標題: Titlebook: Artificial Neural Networks and Machine Learning – ICANN 2020; 29th International C Igor Farka?,Paolo Masulli,Stefan Wermter Conference proc [打印本頁] 作者: 預兆前 時間: 2025-3-21 17:36
書目名稱Artificial Neural Networks and Machine Learning – ICANN 2020影響因子(影響力)
書目名稱Artificial Neural Networks and Machine Learning – ICANN 2020影響因子(影響力)學科排名
書目名稱Artificial Neural Networks and Machine Learning – ICANN 2020網(wǎng)絡公開度
書目名稱Artificial Neural Networks and Machine Learning – ICANN 2020網(wǎng)絡公開度學科排名
書目名稱Artificial Neural Networks and Machine Learning – ICANN 2020被引頻次
書目名稱Artificial Neural Networks and Machine Learning – ICANN 2020被引頻次學科排名
書目名稱Artificial Neural Networks and Machine Learning – ICANN 2020年度引用
書目名稱Artificial Neural Networks and Machine Learning – ICANN 2020年度引用學科排名
書目名稱Artificial Neural Networks and Machine Learning – ICANN 2020讀者反饋
書目名稱Artificial Neural Networks and Machine Learning – ICANN 2020讀者反饋學科排名
作者: 抵押貸款 時間: 2025-3-21 21:40
Wilhelm Dangelmaier,Hans-Jürgen Warneckehich own preeminent recoverability, predictability and interpretability. By simultaneously learning two dictionary pairs, the feature space and label space are well bi-directly bridged and recovered by four dictionaries. Experiments on benchmark datasets show that QDL outperforms the state-of-the-art label space dimension reduction algorithms.作者: ADAGE 時間: 2025-3-22 04:15
Statistische Prozessregelung (SPC), through a graph Laplacian regularization. We write the primal problem of this formulation and derive its dual problem, which is shown to be equivalent to a standard SVM dual using a particular kernel choice. Empirical results over different regression and classification problems support the usefulness of our proposal.作者: breadth 時間: 2025-3-22 06:27 作者: Popcorn 時間: 2025-3-22 10:47
Multi-label Quadruplet Dictionary Learninghich own preeminent recoverability, predictability and interpretability. By simultaneously learning two dictionary pairs, the feature space and label space are well bi-directly bridged and recovered by four dictionaries. Experiments on benchmark datasets show that QDL outperforms the state-of-the-art label space dimension reduction algorithms.作者: Cerebrovascular 時間: 2025-3-22 16:38 作者: ELATE 時間: 2025-3-22 20:03 作者: 不安 時間: 2025-3-23 00:06 作者: admission 時間: 2025-3-23 03:58
,F?rdern und Speichern von Arbeitsgut, least, and obtain the accuracy of 99.72% and 98.74% on benchmark defect datasets, DAGM 2007 and KolektorSDD, respectively, outperforming all the baselines. In addition, our model can process the images with different sizes, which is verified on the RSDDs with the accuracy of 97.00%.作者: Presbyopia 時間: 2025-3-23 06:13 作者: 煉油廠 時間: 2025-3-23 11:27 作者: Isometric 時間: 2025-3-23 17:42
Neural Network Compression via?Learnable Wavelet Transformsers of RNNs. Our wavelet compressed RNNs have significantly fewer parameters yet still perform competitively with the state-of-the-art on synthetic and real-world RNN benchmarks (Source code is available at .). Wavelet optimization adds basis flexibility, without large numbers of extra weights.作者: 輕快走過 時間: 2025-3-23 20:50
0302-9743 sis, cognitive models, neural network theory and information theoretic learning, and robotics and neural models of perception and action...*The conference was postponed to 2021 due to the COVID-19 pandemic..978-3-030-61615-1978-3-030-61616-8Series ISSN 0302-9743 Series E-ISSN 1611-3349 作者: Substitution 時間: 2025-3-23 23:03 作者: 銼屑 時間: 2025-3-24 05:34 作者: 傳授知識 時間: 2025-3-24 09:02 作者: Narrative 時間: 2025-3-24 10:51
,Zusammenfassung und Schluβfolgerungen, any algorithm achieving depth compression of neural networks. In particular, we show that depth compression is as hard as learning the input distribution, ruling out guarantees for most existing approaches. Furthermore, even when the input distribution is of a known, simple form, we show that there are no . algorithms for depth compression.作者: filicide 時間: 2025-3-24 15:42
Glossar, Begriffe und Definitionen,ect of the former uncertainty-based methods. Experiments are made on CIFAR-10 and CIFAR-100, and the results indicates that prediction stability was effective and works well on fewer-labeled datasets. Prediction stability reaches the accuracy of traditional acquisition functions like entropy on CIFAR-10, and notably outperformed them on CIFAR-100.作者: 蔓藤圖飾 時間: 2025-3-24 21:42 作者: Cardiac-Output 時間: 2025-3-25 00:05
