32 citations · 67 across the 12 of their papers we have counts for
9 papers · 1 filter
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…
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…
Would I have gotten that reward? Long-term credit assignment by counterfactual contribution analysis
Alexander Meulemans, Simon Schug, Seijin Kobayashi +2
To make reinforcement learning more sample efficient, we need better credit assignment methods that measure an action's influence on future rewards. Building upon Hindsight Credit…
The least-control principle for local learning at equilibrium
Alexander Meulemans, Nicolas Zucchet, Seijin Kobayashi +2
Equilibrium systems are a powerful way to express neural computations. As special cases, they include models of great current interest in both neuroscience and machine learning, su…
Credit Assignment in Neural Networks through Deep Feedback Control
Alexander Meulemans, Matilde Tristany Farinha, Javier García Ordóñez +3
The success of deep learning sparked interest in whether the brain learns by using similar techniques for assigning credit to each synaptic weight for its contribution to the netwo…
Challenges for Using Impact Regularizers to Avoid Negative Side Effects
David Lindner, Kyle Matoba, Alexander Meulemans
Designing reward functions for reinforcement learning is difficult: besides specifying which behavior is rewarded for a task, the reward also has to discourage undesired outcomes.…
Neural networks with late-phase weights
Johannes von Oswald, Seijin Kobayashi, Alexander Meulemans +3
The largely successful method of training neural networks is to learn their weights using some variant of stochastic gradient descent (SGD). Here, we show that the solutions found…
A Theoretical Framework for Target Propagation
Alexander Meulemans, Francesco S. Carzaniga, Johan A. K. Suykens +2
The success of deep learning, a brain-inspired form of AI, has sparked interest in understanding how the brain could similarly learn across multiple layers of neurons. However, the…
Continual Learning in Recurrent Neural Networks
Benjamin Ehret, Christian Henning, Maria R. Cervera +3
While a diverse collection of continual learning (CL) methods has been proposed to prevent catastrophic forgetting, a thorough investigation of their effectiveness for processing s…