Results 11 to 20 of about 218,844 (266)
Deep learning for protein secondary structure prediction: Pre and post-AlphaFold
Computational and Structural Biotechnology Journal, 2022 This paper aims to provide a comprehensive review of the trends and challenges of deep neural networks for protein secondary structure prediction (PSSP). In recent years, deep neural networks have become the primary method for protein secondary structure
Dewi Pramudi Ismi +2 more
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Tweaking Deep Neural Networks [PDF]
IEEE Transactions on Pattern Analysis and Machine Intelligence, 2021 Deep neural networks are trained so as to achieve a kind of the maximum overall accuracy through a learning process using given training data. Therefore, it is difficult to fix them to improve the accuracies of specific problematic classes or classes of interest that may be valuable to some users or applications.
Jinwook Kim +2 more
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Distributed training method for deep neural networks
Dianzi Jishu Yingyong, 2023 ： Deep neural networks have achieved great success in classification and prediction of high-dimensional data. Training deep neural networks is a data-intensive task, which needs to collect large-scale data from multiple data sources.
Yuan Ye, Tian Yuan, Jiang Qibing
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Topology of deep neural networks
J. Mach. Learn. Res., 2020 We study how the topology of a data set $M = M_a \cup M_b \subseteq \mathbb{R}^d$, representing two classes $a$ and $b$ in a binary classification problem, changes as it passes through the layers of a well-trained neural network, i.e., with perfect accuracy on training set and near-zero generalization error ($\approx 0.01\%$). The goal is to shed light
Gregory Naitzat +2 more
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IEEE Access, 2020 By the development of social media, sentiment analysis has changed to one of the most remarkable research topics in the field of natural language processing which tries to dig information from textual data containing users' opinions or attitudes toward a
Hossein Sadr +2 more
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Estimation and application of matrix eigenvalues based on deep neural network
Journal of Intelligent Systems, 2022 In today’s era of rapid development in science and technology, the development of digital technology has increasingly higher requirements for data processing functions.
Hu Zhiying
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Orthogonal Deep Neural Networks [PDF]
IEEE Transactions on Pattern Analysis and Machine Intelligence, 2021 In this paper, we introduce the algorithms of Orthogonal Deep Neural Networks (OrthDNNs) to connect with recent interest of spectrally regularized deep learning methods. OrthDNNs are theoretically motivated by generalization analysis of modern DNNs, with the aim to find solution properties of network weights that guarantee better generalization.
Shuai Li +4 more
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Fast Sparse Deep Neural Networks: Theory and Performance Analysis
IEEE Access, 2019 In this paper, fast sparse deep neural networks that aim to offer an alternative way of learning in a deep structure are proposed. We examine some optimization algorithms for traditional deep neural networks and find that deep neural networks suffer from
Jin Zhao, Licheng Jiao
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Statistical physics of deep neural networks: Initialization toward optimal channels
Physical Review Research, 2023 In deep learning, neural networks serve as noisy channels between input data and its latent representation. This perspective naturally relates deep learning with the pursuit of constructing channels with optimal performance in information transmission ...
Kangyu Weng +4 more
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Evolving Deep Neural Networks [PDF]
, 2019 The success of deep learning depends on finding an architecture to fit the task. As deep learning has scaled up to more challenging tasks, the architectures have become difficult to design by hand. This paper proposes an automated method, CoDeepNEAT, for optimizing deep learning architectures through evolution.
Risto Miikkulainen +10 more
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