For many years I used the Julia programming language for transforming, cleaning, analyzing, and visualizing data, doing statistics, and performing simulations. I published a handful of open-source packages for things like signed distance fields, nearest-neighbor search, and Turing patterns (among others), made visual explanations of Julia concepts like broadcasting and arrays, and used Julia to ma
What We Learned from a Year of Building with LLMs (Part I)
It’s an exciting time to build with large language models (LLMs). Over the past year, LLMs have become “good enough” for real-world applications. The pace of improvements in LLMs, coupled with a parade of demos on social media, will fuel an estimated $200B investment in AI by 2025. LLMs are also broadly accessible, allowing everyone, not just ML engineers and scientists, to build intelligence into
GitHub - diff-usion/Awesome-Diffusion-Models: A collection of resources and papers on Diffusion Models
DiffEnc: Variational Diffusion with a Learned Encoder Beatrix M. G. Nielsen, Anders Christensen, Andrea Dittadi, Ole Winther arXiv 2023. [Paper] 30 Oct 2023 Upgrading VAE Training With Unlimited Data Plans Provided by Diffusion Models Tim Z. Xiao, Johannes Zenn, Robert Bamler arXiv 2023. [Paper] 30 Oct 2023 Successfully Applying Lottery Ticket Hypothesis to Diffusion Model Chao Jiang, Bo Hui, Boha
Solving Quantitative Reasoning Problems With Language Models
Solving Quantitative Reasoning Problems with Language Models Aitor Lewkowycz∗, Anders Andreassen†, David Dohan†, Ethan Dyer†, Henryk Michalewski†, Vinay Ramasesh†, Ambrose Slone, Cem Anil, Imanol Schlag, Theo Gutman-Solo, Yuhuai Wu, Behnam Neyshabur∗, Guy Gur-Ari∗, and Vedant Misra∗ Google Research Abstract Language models have achieved remarkable performance on a wide range of tasks that require
Happy New Year: GPT in 500 lines of SQL - EXPLAIN EXTENDED
Translations: Russian This year, the talk of the town was AI and how it can do everything for you. I like it when someone or something does everything for me. To this end, I decided to ask ChatGPT to write my New Year's post: "Hey ChatGPT. Can you implement a large language model in SQL?" "No, SQL is not suitable for implementing large language models. SQL is a language for managing and querying d
Annotated history of modern AI and deep neural networks
For a while, DanNet enjoyed a monopoly. From 2011 to 2012 it won every contest it entered, winning four of them in a row (15 May 2011, 6 Aug 2011, 1 Mar 2012, 10 Sep 2012).[GPUCNN5] In particular, at IJCNN 2011 in Silicon Valley, DanNet blew away the competition and achieved the first superhuman visual pattern recognition[DAN1] in an international contest. DanNet was also the first deep CNN to win
Generative Modeling by Estimating Gradients of the Data Distribution | Yang Song
Introduction Existing generative modeling techniques can largely be grouped into two categories based on how they represent probability distributions. likelihood-based models, which directly learn the distribution’s probability density (or mass) function via (approximate) maximum likelihood. Typical likelihood-based models include autoregressive models , normalizing flow models , energy-based mode
What We’ve Learned From A Year of Building with LLMs – Applied LLMs
A practical guide to building successful LLM products, covering the tactical, operational, and strategic. It’s an exciting time to build with large language models (LLMs). Over the past year, LLMs have become “good enough” for real-world applications. And they’re getting better and cheaper every year. Coupled with a parade of demos on social media, there will be an estimated $200B investment in AI
Ilya Sutskever’s Top 30 Reading List The First Law of Complexodynamics The Unreasonable Effectiveness of Recurrent Neural Networks Understanding LSTM Networks Recurrent Neural Network Regularization Keeping Neural Networks Simple by Minimizing the Description Length of the Weights Pointer Networks ImageNet Classification with Deep Convolutional Neural Networks Order Matters: Sequence to Sequence f
1. はじめに NeRF (Neural Radiance Field) とは、複雑なシーンに対して、任意の視点からの3次元的なシーンを画像から再構成する技術です。以下の動画にあるように、物体に対して、様々な角度から見たときのシーンをキレイに再現することができます。反射に関しても、それぞれの角度から見たときの見え方が反映されており、角度によって同じ場所でも微妙に違う反射特性を見て取ることができます。この手法を利用して、例えば、地点AとBで画像を取得した場合、その中間地点の任意の角度から対象物体を見たときのシーンを生成可能です。 この記事では、このNeRFと呼ばれる技術と、それを実行するにあたって必要な周辺の技術について簡単にまとめたいと思います。以下に示す、NeRFの論文と照らし合わせてながら解説を行います。しかし、本記事では、NeRFを実行するまでの流れを示すため、各要素技術に関しては詳
The Manhattan Project, the US program to build an atomic bomb during WWII, is one of the most famous and widely known major government projects: a survey in 1999 ranked the dropping of the atomic bomb as the top news story of the 20th century. Virtually everyone knows that the project built the bombs that were dropped on Hiroshima and Nagasaki. And most of us probably know that the bomb was built
The Principles of Deep Learning Theory An Effective Theory Approach to Understanding Neural Networks Daniel A. Roberts and Sho Yaida based on research in collaboration with Boris Hanin drob@mit.edu, shoyaida@fb.com ii Contents Preface vii 0 Initialization 1 0.1 An Effective Theory Approach . . . . . . . . . . . . . . . . . . . . . . . . 2 0.2 The Theoretical Minimum . . . . . . . . . . . . . . . .
