標(biāo)題: Titlebook: Artificial Neural Networks and Machine Learning – ICANN 2016; 25th International C Alessandro E.P. Villa,Paolo Masulli,Antonio Javier Confe [打印本頁(yè)] 作者: 娛樂(lè)某人 時(shí)間: 2025-3-21 19:02
書目名稱Artificial Neural Networks and Machine Learning – ICANN 2016影響因子(影響力)
書目名稱Artificial Neural Networks and Machine Learning – ICANN 2016影響因子(影響力)學(xué)科排名
書目名稱Artificial Neural Networks and Machine Learning – ICANN 2016網(wǎng)絡(luò)公開度
書目名稱Artificial Neural Networks and Machine Learning – ICANN 2016網(wǎng)絡(luò)公開度學(xué)科排名
書目名稱Artificial Neural Networks and Machine Learning – ICANN 2016被引頻次
書目名稱Artificial Neural Networks and Machine Learning – ICANN 2016被引頻次學(xué)科排名
書目名稱Artificial Neural Networks and Machine Learning – ICANN 2016年度引用
書目名稱Artificial Neural Networks and Machine Learning – ICANN 2016年度引用學(xué)科排名
書目名稱Artificial Neural Networks and Machine Learning – ICANN 2016讀者反饋
書目名稱Artificial Neural Networks and Machine Learning – ICANN 2016讀者反饋學(xué)科排名
作者: Transfusion 時(shí)間: 2025-3-21 20:37 作者: 模仿 時(shí)間: 2025-3-22 04:10 作者: 未開化 時(shí)間: 2025-3-22 06:09
Integration of Unsupervised and Supervised Criteria for Deep Neural Networks Trainingg encourage the incorporation of this idea into on-line learning approaches. The interest of this method in time-series forecasting is motivated by the study of predictive models for domotic houses with intelligent control systems.作者: Anticlimax 時(shí)間: 2025-3-22 10:54
The Effects of Regularization on Learning Facial Expressions with Convolutional Neural NetworksN, almost halving its validation error. A visualization technique is applied to the CNNs to highlight their activations for different inputs, illustrating a significant difference between a standard CNN and a regularized CNN.作者: animated 時(shí)間: 2025-3-22 16:45 作者: flavonoids 時(shí)間: 2025-3-22 20:19
https://doi.org/10.1007/978-3-322-95653-8grating spatial and temporal tactile sensor data from a piezo-resistive sensor array through deep learning techniques, the network is not only able to classify the contact state into stable versus slipping, but also to distinguish between rotational and translation slippage. We evaluated different n作者: 使絕緣 時(shí)間: 2025-3-22 22:32 作者: 土產(chǎn) 時(shí)間: 2025-3-23 03:48 作者: labile 時(shí)間: 2025-3-23 08:08
https://doi.org/10.1007/978-3-658-05036-8sults for a deeply-trained model for emotion recognition through the use of facial expression images. We explore two Convolutional Neural Network (CNN) architectures that offer automatic feature extraction and representation, followed by fully connected softmax layers to classify images into seven e作者: Intellectual 時(shí)間: 2025-3-23 13:29 作者: bibliophile 時(shí)間: 2025-3-23 15:38 作者: Fabric 時(shí)間: 2025-3-23 18:31 作者: Fulsome 時(shí)間: 2025-3-23 23:08 作者: ARCHE 時(shí)間: 2025-3-24 03:07
,Wertsch?pfungsketten und Gesch?ftsmodelle,NNs possessing many layers and providing a good internal representation of the learned data. Due to the potentially high complexity of CNNs on the other hand they are prone to overfitting and as a result, regularization techniques are needed to improve the performance and minimize overfitting. Howev作者: BIPED 時(shí)間: 2025-3-24 07:26
https://doi.org/10.1007/978-3-531-90097-1ge regarding the rules of chess, a deep neural network is trained using a combination of unsupervised pretraining and supervised training. The unsupervised training extracts high level features from a given position, and the supervised training learns to compare two chess positions and select the mo作者: 一回合 時(shí)間: 2025-3-24 13:35
,Wertsch?pfungsketten und Gesch?ftsmodelle,eature maps with increasing specificity and invariance along feedforward paths. The present study explores the possibility of specifically training convolutional networks to resemble the primate cortex more closely. In particular, in addition to supervised learning to minimize an output error functi作者: 糾纏 時(shí)間: 2025-3-24 15:35 作者: 解脫 時(shí)間: 2025-3-24 22:59 作者: 一個(gè)姐姐 時(shí)間: 2025-3-25 02:33 作者: Indicative 時(shí)間: 2025-3-25 06:59
