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Michael Widrich

4 accepted papers

2024

Incorporating probabilistic domain knowledge into deep multiple instance learning

ICML 2024poster

Deep learning methods, including deep multiple instance learning methods, have been criticized for their limited ability to incorporate domain knowledge. A reason that knowledge incorporation is challenging in deep learning is that the models usually lack a mapping between their model components and…

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2021

Hopfield Networks is All You Need

ICLR 2021poster

We introduce a modern Hopfield network with continuous states and a corresponding update rule. The new Hopfield network can store exponentially (with the dimension of the associative space) many patterns, retrieves the pattern with one update, and has exponentially small retrieval errors. It has thr…

2020

Modern Hopfield Networks and Attention for Immune Repertoire Classification

NeurIPS 2020spotlight

A central mechanism in machine learning is to identify, store, and recognize patterns. How to learn, access, and retrieve such patterns is crucial in Hopfield networks and the more recent transformer architectures. We show that the attention mechanism of transformer architectures is actually the upd…

2019

RUDDER: Return Decomposition for Delayed Rewards

NeurIPS 2019poster

We propose RUDDER, a novel reinforcement learning approach for delayed rewards in finite Markov decision processes (MDPs). In MDPs the Q-values are equal to the expected immediate reward plus the expected future rewards. The latter are related to bias problems in temporal difference (TD) learning an…