Large Text Compression Benchmark Matt Mahoney Last update: Mar. 25, 2026. history This competition ranks lossless data compression programs by the compressed size (including the size of the decompression program) of the first 109 bytes of the XML text dump of the English version of Wikipedia on Mar. 3, 2006. About the test data. The goal of this benchmark is not to find the best overall compress
[無料公開] 「Pythonで学ぶ実験計画法入門 ベイズ最適化によるデータ解析」 の “まえがき”、目次の詳細、第１・２章
モデル Y=f(X) を用いることで、まだ実験していない実験条件の候補の値をモデルに入力し、実験の結果としての材料サンプルがもつと考えられる物性の値を推定できます。推定値が材料物性の目標値になる、もしくは近いような実験条件の候補を選択することで、次に行う実験を決められます。 実験の結果が得られたら、それが目標を達成していれば終了です。目標を達成していなかったら、実験条件の候補と実験結果をあわせたものをデータベースに追加して、再度モデルを構築します。新たに構築されたモデルを用いることで、次は別の実験条件の候補が選択されます。このように、モデル構築と次の実験の提案を繰り返すことを適応的実験計画法と呼び、詳細は2.3節で解説します。 1.4 なぜベイズ最適化が必要か これまで、Y の推定値が目標値に近いような X の候補を次の実験条件の候補として選択する、といった説明をしていました。分子設計でも
![[無料公開] 「Pythonで学ぶ実験計画法入門 ベイズ最適化によるデータ解析」 の “まえがき”、目次の詳細、第１・２章](https://cdn-ak-scissors.b.st-hatena.com/image/square/cb27d830586f2c7f82c5aef341090553813af156/backend=imagemagick;height=288;version=1;width=512/https%3A%2F%2Fdatachemeng.com%2Fwp-content%2Fuploads%2F210606python.jpg)
When we cluster things, we put them into groups. In Machine Learning, Clustering is the process of dividing data points into particular groups. One group will have similar data points and differentiate from those with other data points. It is purely based on the patterns, relationships, and correlations in the data. Clustering is a form of Unsupervised Learning. Let’s quickly recap the definition
tigerbeetle/docs/TIGER_STYLE.md at main · tigerbeetle/tigerbeetle
What could go wrong? What's wrong? Which question would we rather ask? The former, because code, like steel, is less expensive to change while it's hot. A problem solved in production is many times more expensive than a problem solved in implementation, or a problem solved in design. Since it's hard enough to discover showstoppers, when we do find them, we solve them. We don't allow potential memc
Stan supports a direct encoding of reparameterizations. Stan also supports changes of variables by directly incrementing the log probability accumulator with the log Jacobian of the transform. Theoretical and practical background A Bayesian posterior is technically a probability measure, which is a parameterization-invariant, abstract mathematical object.1 Stan’s modeling language, on the other ha
111 A Comprehensive Survey of AI-Generated Content (AIGC): A History of Generative AI from GAN to ChatGPT YIHAN CAO∗, Lehigh University & Carnegie Mellon University, USA SIYU LI, Lehigh University, USA YIXIN LIU, Lehigh University, USA ZHILING YAN, Lehigh University, USA YUTONG DAI, Lehigh University, USA PHILIP S. YU, University of Illinois at Chicago, USA LICHAO SUN, Lehigh University, USA Recen
The Little Book of Deep Learning
The Little Book of Deep Learning François Fleuret François Fleuret is a professor of computer sci- ence at the University of Geneva, Switzerland. The cover illustration is a schematic of the Neocognitron by Fukushima [1980], a key an- cestor of deep neural networks. This ebook is formatted to fit on a phone screen. Contents Contents 5 List of figures 7 Foreword 8 I Foundations 10 1 Machine Learnin
Graph ML in 2022: Where Are We Now? | Towards Data Science
Thoughts and Theory, State of the Art Digest It’s been quite a year for Graph ML – thousands of papers, numerous conferences and workshops… How do we catch up with so many cool things happening around? Well, we are puzzled as well and decided to present a structured look at Graph ML highlighting 🔥 trends and major advancements. The image was generated by ruDALL-E with a prompt "graphs floating in
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Why I no longer recommend Julia
Aman's AI Journal • Primers • Ilya Sutskever's Top 30
NeRFの仕組みを1からわかりやすくまとめたい - kentaPtの日記
An Engineering History of the Manhattan Project
https://deeplearningtheory.com/PDLT.pdf
Large Text Compression Benchmark
A Guide to Clustering in Machine Learning
Reparameterization and Change of Variables
A Comprehensive Survey of AI-Generated Content (AIGC): A History of Generative AI from GAN to ChatGPT