,Wertsch?pfungsketten und Gesch?ftsmodelle,oblem of standardizing constitutional classification has become a constraint on the development of Chinese medical constitution. Traditional recognition methods, such as questionnaire and medical examination have the shortcoming of inefficiency and low accuracy. We present an advanced deep convoluti作者: 原始 時(shí)間: 2025-3-25 08:25 作者: Keratin 時(shí)間: 2025-3-25 11:45
Herausforderungen und Perspektiven,g the flow of information through neural networks (Fields et al. 2015 [.]). There are strong experimental evidences that glia are responsible for synaptic meta-plasticity. Synaptic plasticity is the modification of the strength of connections between neurons. Meta-plasticity, i.e. plasticity of syna作者: 疏遠(yuǎn)天際 時(shí)間: 2025-3-25 19:08 作者: diskitis 時(shí)間: 2025-3-25 20:39
,Wertsch?pfungsketten und Gesch?ftsmodelle,works and using Dynamic Time Warping for word scoring. Features are learned from word images, in an unsupervised manner, using a sliding window to extract horizontal patches. For two single writer historical data sets, it is shown that the proposed learned feature extractor outperforms two standard sets of features.作者: 構(gòu)成 時(shí)間: 2025-3-26 01:25 作者: 狂亂 時(shí)間: 2025-3-26 05:00 作者: Silent-Ischemia 時(shí)間: 2025-3-26 11:00 作者: 變量 時(shí)間: 2025-3-26 13:38
Lecture Notes in Computer Sciencehttp://image.papertrans.cn/b/image/162637.jpg作者: ETCH 時(shí)間: 2025-3-26 18:07
Keyword Spotting with Convolutional Deep Belief Networks and Dynamic Time Warpingworks and using Dynamic Time Warping for word scoring. Features are learned from word images, in an unsupervised manner, using a sliding window to extract horizontal patches. For two single writer historical data sets, it is shown that the proposed learned feature extractor outperforms two standard sets of features.作者: critic 時(shí)間: 2025-3-26 21:37 作者: 小溪 時(shí)間: 2025-3-27 03:48
Tactile Convolutional Networks for Online Slip and Rotation Detectiongrating spatial and temporal tactile sensor data from a piezo-resistive sensor array through deep learning techniques, the network is not only able to classify the contact state into stable versus slipping, but also to distinguish between rotational and translation slippage. We evaluated different n作者: 無(wú)目標(biāo) 時(shí)間: 2025-3-27 08:01 作者: 抱狗不敢前 時(shí)間: 2025-3-27 10:44
Revisiting Deep Convolutional Neural Networks for RGB-D Based Object RecognitionNNs are pretrained on a large-scale RGB database and just fine-tuned to process colorized depth images is taken up and extended. We introduce and analyse multiple solutions to improve depth colorization and propose a new method for depth colorization based on surface normals. We show that our improv作者: TRACE 時(shí)間: 2025-3-27 16:42 作者: 鞏固 時(shí)間: 2025-3-27 19:27
Extracting Muscle Synergy Patterns from EMG Data Using Autoencodersor related research. Due to the linear nature of the methods commonly used for extracting muscle synergies, those methods fail to represent agonist-antagonist muscle relationships in the extracted synergies. In this paper, we propose to use a special type of neural networks, called autoencoders, for作者: Gum-Disease 時(shí)間: 2025-3-28 01:05
Integration of Unsupervised and Supervised Criteria for Deep Neural Networks Training showing that deep models improve the performance of shallow ones in some areas like signal processing, signal classification or signal segmentation, whatever type of signals, e.g. video, audio or images. One of the most important methods is greedy layer-wise unsupervised pre-training followed by a 作者: Inscrutable 時(shí)間: 2025-3-28 04:41
Layer-Wise Relevance Propagation for Neural Networks with Local Renormalization Layerse, down to relevance scores for the single input dimensions of the sample such as subpixels of an image. While this approach can be applied directly to generalized linear mappings, product type non-linearities are not covered. This paper proposes an approach to extend layer-wise relevance propagatio作者: magenta 時(shí)間: 2025-3-28 07:27
Analysis of Dropout Learning Regarded as Ensemble Learning huge number of units, and connections. Therefore, overfitting is a serious problem. To avoid this problem, dropout learning is proposed. Dropout learning neglects some inputs and hidden units in the learning process with a probability, ., and then, the neglected inputs and hidden units are combined作者: Substance 時(shí)間: 2025-3-28 14:11
