Chris Cundy | DeepAI

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∙ 06/08/2023

SequenceMatch: Imitation Learning for Autoregressive Sequence Modelling with Backtracking

In many domains, autoregressive models can achieve low log-likelihood on...

0 Chris Cundy, et al. ∙

∙ 04/13/2023

On the Opportunities and Challenges of Foundation Models for Geospatial Artificial Intelligence

Large pre-trained models, also known as foundation models (FMs), are tra...

5 Gengchen Mai, et al. ∙

∙ 10/22/2022

LMPriors: Pre-Trained Language Models as Task-Specific Priors

Particularly in low-data regimes, an outstanding challenge in machine le...

9 Kristy Choi, et al. ∙

∙ 09/30/2022

Beyond Bayes-optimality: meta-learning what you know you don't know

Meta-training agents with memory has been shown to culminate in Bayes-op...

8 Jordi Grau-Moya, et al. ∙

∙ 12/06/2021

BCD Nets: Scalable Variational Approaches for Bayesian Causal Discovery

A structural equation model (SEM) is an effective framework to reason ov...

0 Chris Cundy, et al. ∙

∙ 06/23/2021

IQ-Learn: Inverse soft-Q Learning for Imitation

In many sequential decision-making problems (e.g., robotics control, gam...

9 Divyansh Garg, et al. ∙

∙ 12/30/2020

Privacy-Constrained Policies via Mutual Information Regularized Policy Gradients

As reinforcement learning techniques are increasingly applied to real-wo...

0 Chris Cundy, et al. ∙

∙ 07/13/2018

Exploring Hierarchy-Aware Inverse Reinforcement Learning

We introduce a new generative model for human planning under the Bayesia...

0 Chris Cundy, et al. ∙

∙ 09/12/2017

Parallelizing Linear Recurrent Neural Nets Over Sequence Length

Recurrent neural networks (RNNs) are widely used to model sequential dat...

0 Eric Martin, et al. ∙

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