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Every single time!

It'd been for a while that this draft had been sitting in my drawer with no hope to come out. Finally, putting it out. Theme: Avocado Boba Paper: arxiv.org/abs/2011.14251 Label shift is cool, interesting, important & fundamental. Stay tuned to c soon these methods being applied;)

We’ve developed two neural networks which have learned by associating text and images. CLIP maps images into categories described in text, and DALL-E creates new images, like this, from text. A step toward systems with deeper understanding of the world. openai.com/multimodal/

Our paper is out Nature Communications! Huge shoutout to my PhD advisor Maryam Shanechi for her unconditional support and supervision. Also big thanks to the coauthors Yan Wong, Mahdi, and particularly Bijan Pesaran/Pesaran Lab for their much needed vision & contributions. Summary:👇

Our Nature Biomedical Engineering paper is out. We demonstrate the ability to model & predict the dynamic response of multiregional brain networks to ongoing electrical stimulation. Collab w/Pesaran Lab, 1st-authors: my student Yuxiao Yang & S Qiao in Pesaran Lab Summary👇1/n nature.com/articles/s4155…

A visual search engine for finding images with similar vibes, and not necessarily similar shapes. So impressive.

Update: Python users can now easily install PSID using pip. Also, we now have a Jupyter notebook with some explanations and usage examples for PSID to help get you started: github.com/ShanechiLab/Py… For more info about PSID, see our Nature Neuroscience paper: rdcu.be/b993t

Great review on contrastive representation learning. Still amazes me that I had come across this blog post before this was tweeted.

Training large models demands a lot of GPU memory and a long training time. With several training parallelism strategies and a variety of memory saving designs, it is possible to train very large neural networks across many GPUs. lilianweng.github.io/lil-log/2021/0…

Curiouser and curiouser #stablediffusion deforum

A short but insightful summary of variational diffusion models and score-based generative models. Look forward to the blog post!

In #DiffusionModels, often, a continuous function in time is computed to generate data. In this work, we develop a new #NeuralOperator architecture that allows us to directly predicts this function in one model evaluation, which is amazing. It achieves SOTA in both speed and FID.

I wrote a VSCode extension that allows you to use ChatGPT within the context of your code 🔥 You can click on code blocks to paste them into the editor, or use a selected code snippet as a reference. The extension is on GitHub if you want to check it out github.com/mpociot/chatgp…

Chain ⛓️ Rule(s) rules! Appreciation thread of one of the most interesting coincidences in machine learning. Two rules, both named "Chain Rule", happen to be absolutely critical to recent advances in ML & AI. A 🧵 on the Chain Rule of Probability & the Chain Rule of Calculus👇

Fall 2023 Internship Join us at NVIDIA and be part of a highly esteemed AIAlgo team at the forefront of AI research and development. Prof. Anima Anandkumar nvidia.wd5.myworkdayjobs.com/NVIDIAExternal…

Am watching PyTorch conference keynote and thought to give the torch.compile decorator for NumPy a try. Whoa, in a quick benchmark on my MacBook, I indeed got a ~35x speedup when compiling NumPy code with PyTorch. Exciting times. (Think of the ramifications for scikit-learn).

Just in time for the holidays, we are releasing some new software today from Apple machine learning research. MLX is an efficient machine learning framework specifically designed for Apple silicon (i.e. your laptop!) Code: github.com/ml-explore/mlx Docs: ml-explore.github.io/mlx/build/html…

New in Nature Biomedical Engineering, we present an #AI method that achieves accurate and flexible inference for #brain-computer interfaces. 🎉 It can infer causally in real time, non-causally, & even with missing neural samples, as can happen in wireless #BCI 🧠💻 🧵& code ⬇️ nature.com/articles/s4155…

Placebo effect for LLMs? 🤔