Jovana Mitrovic | DeepAI

DeepAI

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∙ 01/12/2023

SemPPL: Predicting pseudo-labels for better contrastive representations

Learning from large amounts of unsupervised data and a small amount of s...

Matko Bošnjak, et al. ∙

∙ 01/13/2022

Pushing the limits of self-supervised ResNets: Can we outperform supervised learning without labels on ImageNet?

Despite recent progress made by self-supervised methods in representatio...

Nenad Tomašev, et al. ∙

∙ 07/12/2021

CoBERL: Contrastive BERT for Reinforcement Learning

Many reinforcement learning (RL) agents require a large amount of experi...

Andrea Banino, et al. ∙

∙ 05/27/2021

Encoders and Ensembles for Task-Free Continual Learning

We present an architecture that is effective for continual learning in a...

Murray Shanahan, et al. ∙

∙ 10/15/2020

Representation Learning via Invariant Causal Mechanisms

Self-supervised learning has emerged as a strategy to reduce the relianc...

Jovana Mitrovic, et al. ∙

∙ 08/21/2020

Amortized learning of neural causal representations

Causal models can compactly and efficiently encode the data-generating p...

Nan Rosemary Ke, et al. ∙

∙ 02/07/2020

Causally Correct Partial Models for Reinforcement Learning

In reinforcement learning, we can learn a model of future observations a...

Danilo J. Rezende, et al. ∙

∙ 01/23/2019

Causal Reasoning from Meta-reinforcement Learning

Discovering and exploiting the causal structure in the environment is a ...

Ishita Dasgupta, et al. ∙

∙ 04/12/2018

Causal Inference via Kernel Deviance Measures

Discovering the causal structure among a set of variables is a fundament...

Jovana Mitrovic, et al. ∙

∙ 02/04/2018

Hierarchical Adversarially Learned Inference

We propose a novel hierarchical generative model with a simple Markovian...

Mohamed Ishmael Belghazi, et al. ∙

∙ 02/15/2016

DR-ABC: Approximate Bayesian Computation with Kernel-Based Distribution Regression

Performing exact posterior inference in complex generative models is oft...

Jovana Mitrovic, et al. ∙