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09/06/2023
Certifying LLM Safety against Adversarial Prompting
Large language models (LLMs) released for public use incorporate guardra...
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07/27/2023
Verifiable Feature Attributions: A Bridge between Post Hoc Explainability and Inherent Interpretability
With the increased deployment of machine learning models in various real...
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07/26/2023
Efficient Estimation of the Local Robustness of Machine Learning Models
Machine learning models often need to be robust to noisy input data. The...
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06/11/2023
On Minimizing the Impact of Dataset Shifts on Actionable Explanations
The Right to Explanation is an important regulatory principle that allow...
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06/09/2023
Consistent Explanations in the Face of Model Indeterminacy via Ensembling
This work addresses the challenge of providing consistent explanations f...
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05/30/2023
Which Models have Perceptually-Aligned Gradients? An Explanation via Off-Manifold Robustness
One of the remarkable properties of robust computer vision models is tha...
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06/14/2022
Flatten the Curve: Efficiently Training Low-Curvature Neural Networks
The highly non-linear nature of deep neural networks causes them to be s...
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06/02/2022
Which Explanation Should I Choose? A Function Approximation Perspective to Characterizing Post hoc Explanations
Despite the plethora of post hoc model explanation methods, the basic pr...
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03/09/2022
Data-Efficient Structured Pruning via Submodular Optimization
Structured pruning is an effective approach for compressing large pre-tr...
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02/02/2022
Cyclical Pruning for Sparse Neural Networks
Current methods for pruning neural network weights iteratively apply mag...
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06/16/2020
Gradient Alignment in Deep Neural Networks
One cornerstone of interpretable deep learning is the high degree of vis...
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05/02/2019
Full-Jacobian Representation of Neural Networks
Non-linear functions such as neural networks can be locally approximated...
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03/01/2018
Knowledge Transfer with Jacobian Matching
Classical distillation methods transfer representations from a "teacher"...
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07/21/2017
Confidence estimation in Deep Neural networks via density modelling
State-of-the-art Deep Neural Networks can be easily fooled into providin...
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11/21/2016
Generalized Dropout
Deep Neural Networks often require good regularizers to generalize well....
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11/21/2016
Training Sparse Neural Networks
Deep neural networks with lots of parameters are typically used for larg...
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11/17/2016
Compensating for Large In-Plane Rotations in Natural Images
Rotation invariance has been studied in the computer vision community pr...
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01/25/2016
A Taxonomy of Deep Convolutional Neural Nets for Computer Vision
Traditional architectures for solving computer vision problems and the d...
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11/17/2015
Learning Neural Network Architectures using Backpropagation
Deep neural networks with millions of parameters are at the heart of man...
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07/22/2015
