Zobrazeno 1 - 10
of 45
pro vyhledávání: '"Rudrasis Chakraborty"'
Publikováno v:
IEEE Transactions on Pattern Analysis and Machine Intelligence. 44:799-810
Geometric deep learning is a relatively nascent field that has attracted significant attention in the past few years. This is partly due to the availability of data acquired from non-euclidean domains or features extracted from euclidean-space data t
Publikováno v:
IEEE Transactions on Pattern Analysis and Machine Intelligence. 43:3904-3917
Principal component analysis (PCA) and Kernel principal component analysis (KPCA) are fundamental methods in machine learning for dimensionality reduction. The former is a technique for finding this approximation in finite dimensions and the latter i
Autor:
Yunyang Xiong, Zhanpeng Zeng, Rudrasis Chakraborty, Mingxing Tan, Glenn Fung, Yin Li, Vikas Singh
Publikováno v:
Proceedings of the AAAI Conference on Artificial Intelligence. 35:14138-14148
Transformers have emerged as a powerful tool for a broad range of natural language processing tasks. A key component that drives the impressive performance of Transformers is the self-attention mechanism that encodes the influence or dependence of ot
Publikováno v:
Lecture Notes in Computer Science ISBN: 9783031198083
Comput Vis ECCV
Comput Vis ECCV
Comparing the functional behavior of neural network models, whether it is a single network over time or two (or more networks) during or post-training, is an essential step in understanding what they are learning (and what they are not), and for iden
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::ef89ba37eacb5d4034d8de9b55943f71
https://doi.org/10.1007/978-3-031-19809-0_19
https://doi.org/10.1007/978-3-031-19809-0_19
Autor:
Yunyang, Xiong, Zhanpeng, Zeng, Rudrasis, Chakraborty, Mingxing, Tan, Glenn, Fung, Yin, Li, Vikas, Singh
Publikováno v:
Proceedings of the ... AAAI Conference on Artificial Intelligence. AAAI Conference on Artificial Intelligence. 35(16)
Transformers have emerged as a powerful tool for a broad range of natural language processing tasks. A key component that drives the impressive performance of Transformers is the self-attention mechanism that encodes the influence or dependence of ot
Autor:
Jurijs, Nazarovs, Rudrasis, Chakraborty, Songwong, Tasneeyapant, Sathya N, Ravi, Vikas, Singh
Publikováno v:
Uncertain Artif Intell
Panel data involving longitudinal measurements of the same set of participants taken over multiple time points is common in studies to understand childhood development and disease modeling. Deep hybrid models that marry the predictive power of neural
Autor:
Shixuan Li, Meng C. Lin, Rudrasis Chakraborty, Stella X. Yu, Jiayun Wang, Thao N. Yeh, Andrew D. Graham
Publikováno v:
Optometry and vision science : official publication of the American Academy of Optometry, vol 98, iss 9
Optom Vis Sci
Optom Vis Sci
Significance Quantifying meibomian gland morphology from meibography images is used for the diagnosis, treatment, and management of meibomian gland dysfunction in clinics. A novel and automated method is described for quantifying meibomian gland morp
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::0844087cb4f936e68e5847955101d6e4
https://escholarship.org/uc/item/7745h7zm
https://escholarship.org/uc/item/7745h7zm
Convolutional neural networks have been highly successful in image-based learning tasks due to their translation equivariance property. Recent work has generalized the traditional convolutional layer of a convolutional neural network to non-Euclidean
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::c9d1d2f4ff116cc5b2027b67bd651538
http://arxiv.org/abs/2106.15301
http://arxiv.org/abs/2106.15301
Publikováno v:
IEEE transactions on pattern analysis and machine intelligence. 44(8)
Deep neural networks are widely used for understanding 3D point clouds. At each point convolution layer, features are computed from local neighbourhoods of 3D points and combined for subsequent processing in order to extract semantic information. Exi
Publikováno v:
CVPR
Proc IEEE Comput Soc Conf Comput Vis Pattern Recognit
Proc IEEE Comput Soc Conf Comput Vis Pattern Recognit
One strategy for adversarially training a robust model is to maximize its certified radius -- the neighborhood around a given training sample for which the model's prediction remains unchanged. The scheme typically involves analyzing a "smoothed" cla
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::0383e820d870d20abd7ba141084ca04d