Zobrazeno 1 - 10
of 297
pro vyhledávání: '"P Jayavelu"'
Continual learning (CL) adapt the deep learning scenarios with timely updated datasets. However, existing CL models suffer from the catastrophic forgetting issue, where new knowledge replaces past learning. In this paper, we propose Continual Learnin
Externí odkaz:
http://arxiv.org/abs/2409.17806
Molecular optimization is a key challenge in drug discovery and material science domain, involving the design of molecules with desired properties. Existing methods focus predominantly on single-property optimization, necessitating repetitive runs to
Externí odkaz:
http://arxiv.org/abs/2409.07786
Continual learning (CL) models are designed to learn new tasks arriving sequentially without re-training the network. However, real-world ML applications have very limited label information and these models suffer from catastrophic forgetting. To add
Externí odkaz:
http://arxiv.org/abs/2405.14623
Autor:
Lin, Zhuoyi, Wu, Yaoxin, Zhou, Bangjian, Cao, Zhiguang, Song, Wen, Zhang, Yingqian, Jayavelu, Senthilnath
Existing neural heuristics often train a deep architecture from scratch for each specific vehicle routing problem (VRP), ignoring the transferable knowledge across different VRP variants. This paper proposes the cross-problem learning to assist heuri
Externí odkaz:
http://arxiv.org/abs/2404.11677
Publikováno v:
Journal of Pharmacy and Bioallied Sciences, Vol 9, Iss 5, Pp 274-280 (2017)
Diagnostic imaging acts as a distinctive method in analyzing and drawing in the appropriate treatment protocol for any procedure. Pertaining to immediate implant placement, determining the bone width and height plays a vital role in the success of th
Externí odkaz:
https://doaj.org/article/cb3dcf245c514b4fae2d39482de5c608
Autor:
Dutta, Rajdeep, Wang, Qincheng, Singh, Ankur, Kumarjiguda, Dhruv, Xiaoli, Li, Jayavelu, Senthilnath
This paper presents a novel RL algorithm, S-REINFORCE, which is designed to generate interpretable policies for dynamic decision-making tasks. The proposed algorithm leverages two types of function approximators, namely Neural Network (NN) and Symbol
Externí odkaz:
http://arxiv.org/abs/2305.07367
Autor:
Singh, Ankur, Jayavelu, Senthilnath
Despite the recent success of deep neural networks, there remains a need for effective methods to enhance domain generalization using vision transformers. In this paper, we propose a novel domain generalization technique called Robust Representation
Externí odkaz:
http://arxiv.org/abs/2302.06874
Autor:
Dam, Tanmoy, Ferdaus, Md Meftahul, Pratama, Mahardhika, Anavatti, Sreenatha G., Jayavelu, Senthilnath, Abbass, Hussein A.
Many real-world classification problems have imbalanced frequency of class labels; a well-known issue known as the "class imbalance" problem. Classic classification algorithms tend to be biased towards the majority class, leaving the classifier vulne
Externí odkaz:
http://arxiv.org/abs/2209.01555
Autor:
Arpit Mishra, Ajay Jajodia, Eryn Weston, Naresh Doni Jayavelu, Mariana Garcia, Daniel Hossack, R. David Hawkins
Publikováno v:
Frontiers in Immunology, Vol 15 (2024)
Type I diabetes is an autoimmune disease mediated by T-cell destruction of β cells in pancreatic islets. Currently, there is no known cure, and treatment consists of daily insulin injections. Genome-wide association studies and twin studies have ind
Externí odkaz:
https://doaj.org/article/289347cc65d04d078c937b222b6228b9
Autor:
Al Ozonoff, Naresh Doni Jayavelu, Shanshan Liu, Esther Melamed, Carly E. Milliren, Jingjing Qi, Linda N. Geng, Grace A. McComsey, Charles B. Cairns, Lindsey R. Baden, Joanna Schaenman, Albert C. Shaw, Hady Samaha, Vicki Seyfert-Margolis, Florian Krammer, Lindsey B. Rosen, Hanno Steen, Caitlin Syphurs, Ravi Dandekar, Casey P. Shannon, Rafick P. Sekaly, Lauren I. R. Ehrlich, David B. Corry, Farrah Kheradmand, Mark A. Atkinson, Scott C. Brakenridge, Nelson I. Agudelo Higuita, Jordan P. Metcalf, Catherine L. Hough, William B. Messer, Bali Pulendran, Kari C. Nadeau, Mark M. Davis, Ana Fernandez Sesma, Viviana Simon, Harm van Bakel, Seunghee Kim-Schulze, David A. Hafler, Ofer Levy, Monica Kraft, Chris Bime, Elias K. Haddad, Carolyn S. Calfee, David J. Erle, Charles R. Langelier, Walter Eckalbar, Steven E. Bosinger, IMPACC Network, Bjoern Peters, Steven H. Kleinstein, Elaine F. Reed, Alison D. Augustine, Joann Diray-Arce, Holden T. Maecker, Matthew C. Altman, Ruth R. Montgomery, Patrice M. Becker, Nadine Rouphael
Publikováno v:
Nature Communications, Vol 15, Iss 1, Pp 1-17 (2024)
Abstract Post-acute sequelae of SARS-CoV-2 (PASC) is a significant public health concern. We describe Patient Reported Outcomes (PROs) on 590 participants prospectively assessed from hospital admission for COVID-19 through one year after discharge. M
Externí odkaz:
https://doaj.org/article/ec0ddc29cd90460ca1ec27bea2b831b8