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03/14/2023
Simfluence: Modeling the Influence of Individual Training Examples by Simulating Training Runs
Training data attribution (TDA) methods offer to trace a model's predict...
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02/13/2023
Gradient-Based Automated Iterative Recovery for Parameter-Efficient Tuning
Pretrained large language models (LLMs) are able to solve a wide variety...
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02/13/2023
Towards Agile Text Classifiers for Everyone
Text-based safety classifiers are widely used for content moderation and...
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05/23/2022
Tracing Knowledge in Language Models Back to the Training Data
Neural language models (LMs) have been shown to memorize a great deal of...
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01/26/2022
IMACS: Image Model Attribution Comparison Summaries
Developing a suitable Deep Neural Network (DNN) often requires significa...
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06/17/2021
Guided Integrated Gradients: An Adaptive Path Method for Removing Noise
Integrated Gradients (IG) is a commonly used feature attribution method ...
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04/14/2021
An Interpretability Illusion for BERT
We describe an "interpretability illusion" that arises when analyzing th...
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08/12/2020
The Language Interpretability Tool: Extensible, Interactive Visualizations and Analysis for NLP Models
We present the Language Interpretability Tool (LIT), an open-source plat...
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08/07/2019
Debiasing Embeddings for Reduced Gender Bias in Text Classification
(Bolukbasi et al., 2016) demonstrated that pretrained word embeddings ca...
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07/09/2019
The What-If Tool: Interactive Probing of Machine Learning Models
A key challenge in developing and deploying Machine Learning (ML) system...
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06/06/2019
Segment Integrated Gradients: Better attributions through regions
Saliency methods can aid understanding of deep neural networks. Recent y...
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03/19/2018
Zero-Shot Detection
As we move towards large-scale object detection, it is unrealistic to ex...
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02/25/2017
Adaptive Neural Networks for Efficient Inference
We present an approach to adaptively utilize deep neural networks in ord...
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07/21/2016
Man is to Computer Programmer as Woman is to Homemaker? Debiasing Word Embeddings
The blind application of machine learning runs the risk of amplifying bi...
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06/20/2016
Quantifying and Reducing Stereotypes in Word Embeddings
Machine learning algorithms are optimized to model statistical propertie...
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02/28/2016