Fred Sala @fredsala
Assistant Professor @WisconsinCS. Research scientist @SnorkelML. Prev. Stanford postdoc. Working on machine learning & information theory. pages.cs.wisc.edu/~fredsala/ Madison, WI Joined July 2010-
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Learn how to get better foundation model performance without additional fine-tuning. This event is TODAY at Noon Pacific. Time is running out! buff.ly/495ZtmN
There is an explosion of total nonsense around LLM auto-eval in the space these days... be wary of approaches that claim to kick the human out of the loop! E.g. 'Using GPT-4 to auto-eval' makes sense only if you are trying to *distill* GPT-4 functionality. I.e. you are…
Excited to present the first day of the @RealAAAI 2024 Spring Symposium on Clinical Foundation Models! 🎊 Today's lineup includes keynotes, a panel discussion, demos, contributed talks, and a poster session! @clinicalfms
We just released Bonito 🐟, an open-source model that converts your raw, unannotated data into synthetic instruction tuning datasets. With it, you can easily create a specialized LLM for your proprietary and private data! (1/n) github.com/BatsResearch/b…
Interested in GNNs? Check out our implementation of GCNs on Cartesian products of Hyperbolic, Spherical, and Euclidean manifolds. It is the only public and open source implementation of such a network that I'm aware of. github.com/mcneela/Mixed-…
Our preprint on non-Euclidean representation learning for biological pathway graphs is now on arXiv arxiv.org/abs/2401.15478 This research was led by @dannymcneela who is co-advised by @fredsala. 1/
Excited to share that our work on improving the robustness of foundation models, without training or data, will be at @iclr_conf ! Longer version of our paper that won best paper honorable mention at the NeurIPS R0-FoMo workshop last month.
Excited to share that our work on improving the robustness of foundation models, without training or data, will be at @iclr_conf ! Longer version of our paper that won best paper honorable mention at the NeurIPS R0-FoMo workshop last month.
Excited to announce the 𝐁𝐞𝐬𝐭 𝐏𝐚𝐩𝐞𝐫 𝐀𝐰𝐚𝐫𝐝𝐬 (2 papers) and 𝐇𝐨𝐧𝐨𝐫𝐚𝐛𝐥𝐞 𝐌𝐞𝐧𝐭𝐢𝐨𝐧𝐬 (2 more papers) for our NeurIPS workshop R0-FoMo: Robustness of Few-shot & Zero-shot Learning in Foundation Models 🎉 Please join us in congratulating the authors 👏
🎉Exciting News! Join us for the AAAI-24 Spring Symposium on Clinical Foundation Models! 🚀Explore captivating talks, engaging panels, & cutting-edge demos. All details (CfP, timelines, ...) available at 👉clinicalfoundationmodels.github.io. Hurry, papers due by Jan 5, 2024 @clinicalfms
Heading to #NeurIPS 2023 from Dec 10-17! My collaborators and I will present 1. Fairness in weak supervision 2. How to use LLMs for foundation model test-time adaptation @ R0-FoMo Workshop 3. Improving data wrangling LLMs with better demonstrations @ TRL Workshop 🧵
Gonna present this work as an oral at the R0 FoMo workshop on Friday, Dec 15th at 4:10 pm! Feel free to come by ☺️
Gonna present this work as an oral at the R0 FoMo workshop on Friday, Dec 15th at 4:10 pm! Feel free to come by ☺️
This year, @UWMadison CS researchers had an incredible 30(!) papers accepted to @NeurIPSConf. Join us in congratulating all involved, and explore a sampling of their work below. 🧵@uwcdis, @UWMadisonLS, @madsjw, @UWMadScience cs.wisc.edu/2023/12/06/uw-…
Stoked to be headed to @NeurIPSConf #NeurIPS2023 soon! Come check out our papers this year! (Thurs 10:45) Geometry Aware Adaptation for Pretrained Models (Weds 10:45) Skill-it! A Data-Driven Skills Framework for Understanding and Training LMs x.com/mayeechen/stat… 🎉
Stoked to be headed to @NeurIPSConf #NeurIPS2023 soon! Come check out our papers this year! (Thurs 10:45) Geometry Aware Adaptation for Pretrained Models (Weds 10:45) Skill-it! A Data-Driven Skills Framework for Understanding and Training LMs x.com/mayeechen/stat… 🎉
Lauren Pick (CI Postdoc) is on the job market! Lauren has been doing exceptional verification/synthesis work. Notably, her work on distributed systems was used to verify real-world systems at AWS, and she recently had 2 PLDI 23 papers. Hire Lauren! lmpick.github.io
Join us this #GivingTuesday in supporting the new home for innovation and knowledge at the School of Computer, Data & Information Sciences. Your contribution not only supports our new building but also makes a lasting impact on the future of technology💻 ow.ly/2USp50Qcc7t
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“Now imagine the collection of all things that are weird in this way, and think of this collection as itself being a thing.”
high level math: "imagine a thing. now imagine that it's weird."
