Zobrazeno 1 - 10
of 749
pro vyhledávání: '"adversarial regularization"'
Autor:
Bukharin, Alexander, Li, Yan, Yu, Yue, Zhang, Qingru, Chen, Zhehui, Zuo, Simiao, Zhang, Chao, Zhang, Songan, Zhao, Tuo
Multi-Agent Reinforcement Learning (MARL) has shown promising results across several domains. Despite this promise, MARL policies often lack robustness and are therefore sensitive to small changes in their environment. This presents a serious concern
Externí odkaz:
http://arxiv.org/abs/2310.10810
Survival Analysis (SA) models the time until an event occurs, with applications in fields like medicine, defense, finance, and aerospace. Recent research indicates that Neural Networks (NNs) can effectively capture complex data patterns in SA, wherea
Externí odkaz:
http://arxiv.org/abs/2312.16019
Split Learning (SL) has emerged as a practical and efficient alternative to traditional federated learning. While previous attempts to attack SL have often relied on overly strong assumptions or targeted easily exploitable models, we seek to develop
Externí odkaz:
http://arxiv.org/abs/2310.10483
Autor:
Zuo, Simiao, Liang, Chen, Jiang, Haoming, Liu, Xiaodong, He, Pengcheng, Gao, Jianfeng, Chen, Weizhu, Zhao, Tuo
Adversarial regularization has been shown to improve the generalization performance of deep learning models in various natural language processing tasks. Existing works usually formulate the method as a zero-sum game, which is solved by alternating g
Externí odkaz:
http://arxiv.org/abs/2104.04886
Publikováno v:
IEEE Robotics and Automation Letters (RA-L 2021) & IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS 2021)
Semantic segmentation is one of the basic, yet essential scene understanding tasks for an autonomous agent. The recent developments in supervised machine learning and neural networks have enjoyed great success in enhancing the performance of the stat
Externí odkaz:
http://arxiv.org/abs/2107.00691
Autor:
Boutillon, Arnaud, Borotikar, Bhushan, Pons, Christelle, Burdin, Valérie, Conze, Pierre-Henri
Automatic segmentation of the musculoskeletal system in pediatric magnetic resonance (MR) images is a challenging but crucial task for morphological evaluation in clinical practice. We propose a deep learning-based regularized segmentation method for
Externí odkaz:
http://arxiv.org/abs/2101.10173
Autor:
Rout, Litu
Despite numerous attempts sought to provide empirical evidence of adversarial regularization outperforming sole supervision, the theoretical understanding of such phenomena remains elusive. In this study, we aim to resolve whether adversarial regular
Externí odkaz:
http://arxiv.org/abs/2010.00522
Publikováno v:
ICLR 2022 (https://openreview.net/forum?id=-8sBpe7rDiV)
Neuromorphic neural network processors, in the form of compute-in-memory crossbar arrays of memristors, or in the form of subthreshold analog and mixed-signal ASICs, promise enormous advantages in compute density and energy efficiency for NN-based ML
Externí odkaz:
http://arxiv.org/abs/2106.05009
Akademický článek
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Adversarial training is a common approach to improving the robustness of deep neural networks against adversarial examples. In this work, we propose a novel regularization approach as an alternative. To derive the regularizer, we formulate the advers
Externí odkaz:
http://arxiv.org/abs/2004.01832