7.4k citations · 22.9k across the 86 of their papers we have counts for
9 papers · 1 filter
Training Deep Neural Networks via Direct Loss Minimization
Yang Song, Alexander G. Schwing, Richard S. Zemel +1
Supervised training of deep neural nets typically relies on minimizing cross-entropy. However, in many domains, we are interested in performing well on metrics specific to the appl…
Learning to Generate Images with Perceptual Similarity Metrics
Jake Snell, Karl Ridgeway, Renjie Liao +3
Deep networks are increasingly being applied to problems involving image synthesis, e.g., generating images from textual descriptions and reconstructing an input image from a compa…
Gated Graph Sequence Neural Networks
Yujia Li, Daniel Tarlow, Marc Brockschmidt +1
Graph-structured data appears frequently in domains including chemistry, natural language semantics, social networks, and knowledge bases. In this work, we study feature learning t…
The Variational Fair Autoencoder
Christos Louizos, Kevin Swersky, Yujia Li +2
We investigate the problem of learning representations that are invariant to certain nuisance or sensitive factors of variation in the data while retaining as much of the remaining…
Skip-Thought Vectors
Ryan Kiros, Yukun Zhu, Ruslan Salakhutdinov +4
We describe an approach for unsupervised learning of a generic, distributed sentence encoder. Using the continuity of text from books, we train an encoder-decoder model that tries…
Aligning Books and Movies: Towards Story-like Visual Explanations by Watching Movies and Reading Books
Yukun Zhu, Ryan Kiros, Richard Zemel +4
Books are a rich source of both fine-grained information, how a character, an object or a scene looks like, as well as high-level semantics, what someone is thinking, feeling and h…