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This repository contains an efficient implementation of Kolmogorov-Arnold Network (KAN). The original implementation of KAN is available here. The performance issue of the original implementation is mostly because it needs to expand all intermediate variables to perform the different activation functions. For a layer with in_features input and out_features output, the original implementation needs
While helping develop new features for JavaScript, I've found that one of the most useful methods of finding what works and what doesn't is being able to actually run code using the new feature. Babel is fantastic for this, but sometimes features just can't be nicely represented with it. Similarly, implementing a feature in one of the engines is a large undertaking, involving long compile times an
TLDR: Here is the code to explore. It all started during my hobby research on various distributed schedulers and distributed computing frameworks. Naturally, Spark came under the bracket. I was already somewhat familiar with Spark internals since I have been using it for over 3 years. It struck me then that one of the primary reasons why it became hugely successful is not just because of its speed
What is virtual scrolling and why do we need it? Imagine you have a dataset of 100,000 or more items you want to display as a scrollable list without pagination. Rendering that many rows would pollute the DOM, consume too much memory, and degrade the app’s performance. Instead, you want to show the user only a small portion of data at a given time. Other items should be emulated (virtualized) via
Implementation of Imagen, Google's Text-to-Image Neural Network that beats DALL-E2, in Pytorch. It is the new SOTA for text-to-image synthesis. Architecturally, it is actually much simpler than DALL-E2. It consists of a cascading DDPM conditioned on text embeddings from a large pretrained T5 model (attention network). It also contains dynamic clipping for improved classifier free guidance, noise l
Cloud-native applications have evolved into a standardized architecture consisting of multiple loosely coupled components called microservices (often typically implemented as containers) that are supported by an infrastructure for providing application services, such as service mesh. Both of these components are usually hosted on a container orchestration and resource management platform. In this
events { worker_connections 128; } http { lua_package_path '~/lua/?.lua;;'; resolver 8.8.8.8; lua_ssl_trusted_certificate /etc/ssl/certs/ca-certificates.crt; lua_ssl_verify_depth 5; # cache for discovery metadata documents lua_shared_dict discovery 1m; # cache for JWKs lua_shared_dict jwks 1m; # NB: if you have "lua_code_cache off;", use: # set $session_secret xxxxxxxxxxxxxxxxxxx; # see: https://g
Here at Microsoft, we are always looking to engage with open source communities to produce better solutions for the community and our customers. One of the more useful debugging advances that have arrived in the last decade is DTrace. DTrace of course needs no introduction: it's a dynamic tracing framework that allows an admin or developer to get a real-time look into a system either in user or ke
In order to be able to parse text documents correctly, some baseline syntax had to be established. When it comes to Json itself, it may look as you like, no restrictions here. Though, when it comes to SQL, it needs to abide to certain rules: Fields should always be surrounded by double quotes: "example.field". String constants should always be surrounded by single quotes: 'I\'m a string constant!'
New Filesharing Implementation in Docker Desktop Windows Improves Developer Inner Loop UX A common developer workflow when using frameworks like Symfony or React is to edit the source code using a Windows IDE while running the app itself in a Docker container. The source is shared between the host and the container with a command like the following: $ docker run -v C:\Users\me:/code -p 8080:8080 m
Full code and simulated dataset are posted on my Github repo: https://github.com/sibylhe/mmm_stan The methodology of this project is based on this paper by Google, but is applied to a more complicated, real-world setting, where 1) there are 13 media channels and 46 control variables; 2) models are built in a stacked way. 1. IntroductionMarketing Mix Model, or Media Mix Model (MMM) is used by adver
Gemma is a family of lightweight, state-of-the art open models built from research and technology used to create Google Gemini models. They are text-to-text, decoder-only large language models, available in English, with open weights, pre-trained variants, and instruction-tuned variants. For more details, please check out the following links: Gemma on Google AI Gemma on Kaggle Gemma on Vertex AI M
Regular gRPC has a lot going for it but is awkward to use in some environments. gRPC-Web makes it easy to get gRPC working in environments that need HTTP/1.1 but the Google gRPC and gRPC-Web implementations don't like to coexist with your normal Python frameworks like Django or Flask. Sonora doesn't care what ioloop you use, this means you can run it along side any other Python web framework in th
書籍 配布ソース 取り組み方 章ごとの要点をまとめる インターフェースとテストコードから実装をイメージする 実装を読み解いてコメントを書きながら写経する Suggested Reading は気になる資料のリストアップまで Exercises はどこまでやるか悩ましい。 割とボリューミーなので、1周目は読むだけに留めて、2周目で取り組むくらいの気持ちで。 全体に対するメモ 実装パートは事前に解説した内容まで踏み込まない Exerciseパートで、追加で取り組む課題として設定されてたりする 扱える値 string, integer buffer replacement clock まで解説 & 実装はナイーブなもの recovery 記録する log の種類は、ファイル末尾への block の追加まで解説 & 実装するのは以下 CHECKPOINT (transactionの) START,
Alias-Free Generative Adversarial Networks Tero Karras, Miika Aittala, Samuli Laine, Erik Härkönen, Janne Hellsten, Jaakko Lehtinen, Timo Aila https://nvlabs.github.io/stylegan3 Abstract: We observe that despite their hierarchical convolutional nature, the synthesis process of typical generative adversarial networks depends on absolute pixel coordinates in an unhealthy manner. This manifests itsel
This article assumes that you have a working knowledge of how JSON Web Tokens are both used and represented. If you would like to learn more about JSON Web Tokens, you can check out this article by Auth0: An Introduction to JSON Web Tokens. IntroductionJSON Web Tokens (JWTs) are great for representing a user's authentication / authorization state without needing to maintain a server-side session s
Training Generative Adversarial Networks with Limited Data Tero Karras, Miika Aittala, Janne Hellsten, Samuli Laine, Jaakko Lehtinen, Timo Aila https://arxiv.org/abs/2006.06676 Abstract: Training generative adversarial networks (GAN) using too little data typically leads to discriminator overfitting, causing training to diverge. We propose an adaptive discriminator augmentation mechanism that sign
Constant time implementation of the LFU cache eviction algorithm A common strategy to make any system super-performant is Caching. Almost all software products, operating at scale, have multiple layers of caches in their architectures. Caching, when done right, does wonder to the response time and is one of the main reasons why products work so well at a massive scale. Cache engines are limited by
[Paper] [Citations] [Clip Colab] [Coca Colab] Welcome to an open source implementation of OpenAI's CLIP (Contrastive Language-Image Pre-training). Using this codebase, we have trained several models on a variety of data sources and compute budgets, ranging from small-scale experiments to larger runs including models trained on datasets such as LAION-400M, LAION-2B and DataComp-1B. Many of our mode
AMD Quietly Funded A Drop-In CUDA Implementation Built On ROCm: It's Now Open-Source Written by Michael Larabel in Display Drivers on 12 February 2024 at 09:00 AM EST. Page 1 of 4. 153 Comments. While there have been efforts by AMD over the years to make it easier to port codebases targeting NVIDIA's CUDA API to run atop HIP/ROCm, it still requires work on the part of developers. The tooling has i
hnsw-rust is a Rust implementation of The Hierarchical Navigable Small World (HNSW) algorithm. HNSW is a notable advancement in Approximate Nearest Neighbor (ANN) search in high-dimensional spaces, fundamentally altering our approach to these problems. The algorithm constructs a layered graph structure, where higher layers (less dense) are used for rapid global navigation, while lower layers (more
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