Michal Balcerak (@X_MichalB) header
Michal Balcerak (@X_MichalB) profile picture

Michal Balcerak

@X_MichalB

Kempner Research Fellow @ Harvard University | energy-based models, generative modeling & reasoning

 Cambridge, MA, USA
michalbalcerak.ai
Joined May 2019
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Photo by Michal Balcerak (@X_MichalB)Photo by Michal Balcerak (@X_MichalB)Photo by Michal Balcerak (@X_MichalB)
Generative Modeling: What's After Flow Matching? Flow Matching lacks explicit modeling of scores on the data manifold. Introducing Energy Matching [NeurIPS 2025] unlocking exciting new inference-time capabilities! Paper: arxiv.org/pdf/2504.10612 Code: github.com/m1balcerak/Energy…
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Happy to join Harvard University’s @KempnerInst as a Research Fellow! I’ll continue to explore energy-based models for generation and reasoning. Excited about the future of the field! If you work on similar questions, let’s connect here or at NeurIPS in Sydney (presenting GEM).
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Hannes Stark (@HannesStaerk) profile picture
Hannes Stark@HannesStaerkJul 12
Monday Starkly Speaking: we have a session with @X_MichalB explaining the Jordan–Kinderlehrer–Otto (JKO) scheme based on the "Energy Matching" paperhttps://arxiv.org/abs/2504.10612 On Zoom 12pm ET / 6pm CEST: hannes-stark.com/starkly-spe…
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What’s after discrete diffusion? 💎🧬 Graph Energy Matching (GEM): molecular graphs sampled via transport-aligned discrete proposals. SOTA among graph EBMs. Matches/surpasses diffusion/flow baselines at generation. Supercharges inference-time design! michalbalcerak.ai/graph-ener…
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Sean Welleck (@wellecks) profile picture
Sean Welleck
@wellecksFeb 27
Excited to announce our workshop on flow-based generative models at CMU: Frontiers of Flows for Generative AI March 26-27, Pittsburgh PA cmu-l3.github.io/flows2026/ We have an amazing lineup of featured talks, panel discussions, and lightning talks. Registration is now open!
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menze_group (@menze_group) profile picture
menze_group@menze_group6 Jul 2025
Our 'Glioma Optimizing a Discrete Loss' (GliODIL) method is now in Nature Communications! 🧠 It leverages reaction-diffusion priors with MRI + FET-PET data to map patient-specific glioma spread and guide radiotherapy planning.
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Come chat with us tonight on Discord about Energy Matching: Unifying Flow Matching and Energy-Based Models for Generative Modeling 6pm UTC, be there: discord.gg/RPVNUdVu?event=13…
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Stat.ML Papers (@StatMLPapers) profile picture
Stat.ML Papers@StatMLPapers16 Apr 2025
Energy Matching: Unifying Flow Matching and Energy-Based Models for Generative Modeling ift.tt/WaFqg8p
Energy Matching: Unifying Flow Matching and Energy-Based Models...

Energy Matching: Unifying Flow Matching and Energy-Based Models...

Current state-of-the-art generative models map noise to data distributions by matching flows or scores. A key limitation of these models is their inability to readily integrate available partial...

arxiv.org
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menze_group (@menze_group) profile picture
menze_group@menze_group22 Oct 2024
NeurIPS news – We’re excited to share that our paper, "Physics-Regularized Multi-Modal Image Assimilation for Brain Tumor Localization," has been accepted at NeurIPS 2024 in Vancouver! 🚀
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Acai users have a good day - how is yours?
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🌟 Big announcement 🌟 We have established cooperation with HitBTC, the #1 crypto exchange in the world by liquidity! It exists for >6 years and meets our security standards. Sign up for free at acai.dev 🙋‍♂️
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