3.2k citations · 8.8k across the 71 of their papers we have counts for
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Invertible Residual Networks
Jens Behrmann, Will Grathwohl, Ricky T. Q. Chen +2
We show that standard ResNet architectures can be made invertible, allowing the same model to be used for classification, density estimation, and generation. Typically, enforcing i…
FFJORD: Free-form Continuous Dynamics for Scalable Reversible Generative Models
Will Grathwohl, Ricky T. Q. Chen, Jesse Bettencourt +2
A promising class of generative models maps points from a simple distribution to a complex distribution through an invertible neural network. Likelihood-based training of these mod…
Towards Understanding Linear Word Analogies
Kawin Ethayarajh, David Duvenaud, Graeme Hirst
A surprising property of word vectors is that word analogies can often be solved with vector arithmetic. However, it is unclear why arithmetic operators correspond to non-linear em…
Stochastic Combinatorial Ensembles for Defending Against Adversarial Examples
George A. Adam, Petr Smirnov, David Duvenaud +2
Many deep learning algorithms can be easily fooled with simple adversarial examples. To address the limitations of existing defenses, we devised a probabilistic framework that can…
Scalable Recommender Systems through Recursive Evidence Chains
Elias Tragas, Calvin Luo, Maxime Gazeau +2
Recommender systems can be formulated as a matrix completion problem, predicting ratings from user and item parameter vectors. Optimizing these parameters by subsampling data becom…
Explaining Image Classifiers by Counterfactual Generation
Chun-Hao Chang, Elliot Creager, Anna Goldenberg +1
When an image classifier makes a prediction, which parts of the image are relevant and why? We can rephrase this question to ask: which parts of the image, if they were not seen by…