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Patrick McClure

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3 papers

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TMLR Journal 2026 Journal Article

Overcoming Open-Set Approaches to Adversarial Defense

Edgar Wilfred Jatho
Armon Barton
Matthew Wright
Patrick McClure

Machine learning (ML) models are increasingly proposed to replace or augment safety-critical information processing systems, yet their fragility to evasion attacks remains a well-documented, open problem. This work analyzes a class of deep neural network defenses that add a none-of-the-above (NOTA) class as an open-set-inspired, closed-set adversarial defense. We analyze seven prominent adversarial evasion attacks developed for computer vision classification and one attack developed for natural language processing classification, identifying how these attacks fail in the presence of a NOTA defense. We use this knowledge to adapt these attacks and provide empirical evidence that adding a NOTA class alone does not solve the core challenge of defending DNNs against evasion attacks. We release our adapted attack suite to enable more rigorous future evaluations of open-set-inspired defenses.

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NeurIPS Conference 2022 Conference Paper

VICE: Variational Interpretable Concept Embeddings

Lukas Muttenthaler
Charles Y. Zheng
Patrick McClure
Robert A. Vandermeulen
Martin N Hebart
Francisco Pereira

A central goal in the cognitive sciences is the development of numerical models for mental representations of object concepts. This paper introduces Variational Interpretable Concept Embeddings (VICE), an approximate Bayesian method for embedding object concepts in a vector space using data collected from humans in a triplet odd-one-out task. VICE uses variational inference to obtain sparse, non-negative representations of object concepts with uncertainty estimates for the embedding values. These estimates are used to automatically select the dimensions that best explain the data. We derive a PAC learning bound for VICE that can be used to estimate generalization performance or determine a sufficient sample size for experimental design. VICE rivals or outperforms its predecessor, SPoSE, at predicting human behavior in the triplet odd-one-out task. Furthermore, VICE's object representations are more reproducible and consistent across random initializations, highlighting the unique advantage of using VICE for deriving interpretable embeddings from human behavior.

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NeurIPS Conference 2018 Conference Paper

Distributed Weight Consolidation: A Brain Segmentation Case Study

Patrick McClure
Charles Zheng
Jakub Kaczmarzyk
John Rogers-Lee
Satra Ghosh
Dylan Nielson
Peter Bandettini
Francisco Pereira

Collecting the large datasets needed to train deep neural networks can be very difficult, particularly for the many applications for which sharing and pooling data is complicated by practical, ethical, or legal concerns. However, it may be the case that derivative datasets or predictive models developed within individual sites can be shared and combined with fewer restrictions. Training on distributed data and combining the resulting networks is often viewed as continual learning, but these methods require networks to be trained sequentially. In this paper, we introduce distributed weight consolidation (DWC), a continual learning method to consolidate the weights of separate neural networks, each trained on an independent dataset. We evaluated DWC with a brain segmentation case study, where we consolidated dilated convolutional neural networks trained on independent structural magnetic resonance imaging (sMRI) datasets from different sites. We found that DWC led to increased performance on test sets from the different sites, while maintaining generalization performance for a very large and completely independent multi-site dataset, compared to an ensemble baseline.

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