Today, we’re announcing a major breakthrough that marks a significant step forward in the world of quantum computing. For the first time in history, our teams at @GoogleQuantumAI demonstrated that a quantum computer can successfully run a verifiable algorithm, 13,000x faster than

If you're physicist or a pure mathematician, you will find out that learning about homology and cohomology is of vital importance, therefore I will suggest to you this wonderful primer recently produced by Jakob Scholbach. 🔗👇👇👇

Ever since I made a video about Fourier Transforms, one of the most requested topics on the channel has been its close cousin, the Laplace Transform. I've been having a lot of fun animating a mini-series about this topic, and the main part is now out. https://t.co/oKawJAGyx1

I can't* fathom why the top picture, and not the bottom picture, is the standard diagram for an autoencoder. The whole idea of an autoencoder is that you complete a round trip and seek cycle consistency—why lay out the network linearly?

This course will be taught in English. Students from developing countries around the world are encouraged to apply!

My course lecture on Diffusion Models from statistical first principles: https://t.co/vqP5xJkb8r The PyTorch notebooks that implement Diffusion Models from scratch w/ Transformer and UNet: https://t.co/TfD8CZXYUz

Tutorial on Diffusion Models for Imaging and Vision has been published. Official copy: https://t.co/KHUNbpTQEX Free arXiv version: https://t.co/fWv111nG5M

Even though Newton's laws are deterministic, the behavior of many interacting bodies is so chaotic that it looks essentially "random." Statistical mechanics effectively says: why bother with all those complex trajectories? Just go ahead and replace them with truly random motion.

Around ten years ago, I started studying inverse problems in Topological Data Analysis (TDA). For decades people in computational geometry have used tools like Reeb graphs and persistent homology to summarize shape in data, but how lossy is this process? A thread... 1/n

Datamapplot 0.4 is out now, and has far more powerful and effective interactive plots. Here is an example of a Data Map of 2.4 million papers on ArXiv, ready to be explored.

Year 2024, a certain university. #xlstm #math

Our paper "Regression Trees for Fast and Adaptive Prediction Intervals," co-authored with @kuben45, @mpotto1 and @rbstern, is now published in Information Sciences! 🎉 We introduce Locart and Loforest to calibrate prediction intervals for regression with coverage guarantees. +

1/12 Have you ever wondered how ML systems (and brains) deal with uncertainty? In this thread, we'll explore foundational concepts in probability, such as entropy, cross-entropy, and KL-divergence. This summarizes my latest video: https://t.co/X9LZEjBhDb Here's the breakdown of

Introduction to Markov Chain Monte Carlo (MCMC) Methods: https://t.co/F8jiCQpnq4 ➕ Detailed example (with code) of MCMC Simulation: https://t.co/TmxmqBhL1U ➕ A book: https://t.co/lBOT21rpJU

Perhaps the clearest and simplest explanation of entropy that I have seen, must watch! The Key Equation Behind Probability https://t.co/OQbVRy6HlI a través de @YouTube

For anyone curious about nonlinear dynamics and chaos, including students taking a first course in the subject: My lectures are freely available here.

Deep learning has been successfully used to solve some of the hardest problems in many-body quantum physics, since 2016. Here is a non-exhaustive list: 1. 2D Hubbard model (Fermions https://t.co/FliOsUg2Rc, bosons https://t.co/qpMJjSddKK) 2. Out-of-equilibrium Stat Mech

YouTube video is now restored. As I predicted, AGI didn't arrive before Aug 20. PS: Part 2.2 paper is in its very final processing stage; we are one man down (due to a layoff) so please forgive us for just a little more time.

Excited to see experimental realization of the 4D toric code. It may not take long that our recent discovery of 5D non-Abelian self-correcting memory can also be implemented in the lab: https://t.co/F9STfdoRlo