Results 11 to 20 of about 792,416 (284)
Deep Residual Reinforcement Learning
, 2019 We revisit residual algorithms in both model-free and model-based reinforcement learning settings. We propose the bidirectional target network technique to stabilize residual algorithms, yielding a residual version of DDPG that significantly outperforms vanilla DDPG in the DeepMind Control Suite benchmark.
Zhang, S, Boehmer, W, Whiteson, S
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Applied Sciences, 2023 Automatic modulation classification (AMC) is a vital process in wireless communication systems that is fundamentally a classification problem. It is employed to automatically determine the type of modulation of a received signal.
Malik Zohaib Nisar +3 more
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Proceedings of the AAAI Conference on Artificial Intelligence, 2020 We propose a novel continual learning method called Residual Continual Learning (ResCL). Our method can prevent the catastrophic forgetting phenomenon in sequential learning of multiple tasks, without any source task information except the original network.
Lee, Janghyeon +3 more
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Residuality and Learning for Nondeterministic Nominal Automata [PDF]
Logical Methods in Computer Science, 2022 We are motivated by the following question: which data languages admit an active learning algorithm? This question was left open in previous work by the authors, and is particularly challenging for languages recognised by nondeterministic automata. To answer it, we develop the theory of residual nominal automata, a subclass of nondeterministic nominal ...
Moerman, Joshua, Sammartino, Matteo
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On Combining CNN With Non-Local Self-Similarity Based Image Denoising Methods
IEEE Access, 2020 Despite the significant advances in convolutional neural network (CNN) based image denoising, the existing methods still cannot consistently outperform non-local self-similarity (NSS) based methods, especially on images with many repetitive structures ...
Zifei Yan +3 more
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Ultrasonic Logging Image Denoising Based on CNN and Feature Attention
IEEE Access, 2021 Various kinds of noise will be produced during the process of ultrasonic logging in high temperature and high-pressure environment under oil wells, which is blurring the logging image.
Su Li +5 more
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IEEE Access, 2023 Automatic modulation classification (AMC) is becoming a promising technique for future adaptive wireless transceiver systems. The existing blind modulation classification (BMC) methods for orthogonal frequency division multiplexing (OFDM) fail to achieve
Anand Kumar +2 more
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RA-UNet: A Hybrid Deep Attention-Aware Network to Extract Liver and Tumor in CT Scans
Frontiers in Bioengineering and Biotechnology, 2020 Automatic extraction of liver and tumor from CT volumes is a challenging task due to their heterogeneous and diffusive shapes. Recently, 2D deep convolutional neural networks have become popular in medical image segmentation tasks because of the ...
Qiangguo Jin +6 more
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Multiscale Recursive Feedback Network for Image Super-Resolution
IEEE Access, 2022 Deep learning-based networks have achieved great success in the field of image super-resolution. However, many networks do not fully combine high-level and low-level information, and fuse local and global information.
Xiao Chen, Chaowen Sun
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Shakedrop Regularization for Deep Residual Learning [PDF]
IEEE Access, 2019 Overfitting is a crucial problem in deep neural networks, even in the latest network architectures. In this paper, to relieve the overfitting effect of ResNet and its improvements (i.e., Wide ResNet, PyramidNet, and ResNeXt), we propose a new regularization method called ShakeDrop regularization.
Yoshihiro Yamada +3 more
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