Blog Archive

Check out all blog posts in our blog archive.

14 March 2023 › Aaditya Ramdas

Conformal prediction under distribution shift Read More ›

25 October 2022 › Fredrik Lindsten

Deep Gaussian Markov Random Fields Read More ›

11 October 2022 › Guy Wolf

Geometry-based Data Exploration Read More ›

5 July 2022 › Michele Ceriotti

A unified theory of atom cloud representations for machine learning Read More ›

21 June 2022 › Peter Grünwald

E is the New P: Evidence and Optional Continuation Read More ›

7 June 2022 › Wujie Wang

Generative Coarse-Graining of Molecular Conformations Read More ›

24 May 2022 › Francesca Grisoni

Harnessing artificial intelligence for de novo drug design Read More ›

26 April 2022 › Maximilian Dax

Real-Time Gravitational Wave Science with Neural Posterior Estimation Read More ›

14 April 2022 › Gabriel Vivo-Truyols

On the use of Bayesian statistics for chromatography and mass spectrometry: dealing with big data Read More ›

29 March 2022 › Anna Scaife

Catalogue curation, likelihood misspecification and dataset shift: challenges for Bayesian deep-learning in radio astronomy Read More ›

15 March 2022 › Rajesh Ranganath

Interpretability and Out of Distribution Generalization in Deep Predictive Models Read More ›

1 March 2022 › Martin van Hecke

Machine Learning of Combinatiorial Rules in Mechanical Metamaterials Read More ›

7 February 2022 › Jan-Matthis Lückmann

Simulation-Based Inference for Neuroscience and Beyond Read More ›

18 January 2022 › Andrew Ferguson

Ultra-fast molecular simulators and data-driven protein design Read More ›

21 December 2021 › Atılım Güneş Baydin

Probabilistic Programming in Scientific Simulators Read More ›

23 November 2021 › Chris Rackauckas

The Interplay of Science-Guided AI and Differentiable Simulation Read More ›

9 November 2021 › Jan-Willem van de Meent

Compositional Inference in Probabilistic Programs Read More ›

31 August 2021 › Saer Samanipour

Machine Learning and High Resolution Mass Spectrometry Read More ›

22 June 2021 › Pratyush Tiwari

Can artificial intelligence help understand and predict molecular dynamics? Read More ›

25 May 2021 › Alexander Tkatchenko

On Electrons and Machine Learning Force Fields Read More ›

11 May 2021 › Chandler Squires

Predicting Gene Expression across Cell Types and Drugs Read More ›

13 April 2021 › Ferry Hooft

Discovering Collective Variables of Molecular Transitions via Genetic Algorithms and Neural Networks Read More ›

30 March 2021 › Benjamin Miller

Constrained marginal likelihood-to-evidence ratio estimation Read More ›

2 March 2021 › Jakub Tomczak

All that glitters is not Deep Learning in Life Sciences (but sometimes it is!) Read More ›

16 February 2021 › Eliu Huerta Escudero

Towards Accelerated, Reproducible, Physics-informed AI-driven Discovery Read More ›

2 February 2021 › Mario Geiger

e3nn: Euclidean symmetry for neural networks Read More ›

19 January 2021 › Dim Coumou

Machine Learning in climate science: Finding causal connections and improving seasonal forecasts Read More ›

24 November 2020 › Luisa Lucie-Smith

Machine Learning the formation of dark matter halos in the Universe Read More ›

10 November 2020 › Frank Noé

Boltzmann-generating Flows Read More ›

27 October 2020 › Gábor Csányi

Representation and regression problems for molecular structure and dynamics Read More ›

13 October 2020 › David Fischer

Attributing variance in single-cell genomics Read More ›

29 September 2020 › Christoph Weniger

Precision analysis of gravitational strong lensing images with nested likelihood-free inference Read More ›

15 September 2020 › Erik Henning Thiede

Permutation-Equivariant neural networks for Molecular Generation Read More ›

30 June 2020 › Tristan Bereau

Physically-motivated machine learning for multiscale molecular simulations Read More ›