activity
20182024
most citedLearning where to learn: Gradient sparsity in meta and continual learning

24 citations · 24 across the 2 of their papers we have counts for

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5 papers · 1 filter

cs.LG2024

WorkArena: How Capable Are Web Agents at Solving Common Knowledge Work Tasks?

Alexandre Drouin, Maxime Gasse, Massimo Caccia +9

We study the use of large language model-based agents for interacting with software via web browsers. Unlike prior work, we focus on measuring the agents' ability to perform tasks…

cs.LG20232 cited

Towards Compute-Optimal Transfer Learning

Massimo Caccia, Alexandre Galashov, Arthur Douillard +6

The field of transfer learning is undergoing a significant shift with the introduction of large pretrained models which have demonstrated strong adaptability to a variety of downst…

cs.LG202124 cited

Learning where to learn: Gradient sparsity in meta and continual learning

Johannes von Oswald, Dominic Zhao, Seijin Kobayashi +4

Finding neural network weights that generalize well from small datasets is difficult. A promising approach is to learn a weight initialization such that a small number of weight ch…

cs.LG2019

Online Learned Continual Compression with Adaptive Quantization Modules

Lucas Caccia, Eugene Belilovsky, Massimo Caccia +1

We introduce and study the problem of Online Continual Compression, where one attempts to simultaneously learn to compress and store a representative dataset from a non i.i.d data…

cs.LG2019

Online Continual Learning with Maximally Interfered Retrieval

Rahaf Aljundi, Lucas Caccia, Eugene Belilovsky +4

Continual learning, the setting where a learning agent is faced with a never ending stream of data, continues to be a great challenge for modern machine learning systems. In partic…