Pruning Artificial Neural Networks: A Way to Find Well-Generalizing, High-Entropy Sharp Minimaroaches. In this work we also propose PSP-entropy, a measure to understand how a given neuron correlates to some specific learned classes. Interestingly, we observe that the features extracted by iteratively-pruned models are less correlated to specific classes, potentially making these models a better fit in transfer learning approaches.作者: 不如屎殼郎 時間: 2025-3-25 06:44 作者: 空洞 時間: 2025-3-25 10:32
Obstacles to Depth Compression of?Neural Networks any algorithm achieving depth compression of neural networks. In particular, we show that depth compression is as hard as learning the input distribution, ruling out guarantees for most existing approaches. Furthermore, even when the input distribution is of a known, simple form, we show that there are no . algorithms for depth compression.作者: 勉強 時間: 2025-3-25 15:32
Prediction Stability as a Criterion in Active Learningect of the former uncertainty-based methods. Experiments are made on CIFAR-10 and CIFAR-100, and the results indicates that prediction stability was effective and works well on fewer-labeled datasets. Prediction stability reaches the accuracy of traditional acquisition functions like entropy on CIFAR-10, and notably outperformed them on CIFAR-100.作者: effrontery 時間: 2025-3-25 18:26 作者: 言外之意 時間: 2025-3-25 20:57
Lecture Notes in Computer Sciencehttp://image.papertrans.cn/b/image/162650.jpg作者: 敵手 時間: 2025-3-26 01:23
https://doi.org/10.1007/978-3-030-61616-8artificial intelligence; classification; computational linguistics; computer networks; computer vision; i作者: Geyser 時間: 2025-3-26 07:21 作者: 金哥占卜者 時間: 2025-3-26 11:01
Log-Nets: Logarithmic Feature-Product Layers Yield More Compact Networksions. Log-Nets are capable of surpassing the performance of traditional convolutional neural networks (CNNs) while using fewer parameters. Performance is evaluated on the Cifar-10 and ImageNet benchmarks.作者: 人充滿活力 時間: 2025-3-26 12:52
Artificial Neural Networks and Machine Learning – ICANN 2020978-3-030-61616-8Series ISSN 0302-9743 Series E-ISSN 1611-3349 作者: 委派 時間: 2025-3-26 17:47
,Einführung von Fertigungsinseln,ions. Log-Nets are capable of surpassing the performance of traditional convolutional neural networks (CNNs) while using fewer parameters. Performance is evaluated on the Cifar-10 and ImageNet benchmarks.作者: Intellectual 時間: 2025-3-26 21:42 作者: 瑣碎 時間: 2025-3-27 03:56
,F?rdern und Speichern von Arbeitsgut,ep Neural Network (DNN). However, because it takes a long time to sample DNN’s output for calculating its distribution, it is difficult to apply it to edge computing where resources are limited. Thus, this research proposes a method of reducing a sampling time required for MC Dropout in edge computi作者: 流利圓滑 時間: 2025-3-27 08:46 作者: 不法行為 時間: 2025-3-27 12:32
https://doi.org/10.1007/978-3-642-83955-9 and computing resources are required in the commonly used CNN models, posing challenges in training as well as deploying, especially on those devices with limited computational resources. Inspired by the recent advancement of random tensor decomposition, we introduce a Hierarchical Framework for Fa作者: 不持續(xù)就爆 時間: 2025-3-27 16:35
Siegfried Hildebrand,Werner Krauseh minimal or no performance loss. However, there is a general lack in understanding why these pruning strategies are effective. In this work, we are going to compare and analyze pruned solutions with two different pruning approaches, one-shot and gradual, showing the higher effectiveness of the latt作者: abnegate 時間: 2025-3-27 18:31 作者: happiness 時間: 2025-3-28 01:47
Fertigungsinseln in CIM-Strukturenp Learning, also enabled by the availability of Automated Machine Learning and Neural Architecture Search solutions, the computational requirements of the optimization of the structure and the hyperparameters of Deep Neural Networks usually far exceed what is available on tiny systems. Therefore, th作者: 傻 時間: 2025-3-28 03:15
,Zusammenfassung und Schluβfolgerungen, the cost of evaluating a model grows with the size, it is desirable to obtain an equivalent compressed neural network model before deploying it for prediction. The best-studied tools for compressing neural networks obtain models with broadly similar architectures, including the depth of the model. 作者: Polydipsia 時間: 2025-3-28 08:06
