b'Jimmy Ba'

research

∙ 06/01/2023

STEVE-1: A Generative Model for Text-to-Behavior in Minecraft

Constructing AI models that respond to text instructions is challenging,...

0 Shalev Lifshitz, et al. ∙

research

∙ 05/24/2023

Training on Thin Air: Improve Image Classification with Generated Data

Acquiring high-quality data for training discriminative models is a cruc...

0 Yongchao Zhou, et al. ∙

research

∙ 05/19/2023

Clinical Camel: An Open-Source Expert-Level Medical Language Model with Dialogue-Based Knowledge Encoding

Large Language Models (LLMs) present immense potential in the medical fi...

0 Augustin Toma, et al. ∙

research

∙ 05/06/2023

Residual Prompt Tuning: Improving Prompt Tuning with Residual Reparameterization

Prompt tuning is one of the successful approaches for parameter-efficien...

3 Anastasia Razdaibiedina, et al. ∙

research

∙ 04/26/2023

TR0N: Translator Networks for 0-Shot Plug-and-Play Conditional Generation

We propose TR0N, a highly general framework to turn pre-trained uncondit...

0 Zhaoyan Liu, et al. ∙

research

∙ 04/12/2023

Boosted Prompt Ensembles for Large Language Models

Methods such as chain-of-thought prompting and self-consistency have pus...

0 Silviu Pitis, et al. ∙

research

∙ 01/10/2023

Mastering Diverse Domains through World Models

General intelligence requires solving tasks across many domains. Current...

0 Danijar Hafner, et al. ∙

research

∙ 12/07/2022

Multi-Rate VAE: Train Once, Get the Full Rate-Distortion Curve

Variational autoencoders (VAEs) are powerful tools for learning latent r...

19 Juhan Bae, et al. ∙

research

∙ 11/03/2022

Large Language Models Are Human-Level Prompt Engineers

By conditioning on natural language instructions, large language models ...

0 Yongchao Zhou, et al. ∙

research

∙ 09/27/2022

Exploring Low Rank Training of Deep Neural Networks

Training deep neural networks in low rank, i.e. with factorised layers, ...

0 Siddhartha Rao Kamalakara, et al. ∙

research

∙ 06/01/2022

Dataset Distillation using Neural Feature Regression

Dataset distillation aims to learn a small synthetic dataset that preser...

0 Yongchao Zhou, et al. ∙

research

∙ 05/31/2022

You Can't Count on Luck: Why Decision Transformers Fail in Stochastic Environments

Recently, methods such as Decision Transformer that reduce reinforcement...

0 Keiran Paster, et al. ∙

research

∙ 10/27/2021

Learning Domain Invariant Representations in Goal-conditioned Block MDPs

Deep Reinforcement Learning (RL) is successful in solving many complex M...

5 Beining Han, et al. ∙

research

∙ 02/18/2021

Clockwork Variational Autoencoders

Deep learning has enabled algorithms to generate realistic images. Howev...

0 Vaibhav Saxena, et al. ∙

research

∙ 01/15/2021

LIME: Learning Inductive Bias for Primitives of Mathematical Reasoning

While designing inductive bias in neural architectures has been widely s...

58 Yuhuai Wu, et al. ∙

research

∙ 12/23/2020

Noisy Labels Can Induce Good Representations

The current success of deep learning depends on large-scale labeled data...

11 Jingling Li, et al. ∙

research

∙ 12/21/2020

Evaluating Agents without Rewards

Reinforcement learning has enabled agents to solve challenging tasks in ...

17 Brendon Matusch, et al. ∙

research

∙ 12/04/2020

Planning from Pixels using Inverse Dynamics Models

Learning task-agnostic dynamics models in high-dimensional observation s...

0 Keiran Paster, et al. ∙

research

∙ 10/05/2020

Mastering Atari with Discrete World Models

Intelligent agents need to generalize from past experience to achieve go...

17 Danijar Hafner, et al. ∙

research

∙ 09/03/2020

Action and Perception as Divergence Minimization

We introduce a unified objective for action and perception of intelligen...

10 Danijar Hafner, et al. ∙

research

∙ 07/09/2020

A Study of Gradient Variance in Deep Learning

The impact of gradient noise on training deep models is widely acknowled...

20 Fartash Faghri, et al. ∙

research

∙ 07/08/2020

The Scattering Compositional Learner: Discovering Objects, Attributes, Relationships in Analogical Reasoning

In this work, we focus on an analogical reasoning task that contains ric...

1 Yuhuai Wu, et al. ∙

research

∙ 07/06/2020

INT: An Inequality Benchmark for Evaluating Generalization in Theorem Proving

In learning-assisted theorem proving, one of the most critical challenge...

