274 citations · 433 across the 19 of their papers we have counts for
4 papers · 1 filter
Neural Active Learning on Heteroskedastic Distributions
Savya Khosla, Chew Kin Whye, Jordan T. Ash +3
Models that can actively seek out the best quality training data hold the promise of more accurate, adaptable, and efficient machine learning. Active learning techniques often tend…
Eigen Memory Trees
Mark Rucker, Jordan T. Ash, John Langford +2
This work introduces the Eigen Memory Tree (EMT), a novel online memory model for sequential learning scenarios. EMTs store data at the leaves of a binary tree and route new sample…
Transformers Learn Shortcuts to Automata
Bingbin Liu, Jordan T. Ash, Surbhi Goel +2
Algorithmic reasoning requires capabilities which are most naturally understood through recurrent models of computation, like the Turing machine. However, Transformer models, while…
Understanding Contrastive Learning Requires Incorporating Inductive Biases
Nikunj Saunshi, Jordan Ash, Surbhi Goel +5
Contrastive learning is a popular form of self-supervised learning that encourages augmentations (views) of the same input to have more similar representations compared to augmenta…