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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...
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05/03/2023
MaskSearch: Querying Image Masks at Scale
Machine learning tasks over image databases often generate masks that an...
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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...
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04/27/2023
DataComp: In search of the next generation of multimodal datasets
Large multimodal datasets have been instrumental in recent breakthroughs...
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10/06/2022
Leveraging Instance Features for Label Aggregation in Programmatic Weak Supervision
Programmatic Weak Supervision (PWS) has emerged as a widespread paradigm...
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08/02/2022
Binary Classification with Positive Labeling Sources
To create a large amount of training labels for machine learning models ...
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05/25/2022
Understanding Programmatic Weak Supervision via Source-aware Influence Function
Programmatic Weak Supervision (PWS) aggregates the source votes of multi...
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03/02/2022
Nemo: Guiding and Contextualizing Weak Supervision for Interactive Data Programming
Weak Supervision (WS) techniques allow users to efficiently create large...
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02/11/2022
A Survey on Programmatic Weak Supervision
Labeling training data has become one of the major roadblocks to using m...
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10/07/2021
Creating Training Sets via Weak Indirect Supervision
Creating labeled training sets has become one of the major roadblocks in...
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09/23/2021
WRENCH: A Comprehensive Benchmark for Weak Supervision
Recent Weak Supervision (WS) approaches have had widespread success in e...
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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 ...
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03/29/2019
SysML: The New Frontier of Machine Learning Systems
Machine learning (ML) techniques are enjoying rapidly increasing adoptio...
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03/26/2019
Cross-Modal Data Programming Enables Rapid Medical Machine Learning
Labeling training datasets has become a key barrier to building medical ...
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03/14/2019
Learning Dependency Structures for Weak Supervision Models
Labeling training data is a key bottleneck in the modern machine learnin...
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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...
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10/05/2018
Training Complex Models with Multi-Task Weak Supervision
As machine learning models continue to increase in complexity, collectin...
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11/28/2017
Snorkel: Rapid Training Data Creation with Weak Supervision
Labeling training data is increasingly the largest bottleneck in deployi...
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03/02/2017
Learning the Structure of Generative Models without Labeled Data
Curating labeled training data has become the primary bottleneck in mach...
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05/25/2016
