Alexander Ratner | DeepAI

AI Chat

Featured Co-authors

Michael I. Jordan
283 publications
Chao Zhang
239 publications
Ce Zhang
133 publications
Dawn Song
130 publications
Jing Li
126 publications
Christopher Ré
125 publications
Ali Farhadi
106 publications
Martin Jaggi
104 publications
Hannaneh Hajishirzi
102 publications
Joseph E. Gonzalez
99 publications
Yue Yu
98 publications

∙ 08/01/2023

Tool Documentation Enables Zero-Shot Tool-Usage with Large Language Models

Today, large language models (LLMs) are taught to use new tools by provi...

0 Cheng-Yu Hsieh, et al. ∙

∙ 05/03/2023

MaskSearch: Querying Image Masks at Scale

Machine learning tasks over image databases often generate masks that an...

0 Dong He, et al. ∙

∙ 05/03/2023

Distilling Step-by-Step! Outperforming Larger Language Models with Less Training Data and Smaller Model Sizes

Deploying large language models (LLMs) is challenging because they are m...

0 Cheng-Yu Hsieh, et al. ∙

∙ 04/27/2023

DataComp: In search of the next generation of multimodal datasets

Large multimodal datasets have been instrumental in recent breakthroughs...

0 Samir Yitzhak Gadre, et al. ∙

∙ 10/06/2022

Leveraging Instance Features for Label Aggregation in Programmatic Weak Supervision

Programmatic Weak Supervision (PWS) has emerged as a widespread paradigm...

0 Jieyu Zhang, et al. ∙

∙ 08/02/2022

Binary Classification with Positive Labeling Sources

To create a large amount of training labels for machine learning models ...

0 Jieyu Zhang, et al. ∙

∙ 05/25/2022

Understanding Programmatic Weak Supervision via Source-aware Influence Function

Programmatic Weak Supervision (PWS) aggregates the source votes of multi...

0 Jieyu Zhang, et al. ∙

∙ 03/02/2022

Nemo: Guiding and Contextualizing Weak Supervision for Interactive Data Programming

Weak Supervision (WS) techniques allow users to efficiently create large...

0 Cheng-Yu Hsieh, et al. ∙

∙ 02/11/2022

A Survey on Programmatic Weak Supervision

Labeling training data has become one of the major roadblocks to using m...

0 Jieyu Zhang, et al. ∙

∙ 10/07/2021

Creating Training Sets via Weak Indirect Supervision

Creating labeled training sets has become one of the major roadblocks in...

9 Jieyu Zhang, et al. ∙

∙ 09/23/2021

WRENCH: A Comprehensive Benchmark for Weak Supervision

Recent Weak Supervision (WS) approaches have had widespread success in e...

0 Jieyu Zhang, et al. ∙

∙ 09/13/2019

Slice-based Learning: A Programming Model for Residual Learning in Critical Data Slices

In real-world machine learning applications, data subsets correspond to ...

34 Vincent S. Chen, et al. ∙

∙ 03/29/2019

SysML: The New Frontier of Machine Learning Systems

Machine learning (ML) techniques are enjoying rapidly increasing adoptio...

0 Alexander Ratner, et al. ∙

∙ 03/26/2019

Cross-Modal Data Programming Enables Rapid Medical Machine Learning

Labeling training datasets has become a key barrier to building medical ...

12 Jared Dunnmon, et al. ∙

∙ 03/14/2019

Learning Dependency Structures for Weak Supervision Models

Labeling training data is a key bottleneck in the modern machine learnin...

8 Paroma Varma, et al. ∙

∙ 12/02/2018

Snorkel DryBell: A Case Study in Deploying Weak Supervision at Industrial Scale

Labeling training data is one of the most costly bottlenecks in developi...

0 Stephen H. Bach, et al. ∙

∙ 10/05/2018

Training Complex Models with Multi-Task Weak Supervision

As machine learning models continue to increase in complexity, collectin...

14 Alexander Ratner, et al. ∙

∙ 11/28/2017

Snorkel: Rapid Training Data Creation with Weak Supervision

Labeling training data is increasingly the largest bottleneck in deployi...

0 Alexander Ratner, et al. ∙

∙ 03/02/2017

Learning the Structure of Generative Models without Labeled Data

Curating labeled training data has become the primary bottleneck in mach...

0 Stephen H. Bach, et al. ∙

∙ 05/25/2016

Data Programming: Creating Large Training Sets, Quickly

Large labeled training sets are the critical building blocks of supervis...

0 Alexander Ratner, et al. ∙

Are you a researcher?
Expose your work
to one of the largest
A.I. communities in the world