"Performing MLPerf benchmarks is no easy task, and often involves the work of many engineers. But a single graduate student, with some consultation, can do it, too. @tri_dao was that graduate student." spectrum.ieee.org/mlperf-ranking…
Tri on the market next year!!! HE IS AMAZING!
Hazy Research: Strip Mall AI Research Club
1,767 posts
A research group in @StanfordAILab working on the foundations of machine learning & systems. hazyresearch.stanford.edu Ostensibly supervised by Chris Ré
- Announcing the new live-streamed Stanford MLSys Seminar Series, in which we will explore the frontier of machine learning and systems. Read the full announcement: hazyresearch.stanford.edu/mlsys-seminar Schedule: mlsys.stanford.edu Intro video: youtu.be/OEiNnfdxBRE
- Hogwild! NeurIPS test-of-time award talk. This was a super fun project, and I'm happy anyone else enjoyed it. Thank you, @NeurIPSConf! All credit to Feng Niu, @beenwrekt, @madsjw.
- This was quite a week in AI! Google's "no moat" leak and amazing new releases by @togethercompute @MosaicML @BigCodeProject our blog talks about how AI's technical moat is shrinking and why it's good to be optimistic about open source: hazyresearch.stanford.edu/blog/2023-05-0…
- Thank you so much for the fun keynote, @NeurIPSConf As in every year, our lab had a blast! We've enjoyed connecting with so many smart, enthusiastic people--and learning about your work. What an exciting time in AI! Some asked for slides: cs.stanford.edu/~chrismre/pape… and video
- The Great American AI Race. I wrote something about how we need a holistic AI effort from academia, industry, and the US government to have the best shot at a freer, better educated, and healthier world in AI. I’m a mega bull on the US and open source AI. Maybe we’re cooking
- A love song to the database community...There are issues, but it's an amazing time to be in the area. Please don't forget it! dawn.cs.stanford.edu/2018/04/11/db-…
- Some initial work with @percyliang and Braden Hancock on supervising models using only natural language hazyresearch.github.io/snorkel/blog/b…
- Thank you to @arcinstitute and the great @BrianHie for teaching us so much and taking a chance on a group that insists on naming its models after zoo animals (hyenaDNA, (hungry or not!) hippos, mamba) ... and its odd choices in AI architectures too. This project wouldn't haveA new Science study presents “Evo”—a machine learning model capable of decoding and designing DNA, RNA, and protein sequences, from molecular to genome scale, with unparalleled accuracy. Evo’s ability to predict, generate, and engineer entire genomic sequences could change the
- Thank you to @arcinstitute and the great @BrianHie for teaching us so much and taking a chance on a group that insists on naming its models after zoo animals (hyenaDNA, (hungry or not!) hippos, mamba) ... and its odd choices in AI architectures too. This project wouldn't have
- An Unserious Person’s Take on Axiomatic Knowledge in the Era of Foundation Models. hazyresearch.stanford.edu/blog/2024-11-1… This post explains why we started the work that led to Evo (HyenaDNA), recently on the cover of Science–thanks to a host of wonderful collaborators at @arcinstitute . It
- Amazing what's going on at statsml.stanford.edu, four best papers just this year. This group is amazing: @percyliang, Emma Brunskill, John Duchi, Greg Valiant, @ermonste. Junior faculty teaching thousands and making amazing contributions to ML. Greg is also a great cook.
- Don't throw out your algorithms books! @pbailis and @matei_zaharia take a close look at learned indexes: dawn.cs.stanford.edu/2018/01/11/ind…
- bit.ly/2AUKsrL @ylecun's take on @alirahimi19 & @beenwrekt talk. I didn't take it as theory vs. DL. Theory not only tool for rigor. NN progress is awesome, but rigor in DL experiments not uniform: simple experiments, stronger baselines, deeper understanding help everyone











