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04/25/2023
Towards Compute-Optimal Transfer Learning
The field of transfer learning is undergoing a significant shift with th...
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05/28/2022
Task-Agnostic Continual Reinforcement Learning: In Praise of a Simple Baseline
We study task-agnostic continual reinforcement learning (TACRL) in which...
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11/15/2021
Continual Learning via Local Module Composition
Modularity is a compelling solution to continual learning (CL), the prob...
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10/27/2021
Learning where to learn: Gradient sparsity in meta and continual learning
Finding neural network weights that generalize well from small datasets ...
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08/02/2021
Sequoia: A Software Framework to Unify Continual Learning Research
The field of Continual Learning (CL) seeks to develop algorithms that ac...
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04/04/2021
Understanding Continual Learning Settings with Data Distribution Drift Analysis
Classical machine learning algorithms often assume that the data are dra...
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03/18/2021
Beyond Trivial Counterfactual Explanations with Diverse Valuable Explanations
Explainability for machine learning models has gained considerable atten...
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09/14/2020
Synbols: Probing Learning Algorithms with Synthetic Datasets
Progress in the field of machine learning has been fueled by the introdu...
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09/14/2020
CVPR 2020 Continual Learning in Computer Vision Competition: Approaches, Results, Current Challenges and Future Directions
In the last few years, we have witnessed a renewed and fast-growing inte...
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03/12/2020
Online Fast Adaptation and Knowledge Accumulation: a New Approach to Continual Learning
Learning from non-stationary data remains a great challenge for machine ...
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11/19/2019
Online Learned Continual Compression with Stacked Quantization Module
We introduce and study the problem of Online Continual Compression, wher...
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08/11/2019
Online Continual Learning with Maximally Interfered Retrieval
Continual learning, the setting where a learning agent is faced with a n...
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11/06/2018
