13 citations · 32 across the 6 of their papers we have counts for
6 papers
CN101 - A Digital Thermodynamic Computer for Generative AI
Lars Holdijk, Denis Melanson, Zier Mensch +14
Thermodynamic computing is an emerging hardware paradigm, in which stochastic physical dynamics serve as the direct computational primitive. The recent explosion of generative AI h…
Robust Stochastic Gradient Posterior Sampling with Lattice Based Discretisation
Zier Mensch, Lars Holdijk, Samuel Duffield +4
Stochastic-gradient MCMC methods enable scalable Bayesian posterior sampling but often suffer from sensitivity to minibatch size and gradient noise. To address this, we propose Sto…
Stochastic Optimal Control for Collective Variable Free Sampling of Molecular Transition Paths
Lars Holdijk, Yuanqi Du, Ferry Hooft +3
We consider the problem of sampling transition paths between two given metastable states of a molecular system, e.g. a folded and unfolded protein or products and reactants of a ch…
Learning Equivariant Energy Based Models with Equivariant Stein Variational Gradient Descent
Priyank Jaini, Lars Holdijk, Max Welling
We focus on the problem of efficient sampling and learning of probability densities by incorporating symmetries in probabilistic models. We first introduce Equivariant Stein Variat…
Robustness of Generalized Learning Vector Quantization Models against Adversarial Attacks
Sascha Saralajew, Lars Holdijk, Maike Rees +1
Adversarial attacks and the development of (deep) neural networks robust against them are currently two widely researched topics. The robustness of Learning Vector Quantization (LV…
Prototype-based Neural Network Layers: Incorporating Vector Quantization
Sascha Saralajew, Lars Holdijk, Maike Rees +1
Neural networks currently dominate the machine learning community and they do so for good reasons. Their accuracy on complex tasks such as image classification is unrivaled at the…