Augustus Odena | DeepAI

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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 ...

4 Maxwell Nye, et al. ∙

∙ 08/16/2021

Program Synthesis with Large Language Models

This paper explores the limits of the current generation of large langua...

11 Jacob Austin, et al. ∙

∙ 10/22/2020

Learnability and Complexity of Quantum Samples

Given a quantum circuit, a quantum computer can sample the output distri...

3 Murphy Yuezhen Niu, et al. ∙

∙ 10/11/2020

SMYRF: Efficient Attention using Asymmetric Clustering

We propose a novel type of balanced clustering algorithm to approximate ...

34 Giannis Daras, et al. ∙

∙ 07/28/2020

BUSTLE: Bottom-up program-Synthesis Through Learning-guided Exploration

Program synthesis is challenging largely because of the difficulty of se...

4 Augustus Odena, et al. ∙

∙ 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 ...

16 Samarth Sinha, et al. ∙

∙ 02/13/2020

Learning to Represent Programs with Property Signatures

We introduce the notion of property signatures, a representation for pro...

0 Augustus Odena, et al. ∙

∙ 02/11/2020

Improved Consistency Regularization for GANs

Recent work has increased the performance of Generative Adversarial Netw...

38 Zhengli Zhao, et al. ∙

∙ 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...

41 Giannis Daras, et al. ∙

∙ 10/29/2019

Small-GAN: Speeding Up GAN Training Using Core-sets

Recent work by Brock et al. (2018) suggests that Generative Adversarial ...

22 Samarth Sinha, et al. ∙

∙ 10/26/2019

Consistency Regularization for Generative Adversarial Networks

Generative Adversarial Networks (GANs) are known to be difficult to trai...

28 Han Zhang, et al. ∙

∙ 10/02/2019

Improving Differentially Private Models with Active Learning

Broad adoption of machine learning techniques has increased privacy conc...

26 Zhengli Zhao, et al. ∙

∙ 10/16/2018

Discriminator Rejection Sampling

We propose a rejection sampling scheme using the discriminator of a GAN ...

0 Samaneh Azadi, et al. ∙

∙ 08/14/2018

Skill Rating for Generative Models

We explore a new way to evaluate generative models using insights from e...

0 Catherine Olsson, et al. ∙

∙ 07/28/2018

TensorFuzz: Debugging Neural Networks with Coverage-Guided Fuzzing

Machine learning models are notoriously difficult to interpret and debug...

4 Augustus Odena, et al. ∙

∙ 05/21/2018

Self-Attention Generative Adversarial Networks

In this paper, we propose the Self-Attention Generative Adversarial Netw...

0 Han Zhang, et al. ∙

∙ 04/24/2018

Realistic Evaluation of Deep Semi-Supervised Learning Algorithms

Semi-supervised learning (SSL) provides a powerful framework for leverag...

2 Avital Oliver, et al. ∙

∙ 02/23/2018

Is Generator Conditioning Causally Related to GAN Performance?

Recent work (Pennington et al, 2017) suggests that controlling the entir...

0 Augustus Odena, et al. ∙

∙ 02/24/2017

Changing Model Behavior at Test-Time Using Reinforcement Learning

Machine learning models are often used at test-time subject to constrain...

0 Augustus Odena, et al. ∙

∙ 10/30/2016

Conditional Image Synthesis With Auxiliary Classifier GANs

Synthesizing high resolution photorealistic images has been a long-stand...

0 Augustus Odena, et al. ∙

∙ 06/05/2016

Semi-Supervised Learning with Generative Adversarial Networks

We extend Generative Adversarial Networks (GANs) to the semi-supervised ...

0 Augustus Odena, et al. ∙

∙ 01/15/2016

Faster Asynchronous SGD

Asynchronous distributed stochastic gradient descent methods have troubl...

0 Augustus Odena, et al. ∙

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