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pro vyhledávání: '"Fendley, Neil"'
Training reinforcement learning agents that continually learn across multiple environments is a challenging problem. This is made more difficult by a lack of reproducible experiments and standard metrics for comparing different continual learning app
Externí odkaz:
http://arxiv.org/abs/2208.04287
Autor:
Johnson, Erik C., Nguyen, Eric Q., Schreurs, Blake, Ewulum, Chigozie S., Ashcraft, Chace, Fendley, Neil M., Baker, Megan M., New, Alexander, Vallabha, Gautam K.
Despite groundbreaking progress in reinforcement learning for robotics, gameplay, and other complex domains, major challenges remain in applying reinforcement learning to the evolving, open-world problems often found in critical application spaces. R
Externí odkaz:
http://arxiv.org/abs/2203.07454
Searching for small objects in large images is a task that is both challenging for current deep learning systems and important in numerous real-world applications, such as remote sensing and medical imaging. Thorough scanning of very large images is
Externí odkaz:
http://arxiv.org/abs/2012.06509
Whilst adversarial attack detection has received considerable attention, it remains a fundamentally challenging problem from two perspectives. First, while threat models can be well-defined, attacker strategies may still vary widely within those cons
Externí odkaz:
http://arxiv.org/abs/2012.06405
We focus on the development of effective adversarial patch attacks and -- for the first time -- jointly address the antagonistic objectives of attack success and obtrusiveness via the design of novel semi-transparent patches. This work is motivated b
Externí odkaz:
http://arxiv.org/abs/2005.00656
In this paper, we introduce the TrojAI software framework, an open source set of Python tools capable of generating triggered (poisoned) datasets and associated deep learning (DL) models with trojans at scale. We utilize the developed framework to ge
Externí odkaz:
http://arxiv.org/abs/2003.07233
This paper considers attacks against machine learning algorithms used in remote sensing applications, a domain that presents a suite of challenges that are not fully addressed by current research focused on natural image data such as ImageNet. In par
Externí odkaz:
http://arxiv.org/abs/1805.10997
We present a new dataset, Functional Map of the World (fMoW), which aims to inspire the development of machine learning models capable of predicting the functional purpose of buildings and land use from temporal sequences of satellite images and a ri
Externí odkaz:
http://arxiv.org/abs/1711.07846
Publikováno v:
2022 IEEE/CVF Winter Conference on Applications of Computer Vision (WACV).
Whilst adversarial attack detection has received considerable attention, it remains a fundamentally challenging problem from two perspectives. First, while threat models can be well-defined, attacker strategies may still vary widely within those cons
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::f2afd86d39680781943152a2a8f0f5a0
https://doi.org/10.1109/wacv51458.2022.00287
https://doi.org/10.1109/wacv51458.2022.00287
Autor:
Rollend, Derek, Foster, Kevin, Kott, Tomek M., Mocharla, Rohita, Muñoz, Rai, Fendley, Neil, Ashcraft, Chace, Willard, Frank, Reilly, Elizabeth P., Hughes, Marisa
Publikováno v:
Environmental Data Science; 2023, Vol. 2, p1-18, 18p