標(biāo)題: Titlebook: Advances in Neural Networks – ISNN 2019; 16th International S Huchuan Lu,Huajin Tang,Zhanshan Wang Conference proceedings 2019 Springer Nat [打印本頁(yè)] 作者: Stimulant 時(shí)間: 2025-3-21 18:06
書(shū)目名稱Advances in Neural Networks – ISNN 2019影響因子(影響力)
書(shū)目名稱Advances in Neural Networks – ISNN 2019影響因子(影響力)學(xué)科排名
書(shū)目名稱Advances in Neural Networks – ISNN 2019網(wǎng)絡(luò)公開(kāi)度
書(shū)目名稱Advances in Neural Networks – ISNN 2019網(wǎng)絡(luò)公開(kāi)度學(xué)科排名
書(shū)目名稱Advances in Neural Networks – ISNN 2019被引頻次
書(shū)目名稱Advances in Neural Networks – ISNN 2019被引頻次學(xué)科排名
書(shū)目名稱Advances in Neural Networks – ISNN 2019年度引用
書(shū)目名稱Advances in Neural Networks – ISNN 2019年度引用學(xué)科排名
書(shū)目名稱Advances in Neural Networks – ISNN 2019讀者反饋
書(shū)目名稱Advances in Neural Networks – ISNN 2019讀者反饋學(xué)科排名
作者: CRUC 時(shí)間: 2025-3-21 23:59 作者: 背叛者 時(shí)間: 2025-3-22 04:25
Michael P. Johnson,Karen Smilowitzsimulation results demonstrate that stochastic memristor-based CNN performs better on CIFAR-10 dataset when memristive stochasticity is low. This is an encouragement for the engineer of memristor crossbar chip and edge computing application.作者: cumulative 時(shí)間: 2025-3-22 07:23
Linsheng Gu,Mingming Xiang,Yi Lis points whose are in the neighbor of the estimated fingers and outputs a rectify hand pose. We evaluate our method on several famous datasets to prove that our method can get excellent result compared to some most advanced methods.作者: 謙虛的人 時(shí)間: 2025-3-22 08:52
Václav Sná?el,Zdeněk Horák,Milo? Kudělkanetworks are reflected. Based on the simulation results, recommendations are formulated to expand the possibilities of associative signal processing in recurrent neural networks with controlled elements.作者: 壟斷 時(shí)間: 2025-3-22 14:10
Conference proceedings 2019 in Moscow, Russia, in July 2019..The 111 papers presented in the two volumes were carefully reviewed and selected from numerous submissions. The papers were organized in topical sections named: Learning System, Graph Model, and Adversarial Learning; Time Series Analysis, Dynamic Prediction, and Unc作者: 搜尋 時(shí)間: 2025-3-22 21:02
0302-9743 sing, Industrial Application, and Data Generation; Image Recognition, Scene Understanding, and Video Analysis; Bio-signal, Biomedical Engineering, and Hardware...?.978-3-030-22795-1978-3-030-22796-8Series ISSN 0302-9743 Series E-ISSN 1611-3349 作者: 不來(lái) 時(shí)間: 2025-3-22 22:27
https://doi.org/10.1007/978-1-4614-5517-2ision based on learned features. We perform extensive experiments on two standard image classification datasets: CIFAR-10 and CIFAR-100. And results demonstrate that the proposed framework can significantly improve the classification accuracy of a student network.作者: happiness 時(shí)間: 2025-3-23 04:03 作者: 公豬 時(shí)間: 2025-3-23 06:59 作者: Sleep-Paralysis 時(shí)間: 2025-3-23 11:51
https://doi.org/10.1007/978-3-642-19047-6cognition, the result is almost the same as or even slightly higher than the original capsule network, and the reconstructed images also can smooth the noise. This article provides new ideas for the future optimization methods of various capsule networks.作者: NUDGE 時(shí)間: 2025-3-23 17:13
Trust in Online Collaborative ISother semantic information such as the named entity and part of speech information of the word are also added as input data so as to make full use of the words’ information in the corpus. We have conducted some experiments on the widely used datasets, and we got up to 3% improvement in the F1 value compared to previous optimal method.作者: 冬眠 時(shí)間: 2025-3-23 21:04 作者: 可憎 時(shí)間: 2025-3-24 01:38
