1k citations · 1.7k across the 13 of their papers we have counts for
13 papers
Solving math word problems with process- and outcome-based feedback
Jonathan Uesato, Nate Kushman, Ramana Kumar +6
Recent work has shown that asking language models to generate reasoning steps improves performance on many reasoning tasks. When moving beyond prompting, this raises the question o…
Competition-Level Code Generation with AlphaCode
Yujia Li, David Choi, Junyoung Chung +23
Programming is a powerful and ubiquitous problem-solving tool. Developing systems that can assist programmers or even generate programs independently could make programming more pr…
Interpreting Spatially Infinite Generative Models
Chaochao Lu, Richard E. Turner, Yingzhen Li +1
Traditional deep generative models of images and other spatial modalities can only generate fixed sized outputs. The generated images have exactly the same resolution as the traini…
Inverse Graphics GAN: Learning to Generate 3D Shapes from Unstructured 2D Data
Sebastian Lunz, Yingzhen Li, Andrew Fitzgibbon +1
Recent work has shown the ability to learn generative models for 3D shapes from only unstructured 2D images. However, training such models requires differentiating through the rast…
Learning Robust Representations via Multi-View Information Bottleneck
Marco Federici, Anjan Dutta, Patrick Forré +2
The information bottleneck principle provides an information-theoretic method for representation learning, by training an encoder to retain all information which is relevant for pr…
Inverting Supervised Representations with Autoregressive Neural Density Models
Charlie Nash, Nate Kushman, Christopher K. I. Williams
We present a method for feature interpretation that makes use of recent advances in autoregressive density estimation models to invert model representations. We train generative in…
Constructing Unrestricted Adversarial Examples with Generative Models
Yang Song, Rui Shu, Nate Kushman +1
Adversarial examples are typically constructed by perturbing an existing data point within a small matrix norm, and current defense methods are focused on guarding against this typ…
PixelDefend: Leveraging Generative Models to Understand and Defend against Adversarial Examples
Yang Song, Taesup Kim, Sebastian Nowozin +2
Adversarial perturbations of normal images are usually imperceptible to humans, but they can seriously confuse state-of-the-art machine learning models. What makes them so special…
Summary - TerpreT: A Probabilistic Programming Language for Program Induction
Alexander L. Gaunt, Marc Brockschmidt, Rishabh Singh +4
We study machine learning formulations of inductive program synthesis; that is, given input-output examples, synthesize source code that maps inputs to corresponding outputs. Our k…
A Deep Learning Approach for Joint Video Frame and Reward Prediction in Atari Games
Felix Leibfried, Nate Kushman, Katja Hofmann
Reinforcement learning is concerned with identifying reward-maximizing behaviour policies in environments that are initially unknown. State-of-the-art reinforcement learning approa…
Differentiable Programs with Neural Libraries
Alexander L. Gaunt, Marc Brockschmidt, Nate Kushman +1
We develop a framework for combining differentiable programming languages with neural networks. Using this framework we create end-to-end trainable systems that learn to write inte…
TerpreT: A Probabilistic Programming Language for Program Induction
Alexander L. Gaunt, Marc Brockschmidt, Rishabh Singh +4
We study machine learning formulations of inductive program synthesis; given input-output examples, we try to synthesize source code that maps inputs to corresponding outputs. Our…
Neural Generation of Regular Expressions from Natural Language with Minimal Domain Knowledge
Nicholas Locascio, Karthik Narasimhan, Eduardo DeLeon +2
This paper explores the task of translating natural language queries into regular expressions which embody their meaning. In contrast to prior work, the proposed neural model does…