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11/30/2021
Show Your Work: Scratchpads for Intermediate Computation with Language Models
Large pre-trained language models perform remarkably well on tasks that ...
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08/16/2021
Program Synthesis with Large Language Models
This paper explores the limits of the current generation of large langua...
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10/22/2020
Learnability and Complexity of Quantum Samples
Given a quantum circuit, a quantum computer can sample the output distri...
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10/11/2020
SMYRF: Efficient Attention using Asymmetric Clustering
We propose a novel type of balanced clustering algorithm to approximate ...
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07/28/2020
BUSTLE: Bottom-up program-Synthesis Through Learning-guided Exploration
Program synthesis is challenging largely because of the difficulty of se...
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02/14/2020
Top-K Training of GANs: Improving Generators by Making Critics Less Critical
We introduce a simple (one line of code) modification to the Generative ...
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02/13/2020
Learning to Represent Programs with Property Signatures
We introduce the notion of property signatures, a representation for pro...
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02/11/2020
Improved Consistency Regularization for GANs
Recent work has increased the performance of Generative Adversarial Netw...
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11/27/2019
Your Local GAN: Designing Two Dimensional Local Attention Mechanisms for Generative Models
We introduce a new local sparse attention layer that preserves two-dimen...
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10/29/2019
Small-GAN: Speeding Up GAN Training Using Core-sets
Recent work by Brock et al. (2018) suggests that Generative Adversarial ...
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10/26/2019
Consistency Regularization for Generative Adversarial Networks
Generative Adversarial Networks (GANs) are known to be difficult to trai...
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10/02/2019
Improving Differentially Private Models with Active Learning
Broad adoption of machine learning techniques has increased privacy conc...
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10/16/2018
Discriminator Rejection Sampling
We propose a rejection sampling scheme using the discriminator of a GAN ...
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08/14/2018
Skill Rating for Generative Models
We explore a new way to evaluate generative models using insights from e...
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07/28/2018
TensorFuzz: Debugging Neural Networks with Coverage-Guided Fuzzing
Machine learning models are notoriously difficult to interpret and debug...
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05/21/2018
Self-Attention Generative Adversarial Networks
In this paper, we propose the Self-Attention Generative Adversarial Netw...
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04/24/2018
Realistic Evaluation of Deep Semi-Supervised Learning Algorithms
Semi-supervised learning (SSL) provides a powerful framework for leverag...
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02/23/2018
Is Generator Conditioning Causally Related to GAN Performance?
Recent work (Pennington et al, 2017) suggests that controlling the entir...
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02/24/2017
Changing Model Behavior at Test-Time Using Reinforcement Learning
Machine learning models are often used at test-time subject to constrain...
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10/30/2016
Conditional Image Synthesis With Auxiliary Classifier GANs
Synthesizing high resolution photorealistic images has been a long-stand...
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06/05/2016
Semi-Supervised Learning with Generative Adversarial Networks
We extend Generative Adversarial Networks (GANs) to the semi-supervised ...
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01/15/2016
