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Can In-Context Learning Support Intrinsic Curiosity?
Eric Elmoznino, Sangnie Bhardwaj, Johannes von Oswald +5
Effective machine learning depends not only on how we model data, but also on what data we choose to collect. While large sequence models have revolutionized data modeling, the pro…
MesaNet: Sequence Modeling by Locally Optimal Test-Time Training
Johannes von Oswald, Nino Scherrer, Seijin Kobayashi +14
Sequence modeling is currently dominated by causal transformer architectures that use softmax self-attention. Although widely adopted, transformers require scaling memory and compu…
From Words to Amino Acids: Does the Curse of Depth Persist?
Aleena Siji, Amir Mohammad Karimi Mamaghan, Ferdinand Kapl +9
Protein language models (PLMs) have become widely adopted as general-purpose models, demonstrating strong performance in protein engineering and de novo design. Like large language…
Emergent temporal abstractions in autoregressive models enable hierarchical reinforcement learning
Seijin Kobayashi, Yanick Schimpf, Maximilian Schlegel +12
Large-scale autoregressive models pretrained on next-token prediction and finetuned with reinforcement learning (RL) have achieved unprecedented success on many problem domains. Du…
Weight decay induces low-rank attention layers
Seijin Kobayashi, Yassir Akram, Johannes Von Oswald
The effect of regularizers such as weight decay when training deep neural networks is not well understood. We study the influence of weight decay as well as -regularization whe…
Linear Transformers are Versatile In-Context Learners
Max Vladymyrov, Johannes von Oswald, Mark Sandler +1
Recent research has demonstrated that transformers, particularly linear attention models, implicitly execute gradient-descent-like algorithms on data provided in-context during the…