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pro vyhledávání: '"Adibi P"'
Recent research endeavours have theoretically shown the beneficial effect of cooperation in multi-agent reinforcement learning (MARL). In a setting involving $N$ agents, this beneficial effect usually comes in the form of an $N$-fold linear convergen
Externí odkaz:
http://arxiv.org/abs/2407.20441
Depression is a common mental health issue that requires prompt diagnosis and treatment. Despite the promise of social media data for depression detection, the opacity of employed deep learning models hinders interpretability and raises bias concerns
Externí odkaz:
http://arxiv.org/abs/2407.21041
Publikováno v:
Cancer Management and Research, Vol Volume 12, Pp 2255-2264 (2020)
Shahin Salarvand,1 Simin Hemati,2 Payman Adibi,3 Fariba Taleghani,4 Mohammad Saleki5 1Hepatitis Research Center, Community Health Nursing Department, Faculty of Nursing and Midwifery, Lorestan University of Medical Sciences, Khorramabad, Iran; 2Radio
Externí odkaz:
https://doaj.org/article/b6f3e1a873314f918095f044bcdb8329
Publikováno v:
Advances in Medical Education and Practice, Vol Volume 10, Pp 515-526 (2019)
Sara Heydari,1 Peyman Adibi,2 Athar Omid,3 Nikoo Yamani11Department of Medical Education, Medical Education Research Center, Isfahan University of Medical Sciences, Isfahan, Iran; 2Integrative Functional Gastroenterology Research Center, Isfahan Univ
Externí odkaz:
https://doaj.org/article/dce8c89e8a7c435b9099f4a8f0c11479
Autor:
Fabbro, Nicolò Dal, Adibi, Arman, Poor, H. Vincent, Kulkarni, Sanjeev R., Mitra, Aritra, Pappas, George J.
We consider a setting in which $N$ agents aim to speedup a common Stochastic Approximation (SA) problem by acting in parallel and communicating with a central server. We assume that the up-link transmissions to the server are subject to asynchronous
Externí odkaz:
http://arxiv.org/abs/2403.17247
Meta-learning problem is usually formulated as a bi-level optimization in which the task-specific and the meta-parameters are updated in the inner and outer loops of optimization, respectively. However, performing the optimization in the Riemannian s
Externí odkaz:
http://arxiv.org/abs/2402.18605
Autor:
Adibi, Arman, Fabbro, Nicolo Dal, Schenato, Luca, Kulkarni, Sanjeev, Poor, H. Vincent, Pappas, George J., Hassani, Hamed, Mitra, Aritra
Motivated by applications in large-scale and multi-agent reinforcement learning, we study the non-asymptotic performance of stochastic approximation (SA) schemes with delayed updates under Markovian sampling. While the effect of delays has been exten
Externí odkaz:
http://arxiv.org/abs/2402.11800
Discrete optimization problems often arise in deep learning tasks, despite the fact that neural networks typically operate on continuous data. One class of these problems involve objective functions which depend on neural networks, but optimization v
Externí odkaz:
http://arxiv.org/abs/2310.09890
Often linear regression is used to perform mediation analysis. However, in many instances, the underlying relationships may not be linear, as in the case of placental-fetal hormones and fetal development. Although, the exact functional form of the re
Externí odkaz:
http://arxiv.org/abs/2310.09185
Delays and asynchrony are inevitable in large-scale machine-learning problems where communication plays a key role. As such, several works have extensively analyzed stochastic optimization with delayed gradients. However, as far as we are aware, no a
Externí odkaz:
http://arxiv.org/abs/2307.06886