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02/20/2023
Neural Algorithmic Reasoning with Causal Regularisation
Recent work on neural algorithmic reasoning has investigated the reasoni...
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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...
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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...
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07/12/2021
CoBERL: Contrastive BERT for Reinforcement Learning
Many reinforcement learning (RL) agents require a large amount of experi...
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05/27/2021
Encoders and Ensembles for Task-Free Continual Learning
We present an architecture that is effective for continual learning in a...
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10/15/2020
Representation Learning via Invariant Causal Mechanisms
Self-supervised learning has emerged as a strategy to reduce the relianc...
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08/21/2020
Amortized learning of neural causal representations
Causal models can compactly and efficiently encode the data-generating p...
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02/07/2020
Causally Correct Partial Models for Reinforcement Learning
In reinforcement learning, we can learn a model of future observations a...
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01/23/2019
Causal Reasoning from Meta-reinforcement Learning
Discovering and exploiting the causal structure in the environment is a ...
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04/12/2018
Causal Inference via Kernel Deviance Measures
Discovering the causal structure among a set of variables is a fundament...
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02/04/2018
Hierarchical Adversarially Learned Inference
We propose a novel hierarchical generative model with a simple Markovian...
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02/15/2016