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Canadian Institute for Advanced Research - Wikipedia
Quantum information science - Wikipedia
CIFAR-10 and CIFAR-100 datasets
Lecture14
Biological neuron model - Wikipedia
What is the difference between pre-training, fine-tuning, and instruct-tuning exactly? : r/learnmachinelearning
Moses - Main/HomePage
Emad Elsaid
[1511.08228] Neural GPUs Learn Algorithms
Ilya Sutskever - Wikipedia
A9_Lecture_Notes.pdf
The Best Charity Isn't What You Think
Manute Bol - Wikipedia
Perplexity of fixed-length models
The Transformer model family
BLEU - Wikipedia
tensorflow/tensor2tensor: Library of deep learning models and datasets designed to make deep learning more accessible and accelerate ML research.
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Chap8_PDP86.pdf
[1410.4615] Learning to Execute
[1803.08475] Attention, Learn to Solve Routing Problems!
Oriol Vinyals - Wikipedia
Downsampling (signal processing) - Wikipedia
Infinite impulse response - Wikipedia
Normal distribution - Wikipedia
The Bitter Truth About AI; Why Human Ingenuity Often Loses to Computation | Arash Khoeini
Samy Bengio - Wikipedia
Joscha Bach · Luma
Homepage - GoatAI
Westnile.ca.gov | California West Nile Virus Website
Meta-learning (computer science) - Wikipedia
Chatbot Arena (formerly LMSYS): Free AI Chat to Compare & Test Best AI Chatbots
Alternative Chemistries – Speculations and simulations about 4D chemisty.
Emergency Travel | USCIS
The problem with 'alignment' — AI • Objectives • Institute
The Fox Says | Manifund | Substack
Scoping Developmental Interpretability | Manifund
Towards Developmental Interpretability — LessWrong
Framery Phone Booth CO2 Accumulation
Support for Deep Coverage of China and AI | Manifund
davidad 🎇 on X: "If you’re working on evals for safety reasons, be aware that for labs who have ascended to the pure-RL-from-final-answer-correctness stage of the LLM game, high-quality evals are now the main bottleneck on capabilities growth." / X
AI Frontiers
What makes a good "regrant"? - by Jesse Richardson
Approximation error - Wikipedia
2309.01933
‪Jayson Lynch‬ - ‪Google Scholar‬
Winning the power to lose — LessWrong
About
Covariance matrix - Wikipedia
All time greatest eigenvectors | Raymond’s Weblog^