Jürgen Schmidhuber
4 papers in the PaperMetrix corpus
Papers by this author
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Improving Generalization in Meta Reinforcement Learning using Learned Objectives
2019 · arXiv (Cornell University)
Biological evolution has distilled the experiences of many learners into the general learning algorithms of humans. Our novel meta reinforcement learning algorithm MetaGenRL is inspired by this process. MetaGenRL distills the experiences of many complex …
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Enhancing the Transformer with Explicit Relational Encoding for Math Problem Solving
2019 · arXiv (Cornell University)
We incorporate Tensor-Product Representations within the Transformer in order to better support the explicit representation of relation structure. Our Tensor-Product Transformer (TP-Transformer) sets a new state of the art on the recently-introduced Mathematics Dataset containing …
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Meta Learning Backpropagation And Improving It
2020 · arXiv (Cornell University)
Many concepts have been proposed for meta learning with neural networks (NNs), e.g., NNs that learn to reprogram fast weights, Hebbian plasticity, learned learning rules, and meta recurrent NNs. Our Variable Shared Meta Learning (VSML) …
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The Benefits of Model-Based Generalization in Reinforcement Learning
2022 · arXiv (Cornell University)
Model-Based Reinforcement Learning (RL) is widely believed to have the potential to improve sample efficiency by allowing an agent to synthesize large amounts of imagined experience. Experience Replay (ER) can be considered a simple kind …