Systematic experimental analysis of the role of feature learning in continual learning using scaling limits!
06.12.2024 10:02 — 👍 1 🔁 1 💬 0 📌 0
Harvard Applied Math PhD student. Neural computation
Recently a principal scientist at Google DeepMind. Joining Anthropic. Most (in)famous for inventing diffusion models. AI + physics + neuroscience + dynamical systems.
Keep the gradients flowing
Group Leader, CBS-NTT "Physics of Intelligence" Program at Harvard
website: https://sites.google.com/view/htanaka/home
Professor @UCLA, Research Scientist @ByteDance | Recent work: SPIN, SPPO, DPLM 1/2, GPM, MARS | Opinions are my own
Asst. Prof. in Machine Learning at UofT. #LongCOVID patient.
https://www.cs.toronto.edu/~cmaddis/
Member of Technical Staff at OpenAI
Research scientist at Google. PhD from Stanford. Efficient and trustworthy AI, LLMs, and Gemini data & evaluation.
https://sites.google.com/view/berivanisik
Associate Professor at Princeton
Machine Learning Researcher
Research scientist @NVIDIA | PhD in machine learning @UofT. Previously @Google / @MetaAI. Opinions are my own. 🤖 💻 ☕️
IMPRS-IS PhD student @ University of Tübingen with Ulrike von Luxburg and Bedartha Goswami. Mostly thinking about deep learning theory. Also interested in ML for climate science.
mohawastaken.github.io
Lecturer at the University of Bristol.
probabilistic ML, optimisation, interpretability, LLM evals.
PhD student (EPFL, Switzerland), working on the theory of deep learning and statistical physics of computation.
https://yatindandi.github.io/
Research Director, Founding Faculty, Canada CIFAR AI Chair @VectorInst.
Full Prof @UofT - Statistics and Computer Sci. (x-appt) danroy.org
I study assumption-free prediction and decision making under uncertainty, with inference emerging from optimality.
theory of neural networks for natural and artificial intelligence
https://pehlevan.seas.harvard.edu/
Associate Professor of Electrical Engineering, EPFL.
Amazon Scholar (AGI Foundations). IEEE Fellow. ELLIS Fellow.
