7.8k citations · 21.1k across the 144 of their papers we have counts for
4 papers · 2 filters
Adapting Auxiliary Losses Using Gradient Similarity
Yunshu Du, Wojciech M. Czarnecki, Siddhant M. Jayakumar +3
One approach to deal with the statistical inefficiency of neural networks is to rely on auxiliary losses that help to build useful representations. However, it is not always trivia…
Progress & Compress: A scalable framework for continual learning
Jonathan Schwarz, Jelena Luketina, Wojciech M. Czarnecki +4
We introduce a conceptually simple and scalable framework for continual learning domains where tasks are learned sequentially. Our method is constant in the number of parameters an…
Been There, Done That: Meta-Learning with Episodic Recall
Samuel Ritter, Jane X. Wang, Zeb Kurth-Nelson +4
Meta-learning agents excel at rapidly learning new tasks from open-ended task distributions; yet, they forget what they learn about each task as soon as the next begins. When tasks…
Memory-based Parameter Adaptation
Pablo Sprechmann, Siddhant M. Jayakumar, Jack W. Rae +7
Deep neural networks have excelled on a wide range of problems, from vision to language and game playing. Neural networks very gradually incorporate information into weights as the…