Search Results for author: Bruno Mlodozeniec

Found 5 papers, 3 papers with code

Denoising Diffusion Probabilistic Models in Six Simple Steps

no code implementations6 Feb 2024 Richard E. Turner, Cristiana-Diana Diaconu, Stratis Markou, Aliaksandra Shysheya, Andrew Y. K. Foong, Bruno Mlodozeniec

Denoising Diffusion Probabilistic Models (DDPMs) are a very popular class of deep generative model that have been successfully applied to a diverse range of problems including image and video generation, protein and material synthesis, weather forecasting, and neural surrogates of partial differential equations.

Denoising Video Generation +1

Meta- (out-of-context) learning in neural networks

1 code implementation23 Oct 2023 Dmitrii Krasheninnikov, Egor Krasheninnikov, Bruno Mlodozeniec, David Krueger

Brown et al. (2020) famously introduced the phenomenon of in-context learning in large language models (LLMs).

In-Context Learning

Timewarp: Transferable Acceleration of Molecular Dynamics by Learning Time-Coarsened Dynamics

1 code implementation NeurIPS 2023 Leon Klein, Andrew Y. K. Foong, Tor Erlend Fjelde, Bruno Mlodozeniec, Marc Brockschmidt, Sebastian Nowozin, Frank Noé, Ryota Tomioka

Molecular dynamics (MD) simulation is a widely used technique to simulate molecular systems, most commonly at the all-atom resolution where equations of motion are integrated with timesteps on the order of femtoseconds ($1\textrm{fs}=10^{-15}\textrm{s}$).

Ensemble Distribution Distillation

1 code implementation ICLR 2020 Andrey Malinin, Bruno Mlodozeniec, Mark Gales

The properties of EnD$^2$ are investigated on both an artificial dataset, and on the CIFAR-10, CIFAR-100 and TinyImageNet datasets, where it is shown that EnD$^2$ can approach the classification performance of an ensemble, and outperforms both standard DNNs and Ensemble Distillation on the tasks of misclassification and out-of-distribution input detection.

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