Evgenii Egorov @eeevgen
PhD researcher University of Amsterdam / AMLab. Structure-aware ML for scientific systems: inference, efficient architectures, quantum AI4Science. evgenii-egorov.github.io Amsterdam Joined April 2010-
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I think if one can make interpretation of the model by linearization that means model still can make much more efficient use of parameters.
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@2prime_PKU Can you please share how from practical aspect did you organize this human-AI interaction?
I see for some reason only skeptical posts about Anthropic in drug discovery. I think they will be top 1 across all ai4drug with in 6 month
@nblqbl @AlessandroStru4 @PhysRevLett Fadeev has lecture notes where he introduce correspondence between brackets
My little experience with chatgpt5.5pro: it is quite hard to force it to proof something from start to end. It enjoys jumping around and glueing some results with words and also use the word “roughly” as extra glue. Probably I am doing something wrong. I can imagine professional mathematic get a sketch or idea from this consistently but non-professional I don’t think so.
Huge fan
We launched Relational Intelligence. rel-int.ai Why? Current language models are powerful but unexplainable. For one thing, they are locked into GPUs with no path to quantum hardware, with no path to efficiency, with no path to scalability... How? The same structure
Viterbi algorithm strongly suggest that we need also Backward Engineer.
I think the greatest mathematician was either Sophus Lie or Ellie Cartan. I don’t have a qualification to separate.
@B1ar2n3a Great! Can you please share what was your setup for this? Did you use chat gpt directly or as agent with codex?
I would appreciate if people share their setup of using GPT (as a general name) for mathematical derivations.
Short memo from Yuri Leyderman in a help of review writing.
Aaand than you can distill. And than unroll. And than distill. And than? I don’t know probably it is secretly Bayesian rule.
can't wait until all the loops people rediscover loop unrolling
Evgenii Egorov retweeted
John Jumper, on why AlphaFold2 worked: > "It's not one or two home runs. It's 18 doubles. Midsize wins stacked together." His least popular take on AlphaFold2 is the over-crediting of SE(3)-equivariance / geometric deep learning. He ablated it and it only lifted performance about 2/30 points. FAPE (the frame-aligned point error) was actually most responsible for its improvement.
@notnotrishi @MilesCranmer @liztansz Why than this one is tricky? Do you have a good way of thinking visualization about second?
Tried to explain how I visualize knight moves so draw this
I think whole time academy spread and glory this instructions for perfect submission and crystallizing this phrases to use in text and responds. What is it as not removing human? And now when LLM can follow this script most precise well it is some how not a good thing. Lol.
Well, everybody angry in Antropic , but only few understand that there is a secrete society of scientists and politicians that aims to slow down progress of humanity. They act through shutting down nuclear plants, “you X is secretly Y” and SVM++.
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