7.4k citations · 22.9k across the 86 of their papers we have counts for
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Predict Responsibly: Improving Fairness and Accuracy by Learning to Defer
David Madras, Toniann Pitassi, Richard Zemel
In many machine learning applications, there are multiple decision-makers involved, both automated and human. The interaction between these agents often goes unaddressed in algorit…
Few-Shot Learning Through an Information Retrieval Lens
Eleni Triantafillou, Richard Zemel, Raquel Urtasun
Few-shot learning refers to understanding new concepts from only a few examples. We propose an information retrieval-inspired approach for this problem that is motivated by the inc…
Dualing GANs
Yujia Li, Alexander Schwing, Kuan-Chieh Wang +1
Generative adversarial nets (GANs) are a promising technique for modeling a distribution from samples. It is however well known that GAN training suffers from instability due to th…
Causal Effect Inference with Deep Latent-Variable Models
Christos Louizos, Uri Shalit, Joris Mooij +3
Learning individual-level causal effects from observational data, such as inferring the most effective medication for a specific patient, is a problem of growing importance for pol…
Prototypical Networks for Few-shot Learning
Jake Snell, Kevin Swersky, Richard S. Zemel
We propose prototypical networks for the problem of few-shot classification, where a classifier must generalize to new classes not seen in the training set, given only a small numb…
Understanding the Effective Receptive Field in Deep Convolutional Neural Networks
Wenjie Luo, Yujia Li, Raquel Urtasun +1
We study characteristics of receptive fields of units in deep convolutional networks. The receptive field size is a crucial issue in many visual tasks, as the output must respond t…