Subspace Recovery from Heterogeneous Data with Non-isotropic Noise

by AI Generated Robotic ContentFAANGPosted on Comments are Disabled

*= Equal Contributions Recovering linear subspaces from data is a fundamental and important task in statistics and machine learning. Motivated by heterogeneity in Federated Learning settings, we study a basic formulation of this problem: the principal component analysis (PCA), with a focus on dealing with irregular noise. Our data come from users with user contributing …

Read more “Subspace Recovery from Heterogeneous Data with Non-isotropic Noise”

image2

Characterizing Emergent Phenomena in Large Language Models

by AI Generated Robotic ContentFAANGPosted on Comments are Disabled

Posted by Jason Wei and Yi Tay, Research Scientists, Google Research, Brain Team The field of natural language processing (NLP) has been revolutionized by language models trained on large amounts of text data. Scaling up the size of language models often leads to improved performance and sample efficiency on a range of downstream NLP tasks. …

Read more “Characterizing Emergent Phenomena in Large Language Models”

prodegeblog1

How Prodege saved $1.5 million in annual human review costs using low-code computer vision AI

by AI Generated Robotic ContentFAANGPosted on Comments are Disabled

This post was co-authored by Arun Gupta, the Director of Business Intelligence at Prodege, LLC. Prodege is a data-driven marketing and consumer insights platform comprised of consumer brands—Swagbucks, MyPoints, Tada, ySense, InboxDollars, InboxPounds, DailyRewards, PollFish, and Upromise—along with a complementary suite of business solutions for marketers and researchers. Prodege has 120 million users and has …

Read more “How Prodege saved $1.5 million in annual human review costs using low-code computer vision AI”

ML 11592 image001

Identifying and avoiding common data issues while building no code ML models with Amazon SageMaker Canvas

by AI Generated Robotic ContentFAANGPosted on Comments are Disabled

Business analysts work with data and like to analyze, explore, and understand data to achieve effective business outcomes. To address business problems, they often rely on machine learning (ML) practitioners such as data scientists to assist with techniques such as utilizing ML to build models using existing data and generate predictions. However, it isn’t always …

Read more “Identifying and avoiding common data issues while building no code ML models with Amazon SageMaker Canvas”