Forwarded from 懒人的梦呓 (virusyu🅥)
推荐吴恩达的新书：《How to build your career in AI》，不需要技术门槛也能阅读，这本书提供了全方位的AI职业发展建议，包括AI基础技能需要什么，求职面试，如何打造作品集，怎么建立人脉网络等，如果你想往AI方面发展，很值得一读。
下载地址：https://info.deeplearning.ai/how-to-build-a-career-in-ai-book
下载地址：https://info.deeplearning.ai/how-to-build-a-career-in-ai-book
info.deeplearning.ai
How to Build Your Career in AI eBook - Andrew Ng Collected Insights
Get The How to Build Your Career in AI eBook By Andrew NG | Free download | an introductory book about starting and building a successful career in AI
Forwarded from Data Mining & Machine learning (Hirah Tang)
David Stutz
The Importance of Effectively Experimenting in an AI PhD • David Stutz
Engineering and running experiments are a key component of most PhDs in AI. While there are plenty of more theoretical topics that are often limited to smaller scale experimentation, the trend has definitely been to scale up models, datasets and experiments.…
Forwarded from Graph Machine Learning
GraphML News (Nov 9th) - ELLIS PhD Applications, Protenix AF3, New papers
The next week is going to be busy with ICLR rebuttals, so we still have a bit of time to check the news and read new papers.
🎓 The call for PhD applications within ELLIS, the European network of ML and DL labs, is ending soon (November 15th) - this is a great opportunity to start (or continue) your academic journey in a top machine learning lab!
🧬 ByteDance released Protenix, a trainable PyTorch reproduction of AlphaFold 3, with model checkpoints (so you can run it locally) and with the inference server. The tech report is coming, would be interesting to compare with Chai-1 and other open source reproductions.
Weekend reading:
A Cosmic-Scale Benchmark for Symmetry-Preserving Data Processing by Julia Balla et al feat Tess Smidt - introduces cool new graph datasets - given a point cloud of 5000 galaxies, predict their cosmological properties on the graph level and node level (eg, galaxy velocity).
FlowLLM: Flow Matching for Material Generation with Large Language Models as Base Distributions by Anuroop Sriram and FAIR (NeurIPS 24): extension of FlowMM (ICML 2024), a flow matching model for crystal structure generation, but now instead of sampling from a Gaussian, the authors fine-tuned LLaMa 2 on the Materials Project to sample 10000 candidates (using just a small academic budget of 300 A100 gpus) - this prior yields 3x more stable structures.
Flow Matching for Accelerated Simulation of Atomic Transport in Materials by Juno Nam and MIT team feat. Rafael Gómez-Bombarelli - introduces LiFlow, a flow matching model for MD simulations of crystalline materials: where ab-initio methods would take 340 days to simulate 1 ns of a 200-atoms structure, LiFlow takes only 48 seconds 🏎️
The next week is going to be busy with ICLR rebuttals, so we still have a bit of time to check the news and read new papers.
🎓 The call for PhD applications within ELLIS, the European network of ML and DL labs, is ending soon (November 15th) - this is a great opportunity to start (or continue) your academic journey in a top machine learning lab!
🧬 ByteDance released Protenix, a trainable PyTorch reproduction of AlphaFold 3, with model checkpoints (so you can run it locally) and with the inference server. The tech report is coming, would be interesting to compare with Chai-1 and other open source reproductions.
Weekend reading:
A Cosmic-Scale Benchmark for Symmetry-Preserving Data Processing by Julia Balla et al feat Tess Smidt - introduces cool new graph datasets - given a point cloud of 5000 galaxies, predict their cosmological properties on the graph level and node level (eg, galaxy velocity).
FlowLLM: Flow Matching for Material Generation with Large Language Models as Base Distributions by Anuroop Sriram and FAIR (NeurIPS 24): extension of FlowMM (ICML 2024), a flow matching model for crystal structure generation, but now instead of sampling from a Gaussian, the authors fine-tuned LLaMa 2 on the Materials Project to sample 10000 candidates (using just a small academic budget of 300 A100 gpus) - this prior yields 3x more stable structures.
Flow Matching for Accelerated Simulation of Atomic Transport in Materials by Juno Nam and MIT team feat. Rafael Gómez-Bombarelli - introduces LiFlow, a flow matching model for MD simulations of crystalline materials: where ab-initio methods would take 340 days to simulate 1 ns of a 200-atoms structure, LiFlow takes only 48 seconds 🏎️
Forwarded from Data Mining & Machine learning (Hirah Tang)
X (formerly Twitter)
Paul Thompson (@PTenigma) on X
If you are interested in Generative #AI, or statistical physics, you will know that you can use latent diffusion models to make synthetic images (or videos), but these methods are a bit slow (I explain here how the Fokker-Planck formulation and Langevin diffusion…
Forwarded from Data Mining & Machine learning (Hirah Tang)
Forwarded from Data Mining & Machine learning (Hirah Tang)
mcbal
Blog posts | mcbal