3.2k citations · 8.8k across the 71 of their papers we have counts for
8 papers · 1 filter
Neural Networks with Cheap Differential Operators
Ricky T. Q. Chen, David Duvenaud
Gradients of neural networks can be computed efficiently for any architecture, but some applications require differential operators with higher time complexity. We describe a famil…
Your Classifier is Secretly an Energy Based Model and You Should Treat it Like One
Will Grathwohl, Kuan-Chieh Wang, Jörn-Henrik Jacobsen +3
We propose to reinterpret a standard discriminative classifier of p(y|x) as an energy based model for the joint distribution p(x,y). In this setting, the standard class probabiliti…
Optimizing Millions of Hyperparameters by Implicit Differentiation
Jonathan Lorraine, Paul Vicol, David Duvenaud
We propose an algorithm for inexpensive gradient-based hyperparameter optimization that combines the implicit function theorem (IFT) with efficient inverse Hessian approximations.…
Efficient Graph Generation with Graph Recurrent Attention Networks
Renjie Liao, Yujia Li, Yang Song +6
We propose a new family of efficient and expressive deep generative models of graphs, called Graph Recurrent Attention Networks (GRANs). Our model generates graphs one block of nod…
Understanding Undesirable Word Embedding Associations
Kawin Ethayarajh, David Duvenaud, Graeme Hirst
Word embeddings are often criticized for capturing undesirable word associations such as gender stereotypes. However, methods for measuring and removing such biases remain poorly u…
Latent ODEs for Irregularly-Sampled Time Series
Yulia Rubanova, Ricky T. Q. Chen, David Duvenaud
Time series with non-uniform intervals occur in many applications, and are difficult to model using standard recurrent neural networks (RNNs). We generalize RNNs to have continuous…