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of 54 869
pro vyhledávání: '"Ghasemi, A"'
Autor:
Ghasemi, Majid, Moosavi, Amir Hossein, Sorkhoh, Ibrahim, Agrawal, Anjali, Alzhouri, Fadi, Ebrahimi, Dariush
Reinforcement Learning (RL) is a branch of Artificial Intelligence (AI) which focuses on training agents to make decisions by interacting with their environment to maximize cumulative rewards. An overview of RL is provided in this paper, which discus
Externí odkaz:
http://arxiv.org/abs/2408.07712
Autor:
Soltani, Hamidreza, Ghasemi, Erfan
Recent advancements in learned image compression (LIC) methods have demonstrated superior performance over traditional hand-crafted codecs. These learning-based methods often employ convolutional neural networks (CNNs) or Transformer-based architectu
Externí odkaz:
http://arxiv.org/abs/2408.03842
In this paper, we explore the problem of utilizing Integrated Access and Backhaul (IAB) technology in Non-Terrestrial Networks (NTN), with a particular focus on aerial access networks. We consider an Uncrewed Aerial Vehicle (UAV)-based wireless netwo
Externí odkaz:
http://arxiv.org/abs/2407.15463
Additive noise models (ANMs) are an important setting studied in causal inference. Most of the existing works on ANMs assume causal sufficiency, i.e., there are no unobserved confounders. This paper focuses on confounded ANMs, where a set of treatmen
Externí odkaz:
http://arxiv.org/abs/2407.10014
Publikováno v:
Cancer Innovation 2024;3:e136
With the advances in artificial intelligence (AI), data-driven algorithms are becoming increasingly popular in the medical domain. However, due to the nonlinear and complex behavior of many of these algorithms, decision-making by such algorithms is n
Externí odkaz:
http://arxiv.org/abs/2407.12058
Factored Markov Decision Processes (fMDPs) are a class of Markov Decision Processes (MDPs) in which the states (and actions) can be factored into a set of state (and action) variables. The state space, action space and reward function of a fMDP can b
Externí odkaz:
http://arxiv.org/abs/2407.07310
Autor:
Gema, Aryo Pradipta, Leang, Joshua Ong Jun, Hong, Giwon, Devoto, Alessio, Mancino, Alberto Carlo Maria, Saxena, Rohit, He, Xuanli, Zhao, Yu, Du, Xiaotang, Madani, Mohammad Reza Ghasemi, Barale, Claire, McHardy, Robert, Harris, Joshua, Kaddour, Jean, van Krieken, Emile, Minervini, Pasquale
Maybe not. We identify and analyse errors in the popular Massive Multitask Language Understanding (MMLU) benchmark. Even though MMLU is widely adopted, our analysis demonstrates numerous ground truth errors that obscure the true capabilities of LLMs.
Externí odkaz:
http://arxiv.org/abs/2406.04127
Causal discovery aims to uncover cause-and-effect relationships encoded in causal graphs by leveraging observational, interventional data, or their combination. The majority of existing causal discovery methods are developed assuming infinite interve
Externí odkaz:
http://arxiv.org/abs/2405.11548
We consider the problem of selecting an optimal subset of information sources for a hypothesis testing/classification task where the goal is to identify the true state of the world from a finite set of hypotheses, based on finite observation samples
Externí odkaz:
http://arxiv.org/abs/2405.10930
Publikováno v:
IEEE Access 2024
This systematic literature review paper explores the use of extended reality {(XR)} technology for smart built environments and particularly for smart lighting systems design. Smart lighting is a novel concept that has emerged over a decade now and i
Externí odkaz:
http://arxiv.org/abs/2405.06928