no code implementations • 12 Apr 2024 • Katie Christensen, Lyric Otto, Seth Bassetti, Claudia Tebaldi, Brian Hutchinson
Earth system models (ESMs) are the principal tools used in climate science to generate future climate projections under various atmospheric emissions scenarios on a global or regional scale.
no code implementations • 20 Oct 2023 • Sarah Coffland, Katie Christensen, Filip Jagodzinski, Brian Hutchinson
We explore and present how RoseNet is able to emulate the exhaustive data set using deep learning methods, and show to what extent it can predict Rosetta metrics for unseen mutant sequences with two InDels.
no code implementations • 23 Apr 2023 • Seth Bassetti, Brian Hutchinson, Claudia Tebaldi, Ben Kravitz
Earth System Models (ESMs) are essential tools for understanding the impact of human actions on Earth's climate.
1 code implementation • 29 Jul 2021 • Eric Slyman, Chris Daw, Morgan Skrabut, Ana Usenko, Brian Hutchinson
We obtain strong results on the new fine-grained task and state-of-the-art on the 4-way task: our best model obtains frame-level error rates of 6. 2%, 7. 7% and 28. 0% when generalizing to unseen instructors for the 4-way, 5-way, and 9-way classification tasks, respectively (relative reductions of 35. 4%, 48. 3% and 21. 6% over a strong baseline).
no code implementations • 28 Jul 2021 • Piper Wolters, Logan Sizemore, Chris Daw, Brian Hutchinson, Lauren Phillips
Many applications involve detecting and localizing specific sound events within long, untrimmed documents, including keyword spotting, medical observation, and bioacoustic monitoring for conservation.
no code implementations • 29 Apr 2021 • Alexis Ayala, Christopher Drazic, Brian Hutchinson, Ben Kravitz, Claudia Tebaldi
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 forces are going to evolve.
no code implementations • 2 Dec 2020 • Piper Wolters, Chris Careaga, Brian Hutchinson, Lauren Phillips
In this research, we address two audio classification tasks (speaker identification and activity classification) with the Prototypical Network few-shot learning algorithm, and assess performance of various encoder architectures.
no code implementations • 23 Nov 2020 • Alexandra Puchko, Robert Link, Brian Hutchinson, Ben Kravitz, Abigail Snyder
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 scenarios.
Generative Adversarial Network Vocal Bursts Intensity Prediction
no code implementations • 24 Apr 2020 • Loc Truong, Chace Jones, Brian Hutchinson, Andrew August, Brenda Praggastis, Robert Jasper, Nicole Nichols, Aaron Tuor
First, the success rate of backdoor poisoning attacks varies widely, depending on several factors, including model architecture, trigger pattern and regularization technique.
no code implementations • 14 Sep 2019 • Chris Careaga, Brian Hutchinson, Nathan Hodas, Lawrence Phillips
In this work, we address the task of few-shot video action recognition with a set of two-stream models.
no code implementations • 13 Mar 2018 • Andy Brown, Aaron Tuor, Brian Hutchinson, Nicole Nichols
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 attack detection, hardware and software system failures, and malware detection.
1 code implementation • 2 Dec 2017 • Aaron Tuor, Ryan Baerwolf, Nicolas Knowles, Brian Hutchinson, Nicole Nichols, Rob Jasper
By treating system logs as threads of interleaved "sentences" (event log lines) to train online unsupervised neural network language models, our approach provides an adaptive model of normal network behavior.
1 code implementation • 2 Oct 2017 • Aaron Tuor, Samuel Kaplan, Brian Hutchinson, Nicole Nichols, Sean Robinson
As a prospective filter for the human analyst, we present an online unsupervised deep learning approach to detect anomalous network activity from system logs in real time.