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srinathnamburi.bsky.social

@srinathnamburi.bsky.social
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albertge.bsky.social @albertge.bsky.social · 08/05/2025
Online data mixing reduces training costs for foundation models, but faces challenges: ⚠️ Human-defined domains miss semantic nuances ⚠️ Limited eval accessibility ⚠️ Poor scalability Introducing 🎵R&B: first regroup data, then dynamically reweight domains during training!
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Reposted by @srinathnamburi.bsky.social
Fred Sala @fredsala.bsky.social · 23/04/2025
Today at @iclr-conf.bsky.social, come chat with @changho.bsky.social about what types of data drive weak-to-strong generalization!
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Fred Sala @fredsala.bsky.social · 11/12/2024
First up at #NeurIPS2024 from our group, our work on labeling via programmatic distillation (a spotlight!). Label your data orders of magnitude faster and cheaper — come join us today at Poster Session 2 East for a demo!
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Harit Vishwakarma @harit7.bsky.social · 11/12/2024
Excited to present Colander at #NeurIPS2024, our new framework for optimizing confidence functions to make auto-labeling more efficient and reliable. Check out our poster #1906 at today's evening poster session. Wed, Dec 11, 4:30–7:30 p Poster #1906 Project: harit7.github.io/colander
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