Community-Based Reconstruction of Society which utilizes graph convolution and self attention mechanism to predict each box as an object or redundant one. We evaluate our method on the VOC2007 dataset. The experimental results show that our method achieves a higher MAP compared with the traditional greedy NMS and the Soft NMS.作者: hurricane 時(shí)間: 2025-3-24 03:42
Graph Database for Collaborative Communitiesility of conventional GAN and has very quick converge speed. We evaluate our proposed method on two multimodal emotion datasets. The experimental results demonstrate that our proposed method achieves 4.6% and 8.9% improvements of mean accuracies on classifying three and five emotions, respectively.作者: 個(gè)人長(zhǎng)篇演說(shuō) 時(shí)間: 2025-3-24 08:48 作者: 過(guò)剩 時(shí)間: 2025-3-24 13:21 作者: 積云 時(shí)間: 2025-3-24 14:53
Community-Based Operations Researchning procedure. The solution is given by an analytical expression, explicitly including the parameters of the problem. The resulting neural network can, if necessary, be retrained according to the usual algorithm. The method is illustrated by the example of solving a particular ordinary second-order differential equation.作者: Explicate 時(shí)間: 2025-3-24 19:06 作者: 乞討 時(shí)間: 2025-3-25 02:07 作者: Peak-Bone-Mass 時(shí)間: 2025-3-25 05:40 作者: arousal 時(shí)間: 2025-3-25 10:34
From Differential Equations to Multilayer Neural Network ModelsOverview: 作者: VEST 時(shí)間: 2025-3-25 12:57
Projectional Learning Laws for Differential Neural Networks Based on Double-Averaged Sub-Gradient DeOverview: 作者: 古代 時(shí)間: 2025-3-25 19:32
Better Performance of Memristive Convolutional Neural Network Due to Stochastic MemristorsOverview: 作者: Junction 時(shí)間: 2025-3-25 21:59 作者: 嬰兒 時(shí)間: 2025-3-26 02:28 作者: 吞吞吐吐 時(shí)間: 2025-3-26 05:35
Graph Convolution and Self Attention Based Non-maximum SuppressionOverview: 作者: 辮子帶來(lái)幫助 時(shí)間: 2025-3-26 12:22 作者: 泄露 時(shí)間: 2025-3-26 12:39
An Improved Capsule Network Based on Newly Reconstructed Network and the Method of Sharing ParameterOverview: 作者: orthodox 時(shí)間: 2025-3-26 19:44 作者: 施魔法 時(shí)間: 2025-3-26 23:03
A GAN-Based Data Augmentation Method for Multimodal Emotion RecognitionOverview: 作者: Pantry 時(shí)間: 2025-3-27 05:10 作者: 無(wú)聊的人 時(shí)間: 2025-3-27 09:14 作者: growth-factor 時(shí)間: 2025-3-27 13:02
https://doi.org/10.1007/978-3-642-19047-6using another adversarial loss. This is beneficial for the main task as it forces FG-SRGAN to learn valid representations for super-resolution. When applied to super-resolve low-resolution face images in the real world, FG-SRGAN is able to achieve satisfactory performance both qualitatively and quan作者: 災(zāi)禍 時(shí)間: 2025-3-27 14:39 作者: 津貼 時(shí)間: 2025-3-27 18:27 作者: SAGE 時(shí)間: 2025-3-27 22:53
Kendra C. Taylor,Erick C. Jonesopagation through time (BPTT), is really slow..In this paper, by separating the LSTM cell into forward and recurrent substructures, we propose a much simpler and faster training method than the BPTT. The deep LSTM is modified by combining the deep RNN with the multilayer perceptron (MLP). The simula作者: 吝嗇性 時(shí)間: 2025-3-28 04:21
Community-Based Operations Research service and necessary to passengers for reducing their waiting time and bus stops and choosing alternative routes. Recently, the same information is used in smart-phone trip planners. In this paper, we explore an LSTM neural network model for bus arrival time prediction. We take into account hetero作者: emission 時(shí)間: 2025-3-28 07:59
