Zobrazeno 1 - 10
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pro vyhledávání: '"Khanduri, A."'
Autor:
Li, Chengyin, Zhu, Hui, Sultan, Rafi Ibn, Ebadian, Hassan Bagher, Khanduri, Prashant, Indrin, Chetty, Thind, Kundan, Zhu, Dongxiao
In the diverse field of medical imaging, automatic segmentation has numerous applications and must handle a wide variety of input domains, such as different types of Computed Tomography (CT) scans and Magnetic Resonance (MR) images. This heterogeneit
Externí odkaz:
http://arxiv.org/abs/2411.15576
Autor:
Fang, Minghong, Zhang, Zifan, Hairi, Khanduri, Prashant, Liu, Jia, Lu, Songtao, Liu, Yuchen, Gong, Neil
Federated learning (FL) enables multiple clients to collaboratively train machine learning models without revealing their private training data. In conventional FL, the system follows the server-assisted architecture (server-assisted FL), where the t
Externí odkaz:
http://arxiv.org/abs/2406.10416
Existing works in federated learning (FL) often assume an ideal system with either full client or uniformly distributed client participation. However, in practice, it has been observed that some clients may never participate in FL training (aka incom
Externí odkaz:
http://arxiv.org/abs/2405.02745
Autor:
Qiang, Yao, Zhou, Xiangyu, Zade, Saleh Zare, Roshani, Mohammad Amin, Khanduri, Prashant, Zytko, Douglas, Zhu, Dongxiao
The advent of Large Language Models (LLMs) has marked significant achievements in language processing and reasoning capabilities. Despite their advancements, LLMs face vulnerabilities to data poisoning attacks, where adversaries insert backdoor trigg
Externí odkaz:
http://arxiv.org/abs/2402.13459
Autor:
Khanduri, Prashant, Li, Chengyin, Sultan, Rafi Ibn, Qiang, Yao, Kliewer, Joerg, Zhu, Dongxiao
Recently, compositional optimization (CO) has gained popularity because of its applications in distributionally robust optimization (DRO) and many other machine learning problems. Large-scale and distributed availability of data demands the developme
Externí odkaz:
http://arxiv.org/abs/2311.12652
Autor:
Sultan, Rafi Ibn, Li, Chengyin, Zhu, Hui, Khanduri, Prashant, Brocanelli, Marco, Zhu, Dongxiao
In geographical image segmentation, performance is often constrained by the limited availability of training data and a lack of generalizability, particularly for segmenting mobility infrastructure such as roads, sidewalks, and crosswalks. Vision fou
Externí odkaz:
http://arxiv.org/abs/2311.11319
Vision Transformers (ViTs) have become prominent models for solving various vision tasks. However, the interpretability of ViTs has not kept pace with their promising performance. While there has been a surge of interest in developing {\it post hoc}
Externí odkaz:
http://arxiv.org/abs/2309.08035
Autor:
Li, Chengyin, Khanduri, Prashant, Qiang, Yao, Sultan, Rafi Ibn, Chetty, Indrin, Zhu, Dongxiao
Segment Anything Model (SAM) is one of the pioneering prompt-based foundation models for image segmentation and has been rapidly adopted for various medical imaging applications. However, in clinical settings, creating effective prompts is notably ch
Externí odkaz:
http://arxiv.org/abs/2308.14936
Recently, bi-level optimization (BLO) has taken center stage in some very exciting developments in the area of signal processing (SP) and machine learning (ML). Roughly speaking, BLO is a classical optimization problem that involves two levels of hie
Externí odkaz:
http://arxiv.org/abs/2308.00788
Autor:
Sandeep Kumar, Vinod Prasad Khanduri
Publikováno v:
Heliyon, Vol 10, Iss 23, Pp e40797- (2024)
The Himalayan alpine treeline varies depending on altitude and aspects, supporting a variety of plant species. In recent years, climate changes have exerted pressure on the vegetation in this region, challenging its adaptation to rapidly changing env
Externí odkaz:
https://doaj.org/article/e9742af9f2c94a4cbc5e73fd709003c3
