson, Bharath Hariharan, Laurens van der Maaten +3
When building artificial intelligence systems that can reason and answer questions about visual data, we need diagnostic tests to analyze our progress and discover shortcomings. Ex…
Improving Neural Language Models with a Continuous Cache
Edouard Grave, Armand Joulin, Nicolas Usunier
We propose an extension to neural network language models to adapt their prediction to the recent history. Our model is a simplified version of memory augmented networks, which sto…
Semantic Segmentation using Adversarial Networks
Pauline Luc, Camille Couprie, Soumith Chintala +1
Adversarial training has been shown to produce state of the art results for generative image modeling. In this paper we propose an adversarial training approach to train semantic s…
Unsupervised Cross-Domain Image Generation
Yaniv Taigman, Adam Polyak, Lior Wolf
We study the problem of transferring a sample in one domain to an analog sample in another domain. Given two related domains, S and T, we would like to learn a generative function…
Dialogue Learning With Human-In-The-Loop
Jiwei Li, Alexander H. Miller, Sumit Chopra +2
An important aspect of developing conversational agents is to give a bot the ability to improve through communicating with humans and to learn from the mistakes that it makes. Most…