430 citations · 607 across the 19 of their papers we have counts for
3 papers · 1 filter
Uncovering mesa-optimization algorithms in Transformers
Johannes von Oswald, Maximilian Schlegel, Alexander Meulemans +10
Some autoregressive models exhibit in-context learning capabilities: being able to learn as an input sequence is processed, without undergoing any parameter changes, and without be…
Training trajectories, mini-batch losses and the curious role of the learning rate
Mark Sandler, Andrey Zhmoginov, Max Vladymyrov +1
Stochastic gradient descent plays a fundamental role in nearly all applications of deep learning. However its ability to converge to a global minimum remains shrouded in mystery. I…
Continual HyperTransformer: A Meta-Learner for Continual Few-Shot Learning
Max Vladymyrov, Andrey Zhmoginov, Mark Sandler
We focus on the problem of learning without forgetting from multiple tasks arriving sequentially, where each task is defined using a few-shot episode of novel or already seen class…