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06/28/2022
On the amplification of security and privacy risks by post-hoc explanations in machine learning models
A variety of explanation methods have been proposed in recent years to h...
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07/22/2020
IBM Federated Learning: an Enterprise Framework White Paper V0.1
Federated Learning (FL) is an approach to conduct machine learning witho...
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03/31/2020
Explaining Motion Relevance for Activity Recognition in Video Deep Learning Models
A small subset of explainability techniques developed initially for imag...
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03/18/2020
Improving Adversarial Robustness Through Progressive Hardening
Adversarial training (AT) has become a popular choice for training robus...
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11/29/2019
Sanity Checks for Saliency Metrics
Saliency maps are a popular approach to creating post-hoc explanations o...
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11/29/2018
Analyzing Federated Learning through an Adversarial Lens
Federated learning distributes model training among a multitude of agent...
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09/29/2018
Stakeholders in Explainable AI
There is general consensus that it is important for artificial intellige...
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06/20/2018
Interpretable to Whom? A Role-based Model for Analyzing Interpretable Machine Learning Systems
Several researchers have argued that a machine learning system's interpr...
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05/28/2018
GenAttack: Practical Black-box Attacks with Gradient-Free Optimization
Deep neural networks (DNNs) are vulnerable to adversarial examples, even...
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01/31/2017
SenseGen: A Deep Learning Architecture for Synthetic Sensor Data Generation
Our ability to synthesize sensory data that preserves specific statistic...
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12/09/2015
