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09/20/2023
Parallel-mentoring for Offline Model-based Optimization
We study offline model-based optimization to maximize a black-box object...
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06/15/2023
Block-State Transformer
State space models (SSMs) have shown impressive results on tasks that re...
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06/07/2023
Goal-conditioned GFlowNets for Controllable Multi-Objective Molecular Design
In recent years, in-silico molecular design has received much attention ...
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06/02/2023
Improving Generalization in Task-oriented Dialogues with Workflows and Action Plans
Task-oriented dialogue is difficult in part because it involves understa...
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05/25/2023
Are Diffusion Models Vision-And-Language Reasoners?
Text-conditioned image generation models have recently shown immense qua...
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04/07/2023
Conservative objective models are a special kind of contrastive divergence-based energy model
In this work we theoretically show that conservative objective models (C...
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02/14/2023
Score-based Diffusion Models in Function Space
Diffusion models have recently emerged as a powerful framework for gener...
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02/10/2023
Long-Context Language Decision Transformers and Exponential Tilt for Interactive Text Environments
Text-based game environments are challenging because agents must deal wi...
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11/26/2022
Receptive Field Refinement for Convolutional Neural Networks Reliably Improves Predictive Performance
Minimal changes to neural architectures (e.g. changing a single hyperpar...
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11/19/2022
Towards good validation metrics for generative models in offline model-based optimisation
In this work we propose a principled evaluation framework for model-base...
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11/02/2022
Attention-based Neural Cellular Automata
Recent extensions of Cellular Automata (CA) have incorporated key ideas ...
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10/21/2022
Bridging the Gap Between Target Networks and Functional Regularization
Bootstrapping is behind much of the successes of Deep Reinforcement Lear...
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10/21/2022
Score-based Denoising Diffusion with Non-Isotropic Gaussian Noise Models
Generative models based on denoising diffusion techniques have led to an...
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10/14/2022
Using Graph Algorithms to Pretrain Graph Completion Transformers
Recent work on Graph Neural Networks has demonstrated that self-supervis...
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08/16/2022
SMPL-IK: Learned Morphology-Aware Inverse Kinematics for AI Driven Artistic Workflows
Inverse Kinematics (IK) systems are often rigid with respect to their in...
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08/03/2022
Improving Meta-Learning Generalization with Activation-Based Early-Stopping
Meta-Learning algorithms for few-shot learning aim to train neural netwo...
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05/19/2022
Masked Conditional Video Diffusion for Prediction, Generation, and Interpolation
Video prediction is a challenging task. The quality of video frames from...
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03/30/2022
Challenges in leveraging GANs for few-shot data augmentation
In this paper, we explore the use of GAN-based few-shot data augmentatio...
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01/05/2022
Does entity abstraction help generative Transformers reason?
Pre-trained language models (LMs) often struggle to reason logically or ...
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12/22/2021
Direct Behavior Specification via Constrained Reinforcement Learning
The standard formulation of Reinforcement Learning lacks a practical way...
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12/14/2021
Learning to Guide and to Be Guided in the Architect-Builder Problem
We are interested in interactive agents that learn to coordinate, namely...
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10/28/2021
From Machine Learning to Robotics: Challenges and Opportunities for Embodied Intelligence
Machine learning has long since become a keystone technology, accelerati...
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09/07/2021
Simple Video Generation using Neural ODEs
Despite having been studied to a great extent, the task of conditional g...
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07/02/2021
Systematic Evaluation of Causal Discovery in Visual Model Based Reinforcement Learning
Inducing causal relationships from observations is a classic problem in ...
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06/15/2021
Multi-Resolution Continuous Normalizing Flows
Recent work has shown that Neural Ordinary Differential Equations (ODEs)...
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06/04/2021
Beyond Target Networks: Improving Deep Q-learning with Functional Regularization
Target networks are at the core of recent success in Reinforcement Learn...
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02/19/2021
Latent Variable Nested Set Transformers AutoBots
Humans have the innate ability to attend to the most relevant actors in ...
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02/09/2021
Robust Motion In-betweening
In this work we present a novel, robust transition generation technique ...
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10/21/2020
Learning to Summarize Long Texts with Memory Compression and Transfer
We introduce Mem2Mem, a memory-to-memory mechanism for hierarchical recu...
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10/06/2020
Reinforcement Learning with Random Delays
Action and observation delays commonly occur in many Reinforcement Learn...
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09/30/2020
Measuring Systematic Generalization in Neural Proof Generation with Transformers
We are interested in understanding how well Transformer language models ...
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09/19/2020
Conditionally Adaptive Multi-Task Learning: Improving Transfer Learning in NLP Using Fewer Parameters Less Data
Multi-Task Learning (MTL) has emerged as a promising approach for transf...
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08/21/2020
Action-Based Representation Learning for Autonomous Driving
Human drivers produce a vast amount of data which could, in principle, b...
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06/23/2020
Adversarial Soft Advantage Fitting: Imitation Learning without Policy Optimization
Adversarial imitation learning alternates between learning a discriminat...
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06/09/2020
AR-DAE: Towards Unbiased Neural Entropy Gradient Estimation
Entropy is ubiquitous in machine learning, but it is in general intracta...
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02/21/2020
On the impressive performance of randomly weighted encoders in summarization tasks
In this work, we investigate the performance of untrained randomly initi...
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11/11/2019
Real-Time Reinforcement Learning
Markov Decision Processes (MDPs), the mathematical framework underlying ...
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10/06/2019
Neural Multisensory Scene Inference
For embodied agents to infer representations of the underlying 3D physic...
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09/07/2019
On Extractive and Abstractive Neural Document Summarization with Transformer Language Models
We present a method to produce abstractive summaries of long documents t...
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08/28/2019
Interactive Language Learning by Question Answering
Humans observe and interact with the world to acquire knowledge. However...
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08/27/2019
Interactive Machine Comprehension with Information Seeking Agents
Existing machine reading comprehension (MRC) models do not scale effecti...
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08/06/2019
Promoting Coordination through Policy Regularization in Multi-Agent Reinforcement Learning
A central challenge in multi-agent reinforcement learning is the inducti...
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07/16/2019
Towards Understanding Generalization in Gradient-Based Meta-Learning
In this work we study generalization of neural networks in gradient-base...
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06/27/2019
Supervise Thyself: Examining Self-Supervised Representations in Interactive Environments
Self-supervised methods, wherein an agent learns representations solely ...
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06/04/2019
Do Neural Dialog Systems Use the Conversation History Effectively? An Empirical Study
Neural generative models have been become increasingly popular when buil...
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03/07/2019
Adversarial Mixup Resynthesizers
In this paper, we explore new approaches to combining information encode...
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01/30/2019
A Meta-Transfer Objective for Learning to Disentangle Causal Mechanisms
We propose to meta-learn causal structures based on how fast a learner a...
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01/04/2019
Dataflow-based Joint Quantization of Weights and Activations for Deep Neural Networks
This paper addresses a challenging problem - how to reduce energy consum...
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10/04/2018
Recurrent Transition Networks for Character Locomotion
Manually authoring transition animations for a complete locomotion syste...
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03/25/2018
