Machine Learning in Science
@mackelab.bsky.social did:plc:emm26nxpk3io5vvwfxma2yxz
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
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Machine Learning in Science (@mackelab.bsky.social) ist seit 14 November 2024 auf Bluesky. Das Konto hat 2.653 Follower, folgt 198 Konten und hat 128 Posts veröffentlicht.
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What are the dynamics underlying sequence working memory? To found out, we develop and analyse RNNs fitted to multi-session single-unit data of macaques. See my talk Wed 12:00, at @bernsteinneuro.bsky.social. With @jakhmack.bsky.social @mackelab.bsky.social Data from Chen et al., Neuron 2024.
Post anzeigenWe are excited to announce up to 3 PhD positions in a new project on using ML for "Extracting Probabilistic Representations in Exponential Quantum Spaces" (EXPRESSO) with @philipphennig.bsky.social , @mariokrenn.bsky.social, Igor Lesanovsky, @gmartius.bsky.social in the @ml4science.bsky.social.
Post anzeigenI'd have nothing to talk about on this episode if not for my paper co-authors in the @mackelab.bsky.social, as well as Pedro, Jan-Matthis, and Michael paving the way with SBI.
Post anzeigenOnce you get past the first 5 minutes of me bumbling around clearly in need of media training, we settle into a nice groove talking about fitting mechanistic models, ML/AI for neuroscience, underconstrainedness vs. degeneracy, and its potential benefits for biology (plus some good chuckles).
Post anzeigenI’m super excited to present our new work in #Eurips2025 and #Neurips2025! We developed FNOPE: a new simulation-based inference (SBI) method which excels at inferring function-valued parameters! Paper: openreview.net/forum?id=yB5... Code: github.com/mackelab/fnope (1/9)
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