Machine Learning at Berkeley

Students at UC Berkeley working on academic research, ML education, industry projects, and fostering a vibrant ML community 🧠💡

[Machine Learning at Berkeley @BerkeleyML]

slides here: https://t.co/lxNgwYiXHk

[Machine Learning at Berkeley @BerkeleyML]

our members ran a great reading group on major architecture changes in deepseek v4!!

they covered: hyper connections + manifold hyper connections, KV cache + MQA/GQA intro, Deepseek Sparse attention (prerequisite to understanding the new CSA) & a walk through of CSA and HCA

[Machine Learning at Berkeley @BerkeleyML]

join us for the last bioml seminar of this semester!!

[Aakarsh Vermani @aakarshv1]

We'll be closing out this semester's Berkeley BioML Seminar on 4/28 with a talk from @antoinekoehl on PEINT, a powerful deep learning framework for both phylogenetic inference and protein engineering. Sign up below! https://t.co/QM7WXMZTu4

[Machine Learning at Berkeley @BerkeleyML]

slides: https://t.co/khN9f2QWcd

stay tuned for our next seminar!

[Machine Learning at Berkeley @BerkeleyML]

super proud of members Avy Harish (@AvyukthH60737), Sahir Tandon and Chris John for hosting our first physics informed ml seminar!

they covered: physics-informed nns, neural odes, sparse identification of nonlinear dynamics, fourier neural operators & other applications

[sarrah rose @AgaRosegirl]

We're excited to announce the fourth Berkeley BioML seminar of the semester happening next Tuesday 4/7! Join us for a talk by Shreshth (@shreshth_gandhi) from @tahoe_ai on Scaling Perturbation-Trained Single-Cell Foundation Models!

https://t.co/QfYDqvYh1N

[Machine Learning at Berkeley @BerkeleyML]

> intuitions of how to go from pytorch to jax
> training language models from scratch
> how to scale ur model pt 2
> lfggg

[chuyi shang @chuyishang]

Wrote a deep dive on implementing a language model from scratch in JAX and scaling it with distributed training!

If you’re coming from PyTorch and want to see how the same ideas look in JAX, or just want a hands-on intro to distributed training, check out this blog post: https://t.co/nsR3O3Zjxg

Comes with code + an assignment and test cases so you can follow along!

[Machine Learning at Berkeley @BerkeleyML]

@tahoe_ai thank you for sharing about the work tahoe does, it was a very fun conversation!

[Machine Learning at Berkeley @BerkeleyML]

@threebarebears hi daniel! we're berkeley's oldest machine learning club and were wondering if you'd be down to talk to us about the process of making hoppers (:

[Machine Learning at Berkeley @BerkeleyML]

who are the best people to talk to about physics + ml?

[sarrah rose @AgaRosegirl]

We're excited to announce the third Berkeley BioML seminar of the semester happening next Tuesday 3/2! Join us for a talk by Vivek Natarajan (@vivnat ) from GDM on advancing science and medicine with collaborative AI agents.

https://t.co/iDnzuX24zI

[Machine Learning at Berkeley @BerkeleyML]

@TransluceAI

[Machine Learning at Berkeley @BerkeleyML]

Join us for a tech talk with Transluce on 2/23 from 6-7 pm, in Cory 540AB! Transluce is a non-profit AI lab working to ensure that AI oversight scales with AI capabilities. (Pizza provided!)

[Machine Learning at Berkeley @BerkeleyML]

We’ll use Docent, our tool for agent oversight that analyzes execution traces at scale, to dig into what’s really happening. Folks should join for an interactive demo and a chance to dig in themselves.

[Machine Learning at Berkeley @BerkeleyML]

this is happening tonight!

[Arshia Nayebnazar @arshianazar]

We're excited to announce the second Berkeley BioML seminar of the semester happening next Tuesday 2/17! Join us for a talk by Kenny Workman (@kenbwork) from LatchBio about the performance of agents for spatial biology analysis. https://t.co/Pk8XaRpOWJ

[Arshia Nayebnazar @arshianazar]

We're excited to announce the second Berkeley BioML seminar of the semester happening next Tuesday 2/17! Join us for a talk by Kenny Workman (@kenbwork) from LatchBio about the performance of agents for spatial biology analysis. https://t.co/Pk8XaRpOWJ

[Machine Learning at Berkeley @BerkeleyML]

check out terry's new post on diffusion language models!!!

[Terry Kim @thtrkim]

I had a fun time writing a deep dive on Diffusion Language Models - with an equation walkthrough and Excalidraw sketches ✏️

In Part 1, I focused on the method: what does “noise” even mean for text, and how do DLMs denoise back into tokens?

https://t.co/G8zSWCesB3

[Aakarsh Vermani @aakarshv1]

Another semester, another Berkeley BioML Seminar! We're excited to kick off this semester with a talk from @jeffruffolo from Profluent Bio on designing proteins with language models. Join us Monday night! https://t.co/N4Lw4JMPsI