Jimmy Smith @jimmysmith1919
Founding Scientist @LiquidAI_. PhD student @Stanford. Member of @scott_linderman's lab. jimmysmith1919.github.io Stanford, CA Joined October 2017-
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Today we announce our collaboration with Capgemini to build next-generation AI solutions for enterprises. For the last months, we've been working on this together and now following Capgemini's participation in Liquid AI's successful $37.6m seed round, we are committed to…
What role does pre-training diversity play in the emergence of in-context learning? Come see our poster #727 at 5:15 to learn more! x.com/mansiege/statu…
What role does pre-training diversity play in the emergence of in-context learning? Come see our poster #727 at 5:15 to learn more! x.com/mansiege/statu…
Excited to be at #NeurIPS23 this week! I will be presenting our poster on ConvSSMs for modeling long video sequences Tuesday at 5:15pm: nips.cc/virtual/2023/p…. Come find me or DM me if you want to discuss SSMs, efficient sequence modeling, transformer alternatives, etc.
Liquid AI will host a private social event @NeurIPSConf on Thursday, Dec 14th, 7-9pm CST. RSVP here: forms.gle/SLfvvqv5oRPF23… Also, check out our team's contributions: liquid.ai/blog/liquid-at…
Hello World! Excited to be out of stealth mode! Let me introduce you to our Liquid team: Joscha Bach @Plinz (MIT, Harvard, Intel) Jimmy Smith @jimmysmith1919 (Stanford) Stefano Massaroli @Massastrello (MILA) Paul Pak @paulpak__ (NASA, UW-Madison) Noel Loo…
Cool work and impressive results! While S4 and most variants are closer to CNNs, S5 can be seen as maximizing its potential as an RNN. This can unlock creative applications, like examining the hidden states or addressing irregular sampling. Many more cool ablations in the paper!
Cool work and impressive results! While S4 and most variants are closer to CNNs, S5 can be seen as maximizing its potential as an RNN. This can unlock creative applications, like examining the hidden states or addressing irregular sampling. Many more cool ablations in the paper!
Following the weekend buzz about long range sequence modeling, let me tell you about our work on S5: a simple state space layer that achieves top-level performance and efficiency with parallel scans. (E.g. 98.58% on Path-X!) 🧵[1/12]
Ghost @Test113476
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Of course, the credit goes to all the researchers whose contributions has made of this field such an interesting research direction! @_albertgu @MichaelPoli6 @exnx @realDanFu @HazyResearch @jimmysmith1919 & many more!
I am very happy to give this tutorial next week! We will discuss several developments on sub-quadratic long-context architectures such as SSMs, CKConv, Hyena and Mamba. Thank you @Ellis_Amsterdam for having me!
💥 We are excited that @davidwromero (@nvidia) will talk about 'Beyond Transformers: Exploring Subquadratic Long-Context Architectures' at the upcoming Deep Thinking Hour Tutorial! 📅 Thu, April 11th ⏰️09:00 - 11:00 📍L1.01 of @Lab42UvA Come and deep think with us! 🏍
Today we announce our collaboration with Capgemini to build next-generation AI solutions for enterprises. For the last months, we've been working on this together and now following Capgemini's participation in Liquid AI's successful $37.6m seed round, we are committed to…
Want to learn about SGD's implicit bias towards simpler subnetworks generated by permutation symmetry?! Come to our NeurIPS poster session tomorrow morning 10:45 - 12:45 Hall B1+B2 (level 1) #906
Our new preprint reveals how SGD biases neural nets towards vastly simpler subnets w/ superior generalization via stochastic collapse to invariant sets & explains why prolonged large learning rates help co-led w/ @FCHEN_AI @atsushi_y1230 & @SuryaGanguli arxiv.org/abs/2306.04251
Come check out Laughing Hyena today! Poster #441 at 10:45 arxiv: arxiv.org/abs/2310.18780 @MichaelPoli6 @realDanFu
What role does pre-training diversity play in the emergence of in-context learning? Come see our poster #727 at 5:15 to learn more! x.com/mansiege/statu…
Can in-context learning learn new tasks different from those in the pretraining data? Is this an emergent ability, i.e. does it arise from pretraining without being explicitly optimized for? How does this depend on pretraining task diversity? 🧵 1/ arxiv.org/abs/2306.15063
Liquid AI will host a private social event @NeurIPSConf on Thursday, Dec 14th, 7-9pm CST. RSVP here: forms.gle/SLfvvqv5oRPF23… Also, check out our team's contributions: liquid.ai/blog/liquid-at…
Hello World! Excited to be out of stealth mode! Let me introduce you to our Liquid team: Joscha Bach @Plinz (MIT, Harvard, Intel) Jimmy Smith @jimmysmith1919 (Stanford) Stefano Massaroli @Massastrello (MILA) Paul Pak @paulpak__ (NASA, UW-Madison) Noel Loo…
I am delighted to announce the release of “dynamax”, an open source library for dynamic state space models in JAX. It supports inference and learning in HMMs, Linear Gaussian SSMs, as well as non-linear and non-Gaussian SSMs. See github.com/probml/dynamax
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