Wilhelm Dangelmaier,Hans-Jürgen Warneckeped to reduce the dimension of the label space by learning a latent representation of both the feature space and label space. Almost all existing models adopt a two-step strategy, i.e., first learn the latent space, and then connect the feature space with the label space by the latent space. Additio作者: 性行為放縱者 時間: 2025-3-28 13:42 作者: extinguish 時間: 2025-3-28 15:58
Statistische Prozessregelung (SPC),this idea in two main ways: by using a combination of common and task-specific parts, or by fitting individual models adding a graph Laplacian regularization that defines different degrees of task relationships. The first approach is too rigid since it imposes the same relationship among all tasks. 作者: anarchist 時間: 2025-3-28 21:30
Glossar, Begriffe und Definitionen,sible solution. Besides the previous active learning algorithms that only adopted information after training, we propose a new class of methods named sequential-based method based on the information during training. A specific criterion of active learning called prediction stability is proposed to p作者: 多樣 時間: 2025-3-29 02:33 作者: 解脫 時間: 2025-3-29 06:21
,Berührungslos/optische Messverfahren,linear Fokker-Planck dynamics constitutes one of the main mechanisms that can generate .-maximum entropy distributions. In the present work, we investigate a nonlinear Fokker-Planck equation associated with general, continuous, neural network dynamical models for associative memory. These models adm作者: Seizure 時間: 2025-3-29 07:36
Detecting Uncertain BNN Outputs on?FPGA Using Monte Carlo Dropout Samplinghad not learned as “uncertain” on a classification identification problem of the image on an FPGA. Furthermore, for 20 units in parallel, the amount of increase in the circuit scale was only 2–3 times that of non-parallelized circuits. In terms of inference speed, parallelization of dropout circuits作者: 為現(xiàn)場 時間: 2025-3-29 13:48
Pareto Multi-task Deep Learningnderlying training dynamics. The experimental results show that a neural network trained with the proposed evolution strategy can outperform networks individually trained respectively on each of the tasks.作者: Neonatal 時間: 2025-3-29 18:26 作者: 注視 時間: 2025-3-29 20:07
Fine-Grained Channel Pruning for Deep Residual Neural Networks作者: anatomical 時間: 2025-3-30 03:07
Artificial Neural Networks and Machine Learning – ICANN 202029th International C作者: Jejune 時間: 2025-3-30 07:02
,F?rdern und Speichern von Arbeitsgut,had not learned as “uncertain” on a classification identification problem of the image on an FPGA. Furthermore, for 20 units in parallel, the amount of increase in the circuit scale was only 2–3 times that of non-parallelized circuits. In terms of inference speed, parallelization of dropout circuits作者: Somber 時間: 2025-3-30 08:52 作者: cluster 時間: 2025-3-30 15:45
,Berührungslos/optische Messverfahren,unov function of the network model, the deterministic equations of motion (phase-space flow) of the network, and the form of the diffusion coefficients appearing in the nonlinear Fokker-Planck equations. This, in turn, leads to an .-theorem involving a free energy-like functional related to the . en作者: Glaci冰 時間: 2025-3-30 19:31
A Lightweight Fully Convolutional Neural Network of High Accuracy Surface Defect Detection improving accuracy. However, it is difficult to apply in real situation, because of huge number of parameters and the strict hardware requirements. In this paper, a lightweight fully convolutional neural network, named LFCSDD, is proposed. The parameters of our model are 11x fewer than baselines at作者: 誤傳 時間: 2025-3-30 23:40
Detecting Uncertain BNN Outputs on?FPGA Using Monte Carlo Dropout Samplingep Neural Network (DNN). However, because it takes a long time to sample DNN’s output for calculating its distribution, it is difficult to apply it to edge computing where resources are limited. Thus, this research proposes a method of reducing a sampling time required for MC Dropout in edge computi作者: 從屬 時間: 2025-3-31 03:45 作者: Gorilla 時間: 2025-3-31 06:19 作者: CRUDE 時間: 2025-3-31 10:42 作者: CESS 時間: 2025-3-31 17:25
