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06/02/2023
Enhancing CLIP with CLIP: Exploring Pseudolabeling for Limited-Label Prompt Tuning
Fine-tuning vision-language models (VLMs) like CLIP to downstream tasks ...
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06/02/2023
An Adaptive Method for Weak Supervision with Drifting Data
We introduce an adaptive method with formal quality guarantees for weak ...
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05/29/2023
Alfred: A System for Prompted Weak Supervision
Alfred is the first system for programmatic weak supervision (PWS) that ...
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05/25/2022
Tight Lower Bounds on Worst-Case Guarantees for Zero-Shot Learning with Attributes
We develop a rigorous mathematical analysis of zero-shot learning with a...
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05/15/2022
Fairness via Explanation Quality: Evaluating Disparities in the Quality of Post hoc Explanations
As post hoc explanation methods are increasingly being leveraged to expl...
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05/04/2022
Language Models in the Loop: Incorporating Prompting into Weak Supervision
We propose a new strategy for applying large pre-trained language models...
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04/07/2022
Learning to Compose Soft Prompts for Compositional Zero-Shot Learning
We introduce compositional soft prompting (CSP), a parameter-efficient l...
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02/02/2022
PromptSource: An Integrated Development Environment and Repository for Natural Language Prompts
PromptSource is a system for creating, sharing, and using natural langua...
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11/08/2021
TAGLETS: A System for Automatic Semi-Supervised Learning with Auxiliary Data
Machine learning practitioners often have access to a spectrum of data: ...
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10/15/2021
Multitask Prompted Training Enables Zero-Shot Task Generalization
Large language models have recently been shown to attain reasonable zero...
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06/24/2021
What will it take to generate fairness-preserving explanations?
In situations where explanations of black-box models may be useful, the ...
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06/08/2021
Learning from Multiple Noisy Partial Labelers
Programmatic weak supervision creates models without hand-labeled traini...
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12/13/2020
Pseudo Shots: Few-Shot Learning with Auxiliary Data
In many practical few-shot learning problems, even though labeled exampl...
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06/18/2020
Zero-Shot Learning with Common Sense Knowledge Graphs
Zero-shot learning relies on semantic class representations such as attr...
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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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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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05/17/2015