GCLR Talk by Ines Chami

GCLR Talk by Ines Chami

SLT Insights - Underperformance and underachievementПодробнее

SLT Insights - Underperformance and underachievement

Numbers Station Founders on Applying Foundation Models to Data WranglingПодробнее

Numbers Station Founders on Applying Foundation Models to Data Wrangling

GELEX: Generative AI-Hybrid System for Example-Based LearningПодробнее

GELEX: Generative AI-Hybrid System for Example-Based Learning

GCLR Panel DiscussionПодробнее

GCLR Panel Discussion

LLM Avalanche: Panel: Enteprise & DeploymentПодробнее

LLM Avalanche: Panel: Enteprise & Deployment

GraphEDM A Unified Framework for Machine Learning on Graphs | Ines Chami | Knowledge Connexions 2020Подробнее

GraphEDM A Unified Framework for Machine Learning on Graphs | Ines Chami | Knowledge Connexions 2020

😘😘😍Подробнее

😘😘😍

Foundation Models in the Modern Data Stack // Ines Chami // LLMs in Prod Conference Part 2Подробнее

Foundation Models in the Modern Data Stack // Ines Chami // LLMs in Prod Conference Part 2

Ines Montani Keynote - Applied NLP ThinkingПодробнее

Ines Montani Keynote - Applied NLP Thinking

Launchable: Foundation Models - Building ApplicationsПодробнее

Launchable: Foundation Models - Building Applications

CoRL 2020, Spotlight Talk 25: Augmenting GAIL with BC for sample efficient imitation learningПодробнее

CoRL 2020, Spotlight Talk 25: Augmenting GAIL with BC for sample efficient imitation learning

Scientist Stories: Martina Cornel, Gene editing and NIPTПодробнее

Scientist Stories: Martina Cornel, Gene editing and NIPT

Session 1: Gender-smart climate and disaster risk investing and grant makingПодробнее

Session 1: Gender-smart climate and disaster risk investing and grant making

IIR 2021 - Day 2Подробнее

IIR 2021 - Day 2

Generative AI in Creative Practice: ML-Artist Folk Theories of T2I Use, Harm, and Harm-ReductionПодробнее

Generative AI in Creative Practice: ML-Artist Folk Theories of T2I Use, Harm, and Harm-Reduction

Working with audio sounds easier than it is: a deep learning perspective by Agrin HilmkilПодробнее

Working with audio sounds easier than it is: a deep learning perspective by Agrin Hilmkil

Sarah M. Preum on "In-Context Learning for Human-AI collaboration: Methods and Measures"Подробнее

Sarah M. Preum on 'In-Context Learning for Human-AI collaboration: Methods and Measures'