Community-Based Operations Researchroposed. The advantage of the method is the possibility of obtaining a neural network model of arbitrarily high accuracy without a time-consuming learning procedure. The solution is given by an analytical expression, explicitly including the parameters of the problem. The resulting neural network ca作者: amyloid 時(shí)間: 2025-3-28 11:52 作者: 無(wú)思維能力 時(shí)間: 2025-3-28 15:57 作者: 歌劇等 時(shí)間: 2025-3-28 18:57
https://doi.org/10.1007/978-1-4614-5517-2toencoder feature selection (RAFS). This method based on autoencoder uses the radial basis function to achieve mapping instead of the weight. We also consider penalty to give a powerful constraint on redundant features. In extensive experiments, our method shows its outperformance in fair comparison作者: LEERY 時(shí)間: 2025-3-29 00:57 作者: 表兩個(gè) 時(shí)間: 2025-3-29 04:35
Community-Based Reconstruction of Societying. Currently, in some cases, this problem is successfully solved by deep neural networks. However, deep models are computationally expensive and so hardly applicable for online learning tasks which require frequent updating of the model. This paper proposes the lightweight neural net architecture 作者: Gossamer 時(shí)間: 2025-3-29 10:31
Community-Based Reconstruction of Society scores. The detection boxes with maximum score are always selected while all other boxes with a sufficient overlap with the preserved boxes are discarded. This strategy is simple and effective. However, there still need some improvements in this process because the algorithm makes a ‘hard’ decision作者: heterogeneous 時(shí)間: 2025-3-29 12:44
Linsheng Gu,Mingming Xiang,Yi Liestimation network for an unordered point cloud. Our approach utilizes EdgeConv layer as the basic element, where an attention embedding version EdgeConv layer is proposed for feature extraction in hand pose estimation task. To improve the result, we design a hand pose improvement network that input作者: 刺耳的聲音 時(shí)間: 2025-3-29 16:54
Community-Based Reconstruction of Societys usually ignored in the high-level feature extraction by the deep learning, which is important for image semantic segmentation. To avoid this problem, we propose a graph model initialized by a fully convolutional network (FCN) named Graph-FCN for image semantic segmentation. Firstly, the image grid作者: 沒(méi)有準(zhǔn)備 時(shí)間: 2025-3-29 19:52
Advances in 21st Century Human SettlementsMany published works apply reinforcement learning or evolutionary algorithm to design the neural architecture for image classification and achieve state-of-the-art performance. However, using NAS to perform other challenging tasks, such as inpainting irregular regions in an image, has not been explo作者: 堅(jiān)毅 時(shí)間: 2025-3-30 00:47 作者: 兇殘 時(shí)間: 2025-3-30 06:05 作者: Dorsal-Kyphosis 時(shí)間: 2025-3-30 10:16
Václav Sná?el,Zdeněk Horák,Milo? Kudělkang of signals are refined. A number of the space-time structures of such networks are analyzed. Among them, the neural networks with one-, two-, and three-level structures of layers are investigated. The results of studies on the stability of the associative processing of distorted signals by these 作者: nerve-sparing 時(shí)間: 2025-3-30 13:02
Graph Database for Collaborative Communitiesy shown great successes in generating realistic-like data. In this paper, we propose a GAN-based data augmentation method for enhancing the performance of multimodal emotion recognition models. We adopt conditional Boundary Equilibrium GAN (cBEGAN) to generate artificial differential entropy feature作者: 未開(kāi)化 時(shí)間: 2025-3-30 20:07 作者: Juvenile 時(shí)間: 2025-3-30 22:44 作者: 勾引 時(shí)間: 2025-3-31 02:30 作者: GIBE 時(shí)間: 2025-3-31 07:18 作者: Limited 時(shí)間: 2025-3-31 10:50 作者: OUTRE 時(shí)間: 2025-3-31 14:30
