Machine Learning in Science

@mackelab.bsky.social

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We build probabilistic #MachineLearning and #AI Tools for scientific discovery, especially in Neuroscience. Probably not posted by @jakhmack.bsky.social. 📍 @ml4science.bsky.social‬, Tübingen, Germany

Left to right: Philipp Berens, Anna Levina, Matthijs Pals, Peter Dayan, Jakob Macke.

Results on the pyloric simulator.
(a) Voltage traces from the experimental measurement (top) and a posterior predictive simulated using the posterior mean from TSNPE-PF as the parameter (bottom).
(b) Average distance (energy scoring rule) to observation and percentage of valid simulation from posterior samples; compared to experimental results obtained in Glaser et al.

SBI benchmark results for amortized and sequential NPE-PF.
C2ST for NPE, NLE, and NPE-PF across ten reference posteriors (lower is better); dots indicate averages and bars show 95% confidence intervals over five independent runs.

Illustration of NPE and NPE-PF: Both approaches use simulations sampled from the prior and simulator. In (standard) NPE, a neural density estimator is trained to obtain the posterior. In NPE-PF, the posterior is evaluated by autoregressively passing the simulation dataset and observations to TabPFN.