Yaron Lipman
@lipmanya
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Research scientist @AIatMeta (FAIR), prev/visiting @WeizmannScience. Interested in generative models and deep learning of irregular/geometric data.🎗️
Israel
Joined August 2014
A new (and comprehensive) Flow Matching guide and codebase released! Join us tomorrow at 9:30AM @NeurIPSConf for the FM tutorial to hear more.
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RT @shaulneta: DTM vs FM👇.Lots of interest in how Difference Transition Matching (DTM) connects to Flow Matching (FM). Here is a short anim….
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RT @shaulneta: If you're curious to dive deeper into Transition Matching (TM)✨🔍, a great starting point is understanding the similarities a….
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RT @shaulneta: [1/n].New paper alert! 🚀.Excited to introduce 𝐓𝐫𝐚𝐧𝐬𝐢𝐭𝐢𝐨𝐧 𝐌𝐚𝐭𝐜𝐡𝐢𝐧𝐠 (𝐓𝐌)! We're replacing short-timestep kernels from Flow Mat….
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RT @shaulneta: Difference Transition Matching (DTM) process is so simple to Illustrate, you can calculate it on a whiteboard!. At each step….
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RT @urielsinger: Introducing Transition Matching (TM) — a new generative paradigm that unifies Flow Matching and autoregressive models into….
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**Transition Matching** is a new iterative generative paradigm using Flow Matching or AR models to transition between generation intermediate states, leading to an improved generation quality and speed!
[1/n].New paper alert! 🚀.Excited to introduce 𝐓𝐫𝐚𝐧𝐬𝐢𝐭𝐢𝐨𝐧 𝐌𝐚𝐭𝐜𝐡𝐢𝐧𝐠 (𝐓𝐌)! We're replacing short-timestep kernels from Flow Matching/Diffusion with. a generative model🤯, achieving SOTA text-2-image generation!.@urielsinger @itai_gat @lipmanya
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RT @guanhorng_liu: Adjoint-based diffusion samplers have simple & scalable objectives w/o impt weight complication. Like many, though, they….
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A new paper: We finetune an LLM to rethink and resample previously generated tokens, allowing to reduce sampling errors and improve performance.
Excited to share our recent work on corrector sampling in language models! A new sampling method that mitigates error accumulation by iteratively revisiting tokens in a window of previously generated text. With: @shaulneta @urielsinger @lipmanya.Link:
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RT @RickyTQChen: Padding in our non-AR sequence models? Yuck. 🙅. 👉 Instead of unmasking, our new work *Edit Flows* perform iterative refine….
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RT @Luckyballa: So Flow Matching is *just* . xt = mix(x0, x1, t).loss = mse((x1 - x0) - nn(xt, t)). Nice, here it is in a fragment shader :….
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RT @RickyTQChen: We've open sourced Adjoint Sampling!. It's part of a bundled release showcasing FAIR's research and open source commitment….
github.com
code for "Adjoint Sampling: Highly Scalable Diffusion Samplers via Adjoint Matching" - facebookresearch/adjoint_sampling
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RT @RickyTQChen: Against conventional wisdom, I will be giving a talk with particular focus on the "how" and the various intricacies of app….
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RT @shaulneta: Had an absolute blast presenting at #ICLR2025! Thanks to everyone who came to visit my poster🙌 Special shoutout to @drscotth….
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RT @shaulneta: Got lots of questions about kinetic energy in continuous vs discrete space during my poster! Made a simple slide to help exp….
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RT @peholderrieth: Even better if friends and colleagues join you for the same session :) Our work on “Flow Matching with General Discrete….
arxiv.org
The design space of discrete-space diffusion or flow generative models are significantly less well-understood than their continuous-space counterparts, with many works focusing only on a simple...
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RT @peholderrieth: Come to our oral presentation on Generator Matching at ICLR 2025 tomorrow (Saturday). Learn about a generative model tha….
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RT @shaulneta: 📣I'll be at the poster session with our follow-up on Discrete Flow Matching. We derive a closed-form solution to the kinetic….
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