13 citations · 20 across the 3 of their papers we have counts for
3 papers
Dynamic Sparsity: Challenging Common Sparsity Assumptions for Learning World Models in Robotic Reinforcement Learning Benchmarks
Muthukumar Pandaram, Jakob Hollenstein, David Drexel +3
The use of learned dynamics models, also known as world models, can improve the sample efficiency of reinforcement learning. Recent work suggests that the underlying causal graphs…
HE-MAN -- Homomorphically Encrypted MAchine learning with oNnx models
Martin Nocker, David Drexel, Michael Rader +2
Machine learning (ML) algorithms are increasingly important for the success of products and services, especially considering the growing amount and availability of data. This also…
Classical spin Hamiltonians are context-sensitive languages
Sebastian Stengele, David Drexel, Gemma De las Cuevas
Classical spin Hamiltonians are a powerful tool to model complex systems, characterised by a local structure given by the local Hamiltonians. One of the best understood local struc…