Zobrazeno 1 - 10
of 127
pro vyhledávání: '"Li, Chengyin"'
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
The Segment Anything Model (SAM) has shown impressive performance when applied to natural image segmentation. However, it struggles with geographical images like aerial and satellite imagery, especially when segmenting mobility infrastructure includi
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
Vision Transformer (ViT) has recently gained significant attention in solving computer vision (CV) problems due to its capability of extracting informative features and modeling long-range dependencies through the attention mechanism. Whereas recent
Externí odkaz:
http://arxiv.org/abs/2301.13803
There are increasing demands for understanding deep neural networks' (DNNs) behavior spurred by growing security and/or transparency concerns. Due to multi-layer nonlinearity of the deep neural network architectures, explaining DNN predictions still
Externí odkaz:
http://arxiv.org/abs/2301.06989
Electric Vehicle (EV) charging recommendation that both accommodates user preference and adapts to the ever-changing external environment arises as a cost-effective strategy to alleviate the range anxiety of private EV drivers. Previous studies focus
Externí odkaz:
http://arxiv.org/abs/2210.12693
Autor:
Li, Chengyin, Qiang, Yao, Sultan, Rafi Ibn, Bagher-Ebadian, Hassan, Khanduri, Prashant, Chetty, Indrin J., Zhu, Dongxiao
Computed Tomography (CT) based precise prostate segmentation for treatment planning is challenging due to (1) the unclear boundary of the prostate derived from CT's poor soft tissue contrast and (2) the limitation of convolutional neural network-base
Externí odkaz:
http://arxiv.org/abs/2210.03189
Publikováno v:
AdvML Frontiers workshop at 39th International Conference on Machine Learning (ICML), Baltimore, Maryland, USA, 2022
This work tackles a central machine learning problem of performance degradation on out-of-distribution (OOD) test sets. The problem is particularly salient in medical imaging based diagnosis system that appears to be accurate but fails when tested in
Externí odkaz:
http://arxiv.org/abs/2209.04326
Publikováno v:
In Atmospheric Research January 2024 297
