Lo mejor: en muchos casos animaciones más cercanas a la realidad cuando se trate de simulaciones de redes neuronales, algoritmos, etc Puedo hacer animaciones más rápido, y las puedo hacer con más calidad y menos fatiga! Bienvenidos a la era del VIBE ANIMATING
MACHINE LEARNING
-
Multimodal Models and Vision-First Foundation Model Scaling
By
–
Multimodal, world models, recent works on scaling up vision-first foundational models…You have unique views/experience on these things.
-
Adaptive Branching Tree Search for LLM Inference Scaling
By
–
“Wider or Deeper? Scaling LLM Inference-Time Compute with Adaptive Branching Tree Search” has been accepted as a spotlight at #NeurIPS2025 ✨
— hardmaru (@hardmaru) 22 septembre 2025
Paper: https://t.co/Xj6uLJITe4
Blog: https://t.co/3qSUaEixQU
GitHub: https://t.co/fo6LxAf9AP https://t.co/vIX0t5bbOn“Wider or Deeper? Scaling LLM Inference-Time Compute with Adaptive Branching Tree Search” has been accepted as a spotlight at #NeurIPS2025 Paper: https://
arxiv.org/abs/2503.04412
Blog: https://
sakana.ai/ab-mcts/
GitHub: https://
github.com/SakanaAI/TreeQ
uest
… -
MIT E-Book on Algorithms for Decision-Making Available Free
By
–
[700-page PDF e-Book] #Algorithms for Decision-Making — brilliant and comprehensive eBook from MIT: http://
algorithmsbook.com/decisionmaking/ -
Comprehensive 798-Page Machine Learning Algorithms Guide Book Review
By
–
Brilliant 798-page book explains “every #ML algorithm from A to Z” — I have a copy and I love it! >> "Mastering #MachineLearning Algorithms" at http://
amzn.to/37cDZ7F by @GiuseppeB —————
#Python #DeepLearning #AI #DataScience #Mathematics #Calculus #DataScientist -
Hands-On Genetic Algorithms with Python: Second Edition Guide
By
–
Hands-On Genetic Algorithms with #Python — Apply genetic algorithms to solve real-world #AI and #MachineLearning problems [2nd Edition]: http://
amzn.to/3DDQUWs v/ @PacktDataML 𝓚𝓮𝔂 𝓕𝓮𝓪𝓽𝓾𝓻𝓮𝓼:
Learn how to implement genetic algorithms using Python libraries DEAP, -
Lambda values and encoder-decoder architecture in research execution
By
–
Happy to see this execution of a very interesting research direction. Question: did you try more extreme values of lamda? Could the lack of obvious trend in this (fig 3) be the result of variance? What would be its likely source? Also, did you consider using an encoder-decoder
-
Credit Assignment and Multi-Step RL in LLM Research
By
–
Credit assignment is hard. I wonder how many LLM papers use multi-step RL? Tool use is the thing that comes to mind. It would be great if someone working on this could comment. Also, how many people out there are doing multi-step RL with LLMs?
-
Scaling trends in latest AI models and their development
By
–
They seem to scale, eg o1, R1, Qwen3, MAI1 …
-
Questions on masked autoencoders, latent diffusion, and rollout loss consistency
By
–
This was very instructive. Thank you. Two questions: (1) have people replaced the masked autoencoder with latent diffusion? (2) in the rollout loss why not use multiple rollouts and consistency checks as in one of our old papers on learning awareness models