0 Yuhuai Wu, et al. ∙

research

∙ 07/06/2020

Maximum Entropy Gain Exploration for Long Horizon Multi-goal Reinforcement Learning

What goals should a multi-goal reinforcement learning agent pursue durin...

0 Silviu Pitis, et al. ∙

research

∙ 06/18/2020

When Does Preconditioning Help or Hurt Generalization?

While second order optimizers such as natural gradient descent (NGD) oft...

0 Shun-ichi Amari, et al. ∙

research

∙ 02/17/2020

BatchEnsemble: an Alternative Approach to Efficient Ensemble and Lifelong Learning

Ensembles, where multiple neural networks are trained individually and t...

13 Yeming Wen, et al. ∙

research

∙ 02/14/2020

An Inductive Bias for Distances: Neural Nets that Respect the Triangle Inequality

Distances are pervasive in machine learning. They serve as similarity me...

9 Silviu Pitis, et al. ∙

research

∙ 12/03/2019

Dream to Control: Learning Behaviors by Latent Imagination

Learned world models summarize an agent's experience to facilitate learn...

0 Danijar Hafner, et al. ∙

research

∙ 10/16/2019

On Solving Minimax Optimization Locally: A Follow-the-Ridge Approach

Many tasks in modern machine learning can be formulated as finding equil...

16 Yuanhao Wang, et al. ∙

research

∙ 07/19/2019

Lookahead Optimizer: k steps forward, 1 step back

The vast majority of successful deep neural networks are trained using v...

5 Michael R. Zhang, et al. ∙

research

∙ 07/03/2019

Benchmarking Model-Based Reinforcement Learning

Model-based reinforcement learning (MBRL) is widely seen as having the p...

28 Tingwu Wang, et al. ∙

research

∙ 06/20/2019

Exploring Model-based Planning with Policy Networks

Model-based reinforcement learning (MBRL) with model-predictive control ...

2 Tingwu Wang, et al. ∙

research

∙ 06/12/2019

Neural Graph Evolution: Towards Efficient Automatic Robot Design

Despite the recent successes in robotic locomotion control, the design o...

0 Tingwu Wang, et al. ∙

research

∙ 05/30/2019

Graph Normalizing Flows

We introduce graph normalizing flows: a new, reversible graph neural net...

9 Jenny Liu, et al. ∙

research

∙ 02/21/2019

Interplay Between Optimization and Generalization of Stochastic Gradient Descent with Covariance Noise

The choice of batch-size in a stochastic optimization algorithm plays a ...

0 Yeming Wen, et al. ∙

research

∙ 02/19/2019

DOM-Q-NET: Grounded RL on Structured Language

Building agents to interact with the web would allow for significant imp...

0 Sheng Jia, et al. ∙

research

∙ 02/12/2019

ACTRCE: Augmenting Experience via Teacher's Advice For Multi-Goal Reinforcement Learning

Sparse reward is one of the most challenging problems in reinforcement l...

6 Harris Chan, et al. ∙

research

∙ 10/25/2018

Reversible Recurrent Neural Networks

Recurrent neural networks (RNNs) provide state-of-the-art performance in...

6 Matthew MacKay, et al. ∙

research

∙ 03/12/2018

Flipout: Efficient Pseudo-Independent Weight Perturbations on Mini-Batches

Stochastic neural net weights are used in a variety of contexts, includi...

0 Yeming Wen, et al. ∙

research

∙ 02/22/2018

Solving Approximate Wasserstein GANs to Stationarity

Generative Adversarial Networks (GANs) are one of the most practical str...

0 Maziar Sanjabi, et al. ∙

research

∙ 08/17/2017

Scalable trust-region method for deep reinforcement learning using Kronecker-factored approximation

In this work, we propose to apply trust region optimization to deep rein...

0 Yuhuai Wu, et al. ∙

research

∙ 10/20/2016

Using Fast Weights to Attend to the Recent Past

Until recently, research on artificial neural networks was largely restr...

0 Jimmy Ba, et al. ∙

research

∙ 09/22/2015

Learning Wake-Sleep Recurrent Attention Models

Despite their success, convolutional neural networks are computationally...

0 Jimmy Ba, et al. ∙

research

∙ 06/01/2015

Predicting Deep Zero-Shot Convolutional Neural Networks using Textual Descriptions

One of the main challenges in Zero-Shot Learning of visual categories is...

0 Jimmy Ba, et al. ∙

research

∙ 02/10/2015

Show, Attend and Tell: Neural Image Caption Generation with Visual Attention

Inspired by recent work in machine translation and object detection, we ...

0 Kelvin Xu, et al. ∙

research

∙ 12/24/2014

Multiple Object Recognition with Visual Attention

We present an attention-based model for recognizing multiple objects in ...

0 Jimmy Ba, et al. ∙

Jimmy Ba

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