Attending ICML Conference and interested in scaling up biologically plausible learning rules? Stop by our virtual #ICML2020 poster Tuesday, July 14, 0700-0745 PT or 2000-2045 PT for a Q&A! Further details (and video co-presented with Javier Sagastuy) here: icml.cc/virtual/2020/p…

Also, for people not registered for #icml2020 , you can access our preprint and a video of our presentation this past March at #Neuromatch 1.0 here: ai.stanford.edu/blog/icml-2020…

Really happy to have collaborated on this piece. Come take a look! 👀

This was one of the best papers I read recently. There has been a lot of progress on understanding deep learning by uncovering underlying symmetries under certain assumptions. This paper assembles everything in one place and that too for practically used learning algorithms!

This is easily among the best papers I've come across this year. The authors consolidate knowledge of training dynamics and present excellent insights derived from the symmetries of model parameters. A must-read!

Really happy to see this preprint from the first project I was involved in Stanford NeuroAI Lab finally out!

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Just wrapped up my new course on Machine Learning Methods for Neural Data Analysis! We ended with a virtual poster session in GatherTown, and we even took this class photo for posterity :) Huge thanks to this amazing group of students!

1/ I'm often confronted with skepticism that neural network models of the brain are intelligible, or that they're even proper models at all, considering how "different they look" from real brains.

Why would giant concrete columns be invisible? Likely because the vision algorithms are trying to detect objects via categorization, and these don’t resemble a known category. A perfect example of when “visual intuitive physics” is needed, and I doubt oodles more data will help.

Come take a look at our new preprint on the limiting dynamics of SGD!

I made an online tool for taking structured, searchable, and shareable notes on academic papers: paperaweek.com After 2+ years of using it myself, I'm excited to share it more broadly! Details in thread:

21 thoughts from 2021 I'd like to take into 2022:

1/3 We release our ImageNet pretrained Recurrent CNN models, which currently best explain neural dynamics & temporally varying visual behaviors. Ready to be used with 1 line of code! Models: tinyurl.com/ms88azrv Paper (to appear in Neural Computation): tinyurl.com/2p8kcka7

#QQLcontest qql.art/token/0x026397…

At CosyneMeeting, our poster at 3-022 will discuss how to measure similarity between DNN model activations and biological neural responses. Please stop by if you're interested! w/ Javier Sagastuy* (* equal co-authors) Aran Nayebi Rosa Cao Daniel Yamins #cosyne2023

Come check this out tomorrow! Really deep work (pun partially intended 😀) on how to compare deep neural networks to brain data. In particular, what should the transform class be from artificial units to biological ones? Can we use models & data-driven methods to inform this?

Bit of an update: I recently joined Wispr Flow to build wearable neural interfaces 🧠🚀 !

Imran Thobani presents a free lunch for predictivity and specificity in neural comparisons: By dealing with activations in a neural network appropriately wrt their biological interpretation, we can find measures that are both predictive of, and identified by, neural data 🔥

Excited to give a talk on our work (w/ Javier Sagastuy mohammad hossein Rosa Cao Daniel Yamins) on inter-animal transforms at the CogCompNeuro Battle of the Metrics (5:15-7 pm EST)! We develop a principled approach to measuring similarity between DNNs and the brain. #CCN2024