68 citations · 159 across the 12 of their papers we have counts for
14 papers
LatentDiff: Scaling Semantic Dataset Comparison to Millions of Images
James Flora, Kowshik Thopalli, Akshay R. Kulkarni +2
We present LatentDiff, a scalable framework for semantic dataset comparison that operates directly in the latent space of pretrained vision encoders. By combining sparse autoencode…
Biharmonic Distance of Graphs and its Higher-Order Variants: Theoretical Properties with Applications to Centrality and Clustering
Mitchell Black, Lucy Lin, Amir Nayyeri +1
Effective resistance is a distance between vertices of a graph that is both theoretically interesting and useful in applications. We study a variant of effective resistance called…
Transformer-Powered Surrogates Close the ICF Simulation-Experiment Gap with Extremely Limited Data
Matthew L. Olson, Shusen Liu, Jayaraman J. Thiagarajan +3
Recent advances in machine learning, specifically transformer architecture, have led to significant advancements in commercial domains. These powerful models have demonstrated supe…
HiNoVa: A Novel Open-Set Detection Method for Automating RF Device Authentication
Luke Puppo, Weng-Keen Wong, Bechir Hamdaoui +1
New capabilities in wireless network security have been enabled by deep learning, which leverages patterns in radio frequency (RF) data to identify and authenticate devices. Open-s…
Cross-GAN Auditing: Unsupervised Identification of Attribute Level Similarities and Differences between Pretrained Generative Models
Matthew L. Olson, Shusen Liu, Rushil Anirudh +3
Generative Adversarial Networks (GANs) are notoriously difficult to train especially for complex distributions and with limited data. This has driven the need for tools to audit tr…
ADL-ID: Adversarial Disentanglement Learning for Wireless Device Fingerprinting Temporal Domain Adaptation
Abdurrahman Elmaghbub, Bechir Hamdaoui, Weng-Keen Wong
As the journey of 5G standardization is coming to an end, academia and industry have already begun to consider the sixth-generation (6G) wireless networks, with an aim to meet the…
Tweak: Towards Portable Deep Learning Models for Domain-Agnostic LoRa Device Authentication
Jared Gaskin, Bechir Hamdaoui, Weng-Keen Wong
Deep learning based device fingerprinting has emerged as a key method of identifying and authenticating devices solely via their captured RF transmissions. Conventional approaches…
An Analysis of Complex-Valued CNNs for RF Data-Driven Wireless Device Classification
Jun Chen, Weng-Keen Wong, Bechir Hamdaoui +4
Recent deep neural network-based device classification studies show that complex-valued neural networks (CVNNs) yield higher classification accuracy than real-valued neural network…
Contrastive Identification of Covariate Shift in Image Data
Matthew L. Olson, Thuy-Vy Nguyen, Gaurav Dixit +3
Identifying covariate shift is crucial for making machine learning systems robust in the real world and for detecting training data biases that are not reflected in test data. Howe…
Counterfactual State Explanations for Reinforcement Learning Agents via Generative Deep Learning
Matthew L. Olson, Roli Khanna, Lawrence Neal +2
Counterfactual explanations, which deal with "why not?" scenarios, can provide insightful explanations to an AI agent's behavior. In this work, we focus on generating counterfactua…
Counterfactual States for Atari Agents via Generative Deep Learning
Matthew L. Olson, Lawrence Neal, Fuxin Li +1
Although deep reinforcement learning agents have produced impressive results in many domains, their decision making is difficult to explain to humans. To address this problem, past…
Incorporating Feedback into Tree-based Anomaly Detection
Shubhomoy Das, Weng-Keen Wong, Alan Fern +2
Anomaly detectors are often used to produce a ranked list of statistical anomalies, which are examined by human analysts in order to extract the actual anomalies of interest. Unfor…
A Meta-Analysis of the Anomaly Detection Problem
Andrew Emmott, Shubhomoy Das, Thomas Dietterich +2
This article provides a thorough meta-analysis of the anomaly detection problem. To accomplish this we first identify approaches to benchmarking anomaly detection algorithms across…
Sequential Feature Explanations for Anomaly Detection
Md Amran Siddiqui, Alan Fern, Thomas G. Dietterich +1
In many applications, an anomaly detection system presents the most anomalous data instance to a human analyst, who then must determine whether the instance is truly of interest (e…