Zobrazeno 1 - 10
of 9 597
pro vyhledávání: '"Murari A"'
Autor:
Mohammad Naimul Islam, Galina A. Gusarova, Shonit R. Das, Li Li, Eiji Monma, Murari Anjaneyulu, Liberty Mthunzi, Sadiqa K. Quadri, Edward Owusu-Ansah, Sunita Bhattacharya, Jahar Bhattacharya
Publikováno v:
Nature Communications, Vol 13, Iss 1, Pp 1-15 (2022)
Acute lung injury caused by inhalation of pathogens leads to mortality, but the mechanisms are unclear. Here, the authors show in mice that that loss of the mitochondrial calcium uniporter (MCU) of alveolar type 2 cells (AT2) impaired mitochondrial C
Externí odkaz:
https://doaj.org/article/a9cc5eeffd654bdc8a97a85b6dd2ed80
Deep learning has seen remarkable advancements in machine learning, yet it often demands extensive annotated data. Tasks like 3D semantic segmentation impose a substantial annotation burden, especially in domains like medicine, where expert annotatio
Externí odkaz:
http://arxiv.org/abs/2411.15763
Autor:
Canizares, Priscilla, Murari, Davide, Schönlieb, Carola-Bibiane, Sherry, Ferdia, Shumaylov, Zakhar
Hamilton's equations of motion form a fundamental framework in various branches of physics, including astronomy, quantum mechanics, particle physics, and climate science. Classical numerical solvers are typically employed to compute the time evolutio
Externí odkaz:
http://arxiv.org/abs/2410.18262
The key components of machine learning are data samples for training, model for learning patterns, and loss function for optimizing accuracy. Analogously, unlearning can potentially be achieved through anti-data samples (or anti-samples), unlearning
Externí odkaz:
http://arxiv.org/abs/2410.17050
Autor:
Chanyal, B. C., Murari, Sayyam
This study explores the application of quaternionic left and right-handed algebraic frameworks to discuss the neutrino-handedness behavior. The generalized left and right-handed Dirac equations are expressed in terms of quaternionic four-dimensional
Externí odkaz:
http://arxiv.org/abs/2410.05337
Autor:
Chundawat, Vikram S, Niroula, Pushkar, Dhungana, Prasanna, Schoepf, Stefan, Mandal, Murari, Brintrup, Alexandra
Federated learning (FL) has enabled collaborative model training across decentralized data sources or clients. While adding new participants to a shared model does not pose great technical hurdles, the removal of a participant and their related infor
Externí odkaz:
http://arxiv.org/abs/2410.04144
Recent research has seen significant interest in methods for concept removal and targeted forgetting in text-to-image diffusion models. In this paper, we conduct a comprehensive white-box analysis showing the vulnerabilities in existing diffusion mod
Externí odkaz:
http://arxiv.org/abs/2409.05668
Continual learning and machine unlearning are crucial challenges in machine learning, typically addressed separately. Continual learning focuses on adapting to new knowledge while preserving past information, whereas unlearning involves selectively f
Externí odkaz:
http://arxiv.org/abs/2408.11374
This paper considers one of the fundamental parallel-in-time methods for the solution of ordinary differential equations, Parareal, and extends it by adopting a neural network as a coarse propagator. We provide a theoretical analysis of the convergen
Externí odkaz:
http://arxiv.org/abs/2408.09756
Unlearning methods for recommender systems (RS) have emerged to address privacy issues and concerns about legal compliance. However, evolving user preferences and content licensing issues still remain unaddressed. This is particularly true in case of
Externí odkaz:
http://arxiv.org/abs/2405.15328