This is what you all sound like when hyping up AI

What are some foundational ML/AI books? I'd like to invest in my fundamentals more. Ideally something students might read during their PhD.

Prawicowe fryzury.

We've barely scratched the surface of the space of deep learning architectures. It's a high dimensional space, so the volume is almost entirely contained in the surface. But we've scratched a tiny subset of the surface.

Multi-modal #LLMs understand a lot about humans. But do they understand our 3D pose? We train #PoseGPT to estimate, generate, and reason about 3D human pose (#SMPL) in images and text. This is the first true foundation model for understanding 3D humans. yfeng95.github.io/posegpt/

LOL in Polish all of these are correct order.

Tomas Mikolov, the OG and inventor of word2vec, gives this thoughts on the test of time award, and the current state of NLP, and chatGPT. 🍿

Exactly.

Have you ever done a dense grid search over neural network hyperparameters? Like a *really dense* grid search? It looks like this (!!). Blueish colors correspond to hyperparameters for which training converges, redish colors to hyperparameters for which training diverges.

Jeśli te nowe polskie modele językowe nie będą dawały takich odpowiedzi, to będę srodze rozczarowany

research.colfax-intl.com/tutorial-matri… If you learn CUDA, you probably have read the legendary matrix transpose tutorial by Mark Harris. 📚Today, I present to you a CUDA tutorial by friends at Colfax International and myself. We explore many CUDA memory concepts like in Mark's tutorial, and more.

Yet another opportunity to point out that reasoning abilities and common sense should not be confused with an ability to store and approximately retrieve many facts.

When I discussed quitting Google to do a Phd, my manager, Steve Cheng, gave me the advice of "6 shots": Doing something meaningful usually takes about 5 years and we are productive for roughly 30 years. That gives you 6 attempts. So pick each one carefully and give it your best.

Deep Learning Optimizers from First Principles Now with more maths! In this thread, I'll discuss: 1. The difference between 1st order gradient dualizaton approaches and 2nd order optimization approaches. 2. Preconditioning--how to do it and why. 3. How to derive a couple of

The (true) story of development and inspiration behind the "attention" operator, the one in "Attention is All you Need" that introduced the Transformer. From personal email correspondence with the author 🇺🇦 Dzmitry Bahdanau @ NeurIPS ~2 years ago, published here and now (with permission) following

CS224N dropped on YT (2024 spring) full 18 lecture series by none other than Christopher Manning

At last.

SIU3R: Simultaneous Scene Understanding and 3D Reconstruction Beyond Feature Alignment Qi Xu, Dongxu Wei, Lingzhe Zhao, Wenpu Li (李文朴), Zhangchi Huang, Shunping Ji, Peidong Liu tl;dr: reconstruction and understanding->pixel-aligned 2D-to-3D lifting->unified learnable queries

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