Zobrazeno 1 - 10
of 84
pro vyhledávání: '"Li, Adrian"'
Autor:
Mostafiz, Mir Imtiaz, Kim, Eunseob, Li, Adrian Shuai, Bertino, Elisa, Jun, Martin Byung-Guk, Shakouri, Ali
Cutting state monitoring in the milling process is crucial for improving manufacturing efficiency and tool life. Cutting sound detection using machine learning (ML) models, inspired by experienced machinists, can be employed as a cost-effective and n
Externí odkaz:
http://arxiv.org/abs/2410.17574
In the application of deep learning for malware classification, it is crucial to account for the prevalence of malware evolution, which can cause trained classifiers to fail on drifted malware. Existing solutions to combat concept drift use active le
Externí odkaz:
http://arxiv.org/abs/2407.13918
Autor:
Cuan, Catie, Jeffrey, Kyle, Kleiven, Kim, Li, Adrian, Fisher, Emre, Harrison, Matt, Holson, Benjie, Okamura, Allison, Bennice, Matt
For decades, robotics researchers have pursued various tasks for multi-robot systems, from cooperative manipulation to search and rescue. These tasks are multi-robot extensions of classical robotic tasks and often optimized on dimensions such as spee
Externí odkaz:
http://arxiv.org/abs/2404.00442
Many machine learning and data mining algorithms rely on the assumption that the training and testing data share the same feature space and distribution. However, this assumption may not always hold. For instance, there are situations where we need t
Externí odkaz:
http://arxiv.org/abs/2403.00935
The most effective domain adaptation (DA) involves the decomposition of data representation into a domain independent representation (DIRep), and a domain dependent representation (DDRep). A classifier is trained by using the DIRep of the labeled sou
Externí odkaz:
http://arxiv.org/abs/2306.00262
Deep learning (DL) techniques are highly effective for defect detection from images. Training DL classification models, however, requires vast amounts of labeled data which is often expensive to collect. In many cases, not only the available training
Externí odkaz:
http://arxiv.org/abs/2306.00202
Autor:
Herzog, Alexander, Rao, Kanishka, Hausman, Karol, Lu, Yao, Wohlhart, Paul, Yan, Mengyuan, Lin, Jessica, Arenas, Montserrat Gonzalez, Xiao, Ted, Kappler, Daniel, Ho, Daniel, Rettinghouse, Jarek, Chebotar, Yevgen, Lee, Kuang-Huei, Gopalakrishnan, Keerthana, Julian, Ryan, Li, Adrian, Fu, Chuyuan Kelly, Wei, Bob, Ramesh, Sangeetha, Holden, Khem, Kleiven, Kim, Rendleman, David, Kirmani, Sean, Bingham, Jeff, Weisz, Jon, Xu, Ying, Lu, Wenlong, Bennice, Matthew, Fong, Cody, Do, David, Lam, Jessica, Bai, Yunfei, Holson, Benjie, Quinlan, Michael, Brown, Noah, Kalakrishnan, Mrinal, Ibarz, Julian, Pastor, Peter, Levine, Sergey
We describe a system for deep reinforcement learning of robotic manipulation skills applied to a large-scale real-world task: sorting recyclables and trash in office buildings. Real-world deployment of deep RL policies requires not only effective tra
Externí odkaz:
http://arxiv.org/abs/2305.03270
Controlled sharing is fundamental to distributed systems. We consider a capability-based distributed authorization system where a client receives capabilities (access tokens) from an authorization server to access the resources of resource servers. C
Externí odkaz:
http://arxiv.org/abs/2211.04980
The predictive information, the mutual information between the past and future, has been shown to be a useful representation learning auxiliary loss for training reinforcement learning agents, as the ability to model what will happen next is critical
Externí odkaz:
http://arxiv.org/abs/2210.08217
Publikováno v:
In Computers & Security May 2024 140
