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Found 14 papers, 4 papers with code
Unsupervised Contrastive Learning for Robust RF Device Fingerprinting Under Time-Domain Shift
When applied to RF fingerprinting, our model treats RF signals from the same transmission as positive pairs and those from different transmissions as negative pairs.
Transformer-Powered Surrogates Close the ICF Simulation-Experiment Gap with Extremely Limited Data
Recent advances in machine learning, specifically transformer architecture, have led to significant advancements in commercial domains.
HiNoVa: A Novel Open-Set Detection Method for Automating RF Device Authentication
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.
Cross-GAN Auditing: Unsupervised Identification of Attribute Level Similarities and Differences between Pretrained Generative Models
To this end, we introduce Cross-GAN Auditing (xGA) that, given an established "reference" GAN and a newly proposed "client" GAN, jointly identifies intelligible attributes that are either common across both GANs, novel to the client GAN, or missing from the client GAN.
ADL-ID: Adversarial Disentanglement Learning for Wireless Device Fingerprinting Temporal Domain Adaptation
Our framework has been evaluated on real LoRa and WiFi datasets and showed about 24% improvement in accuracy when compared to the baseline CNN network on short-term temporal adaptation.
An Analysis of Complex-Valued CNNs for RF Data-Driven Wireless Device Classification
We perform a deep dive into understanding the impact of (i) the input representation/type and (ii) the architectural layer of the neural network.
Generalizing Cross Entropy Loss with a Beta Proper Composite Loss: An Improved Loss Function for Open Set Recognition
This Beta loss is a proper composite loss with a Beta weight function.
Contrastive Identification of Covariate Shift in Image Data
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.
Counterfactual State Explanations for Reinforcement Learning Agents via Generative Deep Learning
Our second user study investigates if counterfactual state explanations can help non-expert participants identify a flawed agent; we compare against a baseline approach based on a nearest neighbor explanation which uses images from the actual game.
Counterfactual States for Atari Agents via Generative Deep Learning
In this work, we introduce the concept of a counterfactual state to help humans gain a better understanding of what would need to change (minimally) in an Atari game image for the agent to choose a different action.
Open Set Learning with Counterfactual Images
In open set recognition, a classifier must label instances of known classes while detecting instances of unknown classes not encountered during training.
Incorporating Feedback into Tree-based Anomaly Detection
Unfortunately, in realworld applications, this process can be exceedingly difficult for the analyst since a large fraction of high-ranking anomalies are false positives and not interesting from the application perspective.
A Meta-Analysis of the Anomaly Detection Problem
The intended contributions of this article are many; in addition to providing a large publicly-available corpus of anomaly detection benchmarks, we provide an ontology for describing anomaly detection contexts, a methodology for controlling various aspects of benchmark creation, guidelines for future experimental design and a discussion of the many potential pitfalls of trying to measure success in this field.
Sequential Feature Explanations for Anomaly Detection
An SFE of an anomaly is a sequence of features, which are presented to the analyst one at a time (in order) until the information contained in the highlighted features is enough for the analyst to make a confident judgement about the anomaly.