In a world with Llama3/Phi3/Arctic/etc (and more coming!), base LLMs are now a commodity. AI is now all about the inputs & outputs that customize an LLM for unique use cases: - Inputs: Labeled, curated data to prompt/tune/align - Outputs: How you map from model -> product/UX
Congratulations to #UWMadison professor @prowag, who was awarded the Evjue Distinguished Chair; and to Russ Castronovo, David Kaplan, Mark Kenoyer, Stephen Wright, and Ellen Zweibel for being awarded Hilldale Professorships! #OnWisconsin news.wisc.edu/evjue-chair-hi…
Our own Assistant Professor Sameer Deshpande harnesses advanced statistical techniques to better understand the world of sports. In our new Q&A, Desphande talked about his sports analytics research, his 3 years at the UW, and lots more. ⚾ 📈 Read on: ow.ly/QsPF50RoeiU
See why AI data development is a 'hot area.' Co-founder and Head of Applied Machine Learning, Paroma Varma, sits down with Fierce Network to discuss the role of data in enterprise AI. Thanks to Diana Goovaerts for the fantastic coverage! 👉 buff.ly/4dfxNio
1/ With LLMs like Llama 3 & Phi 3, enterprises are no longer blocked on the models. The game is now about one thing: developing the data to tune & evaluate these LLMs for real business use cases. Excited to support this w/ the new @SnorkelAI release! venturebeat.com/data-infrastru… 🧵
There is a really nice community of researchers developing transformer alternatives. Want to highlight these impressive folks. Simran Arora (@simran_s_arora), Chunting Zhou (@violet_zct), Dan Fu (@realDanFu), and Songlin Yang (@SonglinYang4)
How it started How it ended #Hawks @datascience_uw
100%! Access to annotated data is the key to enterprise AI. The key though is that this AI-ready data is not one-size-fits all- it’s annotated *for each specific use case / operating setting*. This game is not about data lakes… it’s about scalable data labeling & development
One of my biggest takeaways from the Llama3 announcement - data gravity is king. And access to high quality, annotated, LLM ready data will drive which companies truly utilize AI vs those who don't As the number of accessible, highly performant models continues to grow (both…
Such a welcome addition to the benchmarking landscape! - Creating benchmarks that correlate well with humans and separate top models has become increasingly hard as they’ve become increasingly capable. - And knowing it will be refreshed reduces the incentive for organizations…
Introducing Arena-Hard – a pipeline to build our next generation benchmarks with live Arena data. Highlights: - Significantly better separability than MT-bench (22.6% -> 87.4%) - Highest agreement to Chatbot Arena ranking (89.1%) - Fast & cheap to run ($25) - Frequent update…
At this point I feel like we understand pretty well what's going on with LLMs: - Outputs are roughly equivalent to kernel smoothing over positional embeddings (arxiv.org/pdf/1908.11775…) - The learned computation model is *probably* bounded by RASP-L (arxiv.org/pdf/2310.16028…) -…
Awesome post! In particular re: the POV on LLM progress, which I could not agree more with obviously: "Currently bottlenecked by high quality annotated data" :) And, this bottleneck only grows exponentially when you consider that "high quality" is specific to each use case.
At this point I feel like we understand pretty well what's going on with LLMs: - Outputs are roughly equivalent to kernel smoothing over positional embeddings (arxiv.org/pdf/1908.11775…) - The learned computation model is *probably* bounded by RASP-L (arxiv.org/pdf/2310.16028…) -…
Incredibly excited to see OSS LLMs leap forward w/ @Meta's Llama 3, and for @SnorkelAI to be part of the "Llama ecosystem" ai.meta.com/blog/llama-2-u… The base LLMs are there. The next frontier in enterprise AI is tuning & aligning models like Llama 3 on unique data & use cases.
👀New paper alert!👀 Novel Symmetry-preserving #NeuralNetwork Model for #Phylogenetic Inference @BioinfoAdv by awesome student Xudong Tang @harry_txd Paper: go.wisc.edu/q9cz75 3-min talk with the highlights 👇 youtu.be/EWPvTnVnHAg
Now that it is official, my amazing student Chris Harshaw is joining the statistics department @Columbia as an Assistant Professor. Super proud of him. chrisharshaw.com
Congrats Jiefeng. Jiefeng was co-advised by me and Prof. Yingyu Liang and did impressive work during his PhD at @WisconsinCS Eager to see his research trajectory at @Google
Thrilled to announce that I've joined Google as a Research Scientist! I'm excited to dive into LLM research and contribute to cutting-edge developments in the field. Looking forward to this new chapter! 🚀 #NewJob #ResearchScientist #Google
Excited to announce Snorkel Custom, a hands-on accelerator for enterprises to build LLMs customized on their data, for their use cases: snorkel.ai/introducing-sn… This is the year to cross the chasm from flashy chatbot demo to real production AI. We are here to help!
Join us for a dynamic panel discussion at Google Cloud Next (Wednesday, April 10, 11:45 AM PDT) where our very own Paroma Varma will share real-world insights from successful startups and growth companies. #Diversityandinclusion #Startups #GrowthCompanies #TalentAcquisition
Classical mixture models are limited to positive weights and this requires learning very large mixtures! Can we learn (deep) mixtures with negative weights? Answer in our #ICLR2024 spotlight by @loreloc_ Aleks, Martin, Stefan, Nicolas @arnosolin 📜openreview.net/forum?id=xIHi5…
After the score-based models, I decided to take a step back and cover the basics in a new blog post: Probabilistic modeling and Mixture Models. Additionally, I make a brief intro to ✨Probabilistic Circuits✨ Check: 📄Post: jmtomczak.github.io/blog/19/19_mog… 🖥️ Code: github.com/jmtomczak/intr…
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