103 citations · 192 across the 7 of their papers we have counts for
8 papers
BrainBERT: Self-supervised representation learning for intracranial recordings
Christopher Wang, Vighnesh Subramaniam, Adam Uri Yaari +4
We create a reusable Transformer, BrainBERT, for intracranial recordings bringing modern representation learning approaches to neuroscience. Much like in NLP and speech recognition…
Continual Learning In Environments With Polynomial Mixing Times
Matthew Riemer, Sharath Chandra Raparthy, Ignacio Cases +3
The mixing time of the Markov chain induced by a policy limits performance in real-world continual learning scenarios. Yet, the effect of mixing times on learning in continual rein…
On the Role of Weight Sharing During Deep Option Learning
Matthew Riemer, Ignacio Cases, Clemens Rosenbaum +2
The options framework is a popular approach for building temporally extended actions in reinforcement learning. In particular, the option-critic architecture provides general purpo…
Posing Fair Generalization Tasks for Natural Language Inference
Atticus Geiger, Ignacio Cases, Lauri Karttunen +1
Deep learning models for semantics are generally evaluated using naturalistic corpora. Adversarial methods, in which models are evaluated on new examples with known semantic proper…
Routing Networks and the Challenges of Modular and Compositional Computation
Clemens Rosenbaum, Ignacio Cases, Matthew Riemer +1
Compositionality is a key strategy for addressing combinatorial complexity and the curse of dimensionality. Recent work has shown that compositional solutions can be learned and of…
Stress-Testing Neural Models of Natural Language Inference with Multiply-Quantified Sentences
Atticus Geiger, Ignacio Cases, Lauri Karttunen +1
Standard evaluations of deep learning models for semantics using naturalistic corpora are limited in what they can tell us about the fidelity of the learned representations, becaus…