Do you not think that the predominant issue is that many reviewers are ill-equipped to deliver a non-vacuous opinion? If so, it isn’t clear to me that this solves the problem
24.11.2024 11:50 — 👍 8 🔁 0 💬 1 📌 0
Senior Staff Research Scientist @Google DeepMind, previously Stats Prof @Oxford Uni - interested in Computational Statistics, Generative Modeling, Monte Carlo methods, Optimal Transport.
Research scientist at Anthropic. Prev. Google Brain/DeepMind, founding team OpenAI. Computer scientist; inventor of the VAE, Adam optimizer, and other methods. ML PhD. Website: dpkingma.com
research scientist at google deepmind.
phd in neural nonsense from stanford.
poolio.github.io
Co-Founder & Chief Scientist @ Emmi AI. Ass. Prof / Group Lead @jkulinz. Former MSFTResearch, UvA_Amsterdam, CERN, TU_Wien
Research Scientist at DeepMind. Opinions my own. Inventor of GANs. Lead author of http://www.deeplearningbook.org . Founding chairman of www.publichealthactionnetwork.org
AI for materials design at Microsoft Research AI for Science | Prev. University of Cambridge. Views are my own.
Science is the best use of ML
Working towards the safe development of AI for the benefit of all at Université de Montréal, LawZero and Mila.
A.M. Turing Award Recipient and most-cited AI researcher.
https://lawzero.org/en
https://yoshuabengio.org/profile/
VP of AI Research, Principal Scientist @ EIT Oxford | ex-Director @ DeepMind Building models to accelerate fundamental sciences and medicine.
Opinions my own.
https://danilorezende.com/
Opensource dev @ WPandCo && Web tech @ Citoyens.com && Photography @ Photopalpininst
Machine Learning researcher at @Xaira_Thera (former @CambridgeEllis and @OxCSML) opinions expressed are my own.
Sr Researcher at Microsoft Research AI for Science
Former Oxford Stats PhD in Bayesian Experimental Design
PhD researcher in Machine Learning at Imperial College. Visiting at University of Oxford.
Interested in all things involving causality and Bayesian machine learning. Recently I have also been interested in scaling theory.
https://anish144.github.io/
Deep learner at FAIR. Into codegen, RL, equivariance, generative models. Spent time at Qualcomm, Scyfer (acquired), UvA, Deepmind, OpenAI.
San Diego Dec 2-7, 25 and Mexico City Nov 30-Dec 5, 25. Comments to this account are not monitored. Please send feedback to townhall@neurips.cc.
Machine learning lab at Columbia University. Probabilistic modeling and approximate inference, embeddings, Bayesian deep learning, and recommendation systems.
🔗 https://www.cs.columbia.edu/~blei/
🔗 https://github.com/blei-lab
Lecturer in Maths & Stats at Bristol. Interested in probabilistic + numerical computation, statistical modelling + inference. (he / him).
Homepage: https://sites.google.com/view/sp-monte-carlo
Seminar: https://sites.google.com/view/monte-carlo-semina
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