The Effects of Regularization on Learning Facial Expressions with Convolutional Neural NetworksNNs possessing many layers and providing a good internal representation of the learned data. Due to the potentially high complexity of CNNs on the other hand they are prone to overfitting and as a result, regularization techniques are needed to improve the performance and minimize overfitting. Howev作者: 整理 時(shí)間: 2025-3-28 15:19 作者: audiologist 時(shí)間: 2025-3-28 21:16 作者: Throttle 時(shí)間: 2025-3-29 02:31 作者: intention 時(shí)間: 2025-3-29 07:00
Keyword Spotting with Convolutional Deep Belief Networks and Dynamic Time Warpingworks and using Dynamic Time Warping for word scoring. Features are learned from word images, in an unsupervised manner, using a sliding window to extract horizontal patches. For two single writer historical data sets, it is shown that the proposed learned feature extractor outperforms two standard 作者: 同來(lái)核對(duì) 時(shí)間: 2025-3-29 07:33
Computational Advantages of Deep Prototype-Based Learningdel but at a fraction of the computational cost, especially w.r.t. memory requirements. As prototype-based classification and regression methods are typically plagued by the exploding number of prototypes necessary to solve complex problems, this is an important step towards efficient prototype-base作者: 沙發(fā) 時(shí)間: 2025-3-29 15:00
Deep Convolutional Neural Networks for Classifying Body Constitutionoblem of standardizing constitutional classification has become a constraint on the development of Chinese medical constitution. Traditional recognition methods, such as questionnaire and medical examination have the shortcoming of inefficiency and low accuracy. We present an advanced deep convoluti作者: Comprise 時(shí)間: 2025-3-29 19:08
Feature Extractor Based Deep Method to Enhance Online Arabic Handwritten Recognition Systemit handcrafted features based on beta-elliptic model and automatic features using deep classifier called Convolutional Deep Belief Network (CDBN). The experiments are conducted on two different Arabic databases: LMCA and ADAB databases which including respectively isolated characters and Tunisian na作者: AV-node 時(shí)間: 2025-3-29 20:03 作者: Hemodialysis 時(shí)間: 2025-3-30 00:44 作者: absorbed 時(shí)間: 2025-3-30 05:10
Tactile Convolutional Networks for Online Slip and Rotation Detectionetwork layouts and reached a final classification rate of more than 97?%. Using consumer class GPUs, slippage and rotation events can be detected within 10?ms, which is still feasible for adaptive grasp control.作者: 構(gòu)想 時(shí)間: 2025-3-30 09:27 作者: 蜿蜒而流 時(shí)間: 2025-3-30 14:05 作者: 繁榮中國(guó) 時(shí)間: 2025-3-30 18:37 作者: Accomplish 時(shí)間: 2025-3-30 21:28
Analysis of Dropout Learning Regarded as Ensemble Learning with the learned network to express the final output. We find that the process of combining the neglected hidden units with the learned network can be regarded as ensemble learning, so we analyze dropout learning from this point of view.作者: 含鐵 時(shí)間: 2025-3-31 04:53
A Convolutional Network Model of the Primate Middle Temporal Areaon, a deep layer is directly trained to approximate primate electrophysiology data. This method is used to develop a model of the macaque monkey dorsal stream that estimates heading and speed from visual input.作者: 輕推 時(shí)間: 2025-3-31 06:36 作者: FOLLY 時(shí)間: 2025-3-31 11:41 作者: Incise 時(shí)間: 2025-3-31 17:22 作者: 閃光你我 時(shí)間: 2025-3-31 19:17 作者: ARIA 時(shí)間: 2025-3-31 21:49
Radio und Fernsehen in der Schweiz, results also indicate that classification using only surface normals without RGB images outperforms classification using pure RGB images, which is to our knowledge a novel discovery in the field of DCNNs.作者: FAWN 時(shí)間: 2025-4-1 04:58
https://doi.org/10.1007/978-3-658-05036-8 into two streams based on eye and mouth positions. The first proposed architecture produces state of the art results with an accuracy rate of 96.93?% and the second architecture with split input produces an average accuracy rate of 86.73?%, respectively.
