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pro vyhledávání: '"Torkamani, MohamadAli"'
This work tackles an intriguing and fundamental open challenge in representation learning: Given a well-trained deep learning model, can it be reprogrammed to enhance its robustness against adversarial or noisy input perturbations without altering it
Externí odkaz:
http://arxiv.org/abs/2410.04577
Transformers have gained widespread acclaim for their versatility in handling diverse data structures, yet their application to log data remains underexplored. Log data, characterized by its hierarchical, dictionary-like structure, poses unique chall
Externí odkaz:
http://arxiv.org/abs/2408.16803
Autor:
Han, Haoyu, Liu, Xiaorui, Shi, Feng, Torkamani, MohamadAli, Aggarwal, Charu C., Tang, Jiliang
Graph Neural Networks (GNNs) have emerged as a powerful tool for semi-supervised node classification tasks. However, recent studies have revealed various biases in GNNs stemming from both node features and graph topology. In this work, we uncover a n
Externí odkaz:
http://arxiv.org/abs/2305.15822
Recent works have demonstrated the benefits of capturing long-distance dependency in graphs by deeper graph neural networks (GNNs). But deeper GNNs suffer from the long-lasting scalability challenge due to the neighborhood explosion problem in large-
Externí odkaz:
http://arxiv.org/abs/2302.01503
Autor:
Han, Haoyu, Liu, Xiaorui, Mao, Haitao, Torkamani, MohamadAli, Shi, Feng, Lee, Victor, Tang, Jiliang
Graph Neural Networks (GNNs) have greatly advanced the semi-supervised node classification task on graphs. The majority of existing GNNs are trained in an end-to-end manner that can be viewed as tackling a bi-level optimization problem. This process
Externí odkaz:
http://arxiv.org/abs/2206.03638
Most deep neural networks use simple, fixed activation functions, such as sigmoids or rectified linear units, regardless of domain or network structure. We introduce differential equation units (DEUs), an improvement to modern neural networks, which
Externí odkaz:
http://arxiv.org/abs/1909.03069
Most deep neural networks use simple, fixed activation functions, such as sigmoids or rectified linear units, regardless of domain or network structure. We introduce differential equation units (DEUs), an improvement to modern neural networks, which
Externí odkaz:
http://arxiv.org/abs/1905.07685
Autor:
Torkamani, MohamadAli
Machine learning algorithms are invented to learn from data and to use data to perform predictions and analyses. Many agencies are now using machine learning algorithms to present services and to perform tasks that used to be done by humans. These se
Externí odkaz:
http://hdl.handle.net/1794/20677