68 citations · 127 across the 4 of their papers we have counts for
6 papers
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…
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…