152 citations · 262 across the 12 of their papers we have counts for
12 papers
Fine-Grained Classroom Activity Detection from Audio with Neural Networks
Eric Slyman, Chris Daw, Morgan Skrabut +2
Instructors are increasingly incorporating student-centered learning techniques in their classrooms to improve learning outcomes. In addition to lecture, these class sessions invol…
Proposal-based Few-shot Sound Event Detection for Speech and Environmental Sounds with Perceivers
Piper Wolters, Logan Sizemore, Chris Daw +2
Many applications involve detecting and localizing specific sound events within long, untrimmed documents, including keyword spotting, medical observation, and bioacoustic monitori…
Loosely Conditioned Emulation of Global Climate Models With Generative Adversarial Networks
Alexis Ayala, Christopher Drazic, Brian Hutchinson +2
Climate models encapsulate our best understanding of the Earth system, allowing research to be conducted on its future under alternative assumptions of how human-driven climate for…
A Study of Few-Shot Audio Classification
Piper Wolters, Chris Careaga, Brian Hutchinson +1
Advances in deep learning have resulted in state-of-the-art performance for many audio classification tasks but, unlike humans, these systems traditionally require large amounts of…
Untangling the Galaxy III: Photometric Search for Pre-main Sequence Stars with Deep Learning
Aidan McBride, Ryan Lingg, Marina Kounkel +2
A reliable census of pre-main sequence stars with known ages is critical to our understanding of early stellar evolution, but historically there has been difficulty in separating s…
DeepClimGAN: A High-Resolution Climate Data Generator
Alexandra Puchko, Robert Link, Brian Hutchinson +2
Earth system models (ESMs), which simulate the physics and chemistry of the global atmosphere, land, and ocean, are often used to generate future projections of climate change scen…
Systematic Evaluation of Backdoor Data Poisoning Attacks on Image Classifiers
Loc Truong, Chace Jones, Brian Hutchinson +5
Backdoor data poisoning attacks have recently been demonstrated in computer vision research as a potential safety risk for machine learning (ML) systems. Traditional data poisoning…
Metric-Based Few-Shot Learning for Video Action Recognition
Chris Careaga, Brian Hutchinson, Nathan Hodas +1
In the few-shot scenario, a learner must effectively generalize to unseen classes given a small support set of labeled examples. While a relatively large amount of research has gon…
Recurrent Neural Network Attention Mechanisms for Interpretable System Log Anomaly Detection
Andy Brown, Aaron Tuor, Brian Hutchinson +1
Deep learning has recently demonstrated state-of-the art performance on key tasks related to the maintenance of computer systems, such as intrusion detection, denial of service att…
Protein Mutation Stability Ternary Classification using Neural Networks and Rigidity Analysis
Richard Olney, Aaron Tuor, Filip Jagodzinski +1
Discerning how a mutation affects the stability of a protein is central to the study of a wide range of diseases. Machine learning and statistical analysis techniques can inform ho…
Recurrent Neural Network Language Models for Open Vocabulary Event-Level Cyber Anomaly Detection
Aaron Tuor, Ryan Baerwolf, Nicolas Knowles +3
Automated analysis methods are crucial aids for monitoring and defending a network to protect the sensitive or confidential data it hosts. This work introduces a flexible, powerful…
Deep Learning for Unsupervised Insider Threat Detection in Structured Cybersecurity Data Streams
Aaron Tuor, Samuel Kaplan, Brian Hutchinson +2
Analysis of an organization's computer network activity is a key component of early detection and mitigation of insider threat, a growing concern for many organizations. Raw system…