Zobrazeno 1 - 7
of 7
pro vyhledávání: '"Bisla, Devansh"'
Autor:
Fang, Shihong, Zhu, Haoran, Bisla, Devansh, Choromanska, Anna, Ravindran, Satish, Ren, Dongyin, Wu, Ryan
Among various sensors for assisted and autonomous driving systems, automotive radar has been considered as a robust and low-cost solution even in adverse weather or lighting conditions. With the recent development of radar technologies and open-sourc
Externí odkaz:
http://arxiv.org/abs/2209.12940
In this paper, we study the sharpness of a deep learning (DL) loss landscape around local minima in order to reveal systematic mechanisms underlying the generalization abilities of DL models. Our analysis is performed across varying network and optim
Externí odkaz:
http://arxiv.org/abs/2201.08025
Autor:
McShea, Bernard, Wright, Kevin, Lam, Denley, Schmidt, Steve, Choromanska, Anna, Bisla, Devansh, Fang, Shihong, Sarmadi, Alireza, Krishnamurthy, Prashanth, Khorrami, Farshad
Securing enterprise networks presents challenges in terms of both their size and distributed structure. Data required to detect and characterize malicious activities may be diffused and may be located across network and endpoint devices. Further, cyb
Externí odkaz:
http://arxiv.org/abs/2112.04114
This paper focuses on understanding how the generalization error scales with the amount of the training data for deep neural networks (DNNs). Existing techniques in statistical learning require computation of capacity measures, such as VC dimension,
Externí odkaz:
http://arxiv.org/abs/2105.01867
Melanoma is one of the ten most common cancers in the US. Early detection is crucial for survival, but often the cancer is diagnosed in the fatal stage. Deep learning has the potential to improve cancer detection rates, but its applicability to melan
Externí odkaz:
http://arxiv.org/abs/1902.06061
Autor:
Bisla, Devansh, Choromanska, Anna
In many machine learning applications, from medical diagnostics to autonomous driving, the availability of prior knowledge can be used to improve the predictive performance of learning algorithms and incorporate `physical,' `domain knowledge,' or `co
Externí odkaz:
http://arxiv.org/abs/1805.09474
Akademický článek
Tento výsledek nelze pro nepřihlášené uživatele zobrazit.
K zobrazení výsledku je třeba se přihlásit.
K zobrazení výsledku je třeba se přihlásit.