@lcharlin
1/n Does scaling test-time compute on LLM self-verification keep paying off? 🤔View quoted post on X
Not really. Our work shows scaling compute makes judgements more consistent, but not necessarily more correct 🧵
Paper: arxiv.org/abs/2611.00350
Blog: anirudhb11.github.io/blog/scorpio
Code: github.com/anirudhb11/sco…
The entire industry is racing to make AI cheaper. Most people assume the answer is better chips or bigger GPU clusters. However, while the industry is buying its next 2× when there's a 1.78× speedup is sitting in the software.
We've been hard at work @unifiedsciences, building an autonomous optimization engine toward radically more efficient compute.
Today I'm very happy to share some early results where our unified engine:
1) wrote faster GEMM kernels for cuBLAS (up to 1.78x faster)
2) made an efficient AI model 6x smaller and 5x faster without any quality loss
3) optimized diverse everyday AI workloads on a consumer GPU!View quoted post on X
ever been here?
open overleaf → write a paragraph → "hmm...this needs a citation" → open 15 different tabs → skim 8 abstracts → find the 1 actually relevant paper → format bibtex → paste it back on overleaf
if so, i built a plugin just for you. meet openleaf:
→ reads your paper paragraph by paragraph
→ searches major academic databases
→ filters out irrelevant papers using ai
→ one click to add BibTeX to your .bib
you'll also find the 🤝 friendly and 🔥 fire reviewers there. i don't think i need to tell you what they do :)
free. open source. no account. no data collection.
works with ollama, openrouter, openai api and more.
github.com/demfier/openle…
dear algorithm, please show this to my fellow researchers in need 🙏
#overleaf #latex #opensource #academictwitterView quoted post on X
How can we make recommender systems more transparent and controllable without sacrificing quality? Introducing TEARS a scrutable RS that replaces numerical user profiles with editable text summaries (accepted @TheWebConf).View quoted post on X
arxiv.org/abs/2410.19302
1/🧵
Le professeur affilié à Mila Laurent Charlin (@lcharlin) co-organise l'atelier Compositional Learning: Perspectives, Methods, and Paths Forward à @NeurIPSConf aujourd'hui, West Meeting Room 118-120. Venez le découvrir!View quoted post on X
