A New Framework for How the Brain Compresses Our Noisy World
(翻译)大脑压缩嘈杂世界的新框架
An updated view of categorization reflects the modern understanding that the nervous system is more of a prediction engine than a filing cabinet.

(翻译)大脑压缩嘈杂世界的新框架
An updated view of categorization reflects the modern understanding that the nervous system is more of a prediction engine than a filing cabinet.

(翻译)不平衡数学中的“巨大突破”
For the first time in 30 years, computer scientists have found a better way to allocate objects evenly between two groups.

作者丨 张璐 编辑丨 岑峰 幸丽娟 &n

作者丨 幸丽娟 编辑丨岑 峰  

作者丨 幸丽娟 编辑丨岑 峰  

作者丨 幸丽娟 编辑丨岑 峰  

(翻译)将手语AI交到用户手中
Introducing sign-language-to-text (SL2T), our breakthrough model powering new sign language features for Deaf and hard of hearing users.
(翻译)WeatherNext:AI模型在预测气旋方面取得突破
(翻译)在 SageMaker HyperPod 上推出新的 Ray 功能
Amazon SageMaker HyperPod now offers managed Ray support on Amazon EKS. Create and monitor Ray clusters, connect JupyterLab and Code Editor notebooks to live clusters, get out-of-the-box observability, and run resilient distributed training and accelerated inference from SageMaker Studio, all with o
(翻译)AI 驱动的元数据修正与协调
Metadata harmonization (standardizing labels, identifiers, and formats so datasets can work together) is still largely manual. This post shows how AI-powered metadata correction works in practice, covering two approaches, human-in-the-loop validation and autonomous agent-driven workflows, plus gover
(翻译)GPU加速的大规模金融工具聚类
Use AdaptGrow, a GPU-accelerated matrix factorization algorithm, to turn rolling correlation and tail-dependence matrices into hard clusters…

(翻译)生成式推荐器如何重新定义大规模推荐系统
Recommender systems (RecSys) are one of the most ubiquitous machine learning problems in the consumer internet industry yet notoriously difficult to train and…

(翻译)我们通过复现 2,200 篇 ICML 论文学到了什么
We’re on a journey to advance and democratize artificial intelligence through open source and open science.
(翻译)使用Snowflake、Amazon SageMaker Canvas和Amazon Quick构建无代码机器学习工作流——第1部分:设置Snowflake环境
Healthcare, retail, and life sciences teams store large volumes of operational data in Snowflake, but turning it into predictions is hard. In Part 1 of this series, you set up your AWS account and Snowflake environment for a no-code ML workflow with Amazon SageMaker Canvas, laying the foundation for
(翻译)OlmoEarth 嵌入介绍:从 OlmoEarth Studio 导出自定义嵌入用于下游分析
A Blog post by Ai2 on Hugging Face
