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06/01/2023
STEVE-1: A Generative Model for Text-to-Behavior in Minecraft
Constructing AI models that respond to text instructions is challenging,...
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05/24/2023
Training on Thin Air: Improve Image Classification with Generated Data
Acquiring high-quality data for training discriminative models is a cruc...
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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...
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05/06/2023
Residual Prompt Tuning: Improving Prompt Tuning with Residual Reparameterization
Prompt tuning is one of the successful approaches for parameter-efficien...
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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...
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04/12/2023
Boosted Prompt Ensembles for Large Language Models
Methods such as chain-of-thought prompting and self-consistency have pus...
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01/10/2023
Mastering Diverse Domains through World Models
General intelligence requires solving tasks across many domains. Current...
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12/07/2022
Multi-Rate VAE: Train Once, Get the Full Rate-Distortion Curve
Variational autoencoders (VAEs) are powerful tools for learning latent r...
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11/03/2022
Large Language Models Are Human-Level Prompt Engineers
By conditioning on natural language instructions, large language models ...
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09/27/2022
Exploring Low Rank Training of Deep Neural Networks
Training deep neural networks in low rank, i.e. with factorised layers, ...
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06/01/2022
Dataset Distillation using Neural Feature Regression
Dataset distillation aims to learn a small synthetic dataset that preser...
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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...
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10/27/2021
Learning Domain Invariant Representations in Goal-conditioned Block MDPs
Deep Reinforcement Learning (RL) is successful in solving many complex M...
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02/18/2021
Clockwork Variational Autoencoders
Deep learning has enabled algorithms to generate realistic images. Howev...
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01/15/2021
LIME: Learning Inductive Bias for Primitives of Mathematical Reasoning
While designing inductive bias in neural architectures has been widely s...
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12/23/2020
Noisy Labels Can Induce Good Representations
The current success of deep learning depends on large-scale labeled data...
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12/21/2020
Evaluating Agents without Rewards
Reinforcement learning has enabled agents to solve challenging tasks in ...
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12/04/2020
Planning from Pixels using Inverse Dynamics Models
Learning task-agnostic dynamics models in high-dimensional observation s...
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10/05/2020
Mastering Atari with Discrete World Models
Intelligent agents need to generalize from past experience to achieve go...
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09/03/2020
Action and Perception as Divergence Minimization
We introduce a unified objective for action and perception of intelligen...
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07/09/2020
A Study of Gradient Variance in Deep Learning
The impact of gradient noise on training deep models is widely acknowled...
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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...
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07/06/2020
INT: An Inequality Benchmark for Evaluating Generalization in Theorem Proving
In learning-assisted theorem proving, one of the most critical challenge...
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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...
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06/18/2020
When Does Preconditioning Help or Hurt Generalization?
While second order optimizers such as natural gradient descent (NGD) oft...
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02/17/2020
BatchEnsemble: an Alternative Approach to Efficient Ensemble and Lifelong Learning
Ensembles, where multiple neural networks are trained individually and t...
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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...
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12/03/2019
Dream to Control: Learning Behaviors by Latent Imagination
Learned world models summarize an agent's experience to facilitate learn...
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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...
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07/19/2019
Lookahead Optimizer: k steps forward, 1 step back
The vast majority of successful deep neural networks are trained using v...
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07/03/2019
Benchmarking Model-Based Reinforcement Learning
Model-based reinforcement learning (MBRL) is widely seen as having the p...
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06/20/2019
Exploring Model-based Planning with Policy Networks
Model-based reinforcement learning (MBRL) with model-predictive control ...
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06/12/2019
Neural Graph Evolution: Towards Efficient Automatic Robot Design
Despite the recent successes in robotic locomotion control, the design o...
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05/30/2019
Graph Normalizing Flows
We introduce graph normalizing flows: a new, reversible graph neural net...
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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 ...
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02/19/2019
DOM-Q-NET: Grounded RL on Structured Language
Building agents to interact with the web would allow for significant imp...
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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...
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10/25/2018
Reversible Recurrent Neural Networks
Recurrent neural networks (RNNs) provide state-of-the-art performance in...
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03/12/2018
Flipout: Efficient Pseudo-Independent Weight Perturbations on Mini-Batches
Stochastic neural net weights are used in a variety of contexts, includi...
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02/22/2018
Solving Approximate Wasserstein GANs to Stationarity
Generative Adversarial Networks (GANs) are one of the most practical str...
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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...
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10/20/2016
Using Fast Weights to Attend to the Recent Past
Until recently, research on artificial neural networks was largely restr...
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09/22/2015
Learning Wake-Sleep Recurrent Attention Models
Despite their success, convolutional neural networks are computationally...
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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...
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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 ...
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12/24/2014