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pro vyhledávání: '"Araabi, Babak N."'
Task allocation is a crucial process in modern systems, but it is often challenged by incomplete information about the utilities of participating agents. In this paper, we propose a new profit maximization mechanism for the task allocation problem, w
Externí odkaz:
http://arxiv.org/abs/2302.05677
Autor:
Movahedi, Sajad, Adabinejad, Melika, Imani, Ayyoob, Keshavarz, Arezou, Dehghani, Mostafa, Shakery, Azadeh, Araabi, Babak N.
Differentiable neural architecture search (DARTS) is a popular method for neural architecture search (NAS), which performs cell-search and utilizes continuous relaxation to improve the search efficiency via gradient-based optimization. The main short
Externí odkaz:
http://arxiv.org/abs/2210.07998
Publikováno v:
In Journal of Process Control September 2023 129
Autor:
Derakhshani, Mohammad Mahdi, Masoudnia, Saeed, Shaker, Amir Hossein, Mersa, Omid, Sadeghi, Mohammad Amin, Rastegari, Mohammad, Araabi, Babak N.
We present a simple and effective learning technique that significantly improves mAP of YOLO object detectors without compromising their speed. During network training, we carefully feed in localization information. We excite certain activations in o
Externí odkaz:
http://arxiv.org/abs/1906.05388
This paper introduces two straightforward, effective indices to evaluate the input data and the data flowing through layers of a feedforward deep neural network. For classification problems, the separation rate of target labels in the space of datafl
Externí odkaz:
http://arxiv.org/abs/1906.05156
Autor:
Masoudnia, Saeed, Mersa, Omid, Araabi, Babak N., Vahabie, Abdol-Hossein, Sadeghi, Mohammad Amin, Ahmadabadi, Majid Nili
Publikováno v:
Expert Systems with Applications, 2019, 133, 317-330
Offline Signature Verification (OSV) is a challenging pattern recognition task, especially in presence of skilled forgeries that are not available during training. This study aims to tackle its challenges and meet the substantial need for generalizat
Externí odkaz:
http://arxiv.org/abs/1903.06536
Publikováno v:
2019 4th International Conference on Pattern Recognition and Image Analysis (IPRIA)
Offline Signature Verification (OSV) is a challenging pattern recognition task, especially when it is expected to generalize well on the skilled forgeries that are not available during the training. Its challenges also include small training sample a
Externí odkaz:
http://arxiv.org/abs/1903.06249
Publikováno v:
2019 4th International Conference on Pattern Recognition and Image Analysis (IPRIA)
Offline Signature Verification (OSV) remains a challenging pattern recognition task, especially in the presence of skilled forgeries that are not available during the training. This challenge is aggravated when there are small labeled training data a
Externí odkaz:
http://arxiv.org/abs/1903.06255
Akademický článek
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The pivotal role of datasets in signature verification systems motivates researchers to collect signature samples. Distinct characteristics of Persian signature demands for richer and culture-dependent offline signature datasets. This paper introduce
Externí odkaz:
http://arxiv.org/abs/1603.03235