7.4k citations · 22.9k across the 86 of their papers we have counts for
14 papers · 1 filter
Learning Latent Subspaces in Variational Autoencoders
Jack Klys, Jake Snell, Richard Zemel
Variational autoencoders (VAEs) are widely used deep generative models capable of learning unsupervised latent representations of data. Such representations are often difficult to…
Excessive Invariance Causes Adversarial Vulnerability
Jörn-Henrik Jacobsen, Jens Behrmann, Richard Zemel +1
Despite their impressive performance, deep neural networks exhibit striking failures on out-of-distribution inputs. One core idea of adversarial example research is to reveal neura…
Neural Guided Constraint Logic Programming for Program Synthesis
Lisa Zhang, Gregory Rosenblatt, Ethan Fetaya +5
Synthesizing programs using example input/outputs is a classic problem in artificial intelligence. We present a method for solving Programming By Example (PBE) problems by using a…
Incremental Few-Shot Learning with Attention Attractor Networks
Mengye Ren, Renjie Liao, Ethan Fetaya +1
Machine learning classifiers are often trained to recognize a set of pre-defined classes. However, in many applications, it is often desirable to have the flexibility of learning a…
Understanding the Origins of Bias in Word Embeddings
Marc-Etienne Brunet, Colleen Alkalay-Houlihan, Ashton Anderson +1
The power of machine learning systems not only promises great technical progress, but risks societal harm. As a recent example, researchers have shown that popular word embedding a…
Fairness Through Causal Awareness: Learning Latent-Variable Models for Biased Data
David Madras, Elliot Creager, Toniann Pitassi +1
How do we learn from biased data? Historical datasets often reflect historical prejudices; sensitive or protected attributes may affect the observed treatments and outcomes. Classi…