Search Results for author: Oliver Richter

Found 7 papers, 4 papers with code

Normalized Attention Without Probability Cage

2 code implementations19 May 2020 Oliver Richter, Roger Wattenhofer

Attention architectures are widely used; they recently gained renewed popularity with Transformers yielding a streak of state of the art results.

The Frechet Distance of training and test distribution predicts the generalization gap

no code implementations25 Sep 2019 Julian Zilly, Hannes Zilly, Oliver Richter, Roger Wattenhofer, Andrea Censi, Emilio Frazzoli

Empirically across several data domains, we substantiate this viewpoint by showing that test performance correlates strongly with the distance in data distributions between training and test set.

Learning Theory Transfer Learning

On Identifiability in Transformers

no code implementations ICLR 2020 Gino Brunner, Yang Liu, Damián Pascual, Oliver Richter, Massimiliano Ciaramita, Roger Wattenhofer

We show that, for sequences longer than the attention head dimension, attention weights are not identifiable.

Learning Policies through Quantile Regression

no code implementations27 Jun 2019 Oliver Richter, Roger Wattenhofer

Policy gradient based reinforcement learning algorithms coupled with neural networks have shown success in learning complex policies in the model free continuous action space control setting.

Using State Predictions for Value Regularization in Curiosity Driven Deep Reinforcement Learning

1 code implementation30 Sep 2018 Gino Brunner, Manuel Fritsche, Oliver Richter, Roger Wattenhofer

Learning in sparse reward settings remains a challenge in Reinforcement Learning, which is often addressed by using intrinsic rewards.

reinforcement-learning

Teaching a Machine to Read Maps with Deep Reinforcement Learning

1 code implementation20 Nov 2017 Gino Brunner, Oliver Richter, Yuyi Wang, Roger Wattenhofer

Localization and navigation is also an important problem in domains such as robotics, and has recently become a focus of the deep reinforcement learning community.

Navigate reinforcement-learning

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