Maneesh Sahani | DeepAI

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∙ 09/07/2023

A State Representation for Diminishing Rewards

A common setting in multitask reinforcement learning (RL) demands that a...

0 Ted Moskovitz, et al. ∙

∙ 06/23/2023

Prediction under Latent Subgroup Shifts with High-Dimensional Observations

We introduce a new approach to prediction in graphical models with laten...

0 William I. Walker, et al. ∙

∙ 05/24/2023

Successor-Predecessor Intrinsic Exploration

Exploration is essential in reinforcement learning, particularly in envi...

0 Changmin Yu, et al. ∙

∙ 09/13/2022

Unsupervised representational learning with recognition-parametrised probabilistic models

We introduce a new approach to probabilistic unsupervised learning based...

0 William I. Walker, et al. ∙

∙ 09/12/2022

Amortised Inference in Structured Generative Models with Explaining Away

A key goal of unsupervised learning is to go beyond density estimation a...

0 Changmin Yu, et al. ∙

∙ 07/17/2022

Minimum Description Length Control

We propose a novel framework for multitask reinforcement learning based ...

0 Ted Moskovitz, et al. ∙

∙ 12/03/2021

Divergent representations of ethological visual inputs emerge from supervised, unsupervised, and reinforcement learning

Artificial neural systems trained using reinforcement, supervised, and u...

7 Grace W. Lindsay, et al. ∙

∙ 09/28/2021

A First-Occupancy Representation for Reinforcement Learning

Both animals and artificial agents benefit from state representations th...

0 Ted Moskovitz, et al. ∙

∙ 02/22/2020

Amortised Learning by Wake-Sleep

Models that employ latent variables to capture structure in observed dat...

0 Li K. Wenliang, et al. ∙

∙ 06/22/2019

A neurally plausible model learns successor representations in partially observable environments

Animals need to devise strategies to maximize returns while interacting ...

0 Eszter Vertes, et al. ∙

∙ 06/01/2019

Kernel Instrumental Variable Regression

Instrumental variable regression is a strategy for learning causal relat...

14 Rahul Singh, et al. ∙

∙ 02/12/2019

Learning interpretable continuous-time models of latent stochastic dynamical systems

We develop an approach to learn an interpretable semi-parametric model o...

0 Lea Duncker, et al. ∙

∙ 07/04/2018

Empirical fixed point bifurcation analysis

In a common experimental setting, the behaviour of a noisy dynamical sys...

0 Gergo Bohner, et al. ∙

∙ 05/28/2018

Flexible and accurate inference and learning for deep generative models

We introduce a new approach to learning in hierarchical latent-variable ...

0 Eszter Vertes, et al. ∙

∙ 11/02/2017

A Universal Marginalizer for Amortized Inference in Generative Models

We consider the problem of inference in a causal generative model where ...

0 Laura Douglas, et al. ∙

∙ 10/24/2014

Bayesian Manifold Learning: The Locally Linear Latent Variable Model (LL-LVM)

We introduce the Locally Linear Latent Variable Model (LL-LVM), a probab...

0 Mijung Park, et al. ∙

∙ 01/23/2013

Regularization and nonlinearities for neural language models: when are they needed?

Neural language models (LMs) based on recurrent neural networks (RNN) ar...

0 Marius Pachitariu, et al. ∙

∙ 06/27/2012

Variational Inference in Non-negative Factorial Hidden Markov Models for Efficient Audio Source Separation

The past decade has seen substantial work on the use of non-negative mat...

0 Gautham Mysore, et al. ∙

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