Titlebook: Artificial Neural Networks and Machine Learning – ICANN 2016; 25th International C Alessandro E.P. Villa,Paolo Masulli,Antonio Javier Confe

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小溪

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

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抱狗不敢前

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

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巩固

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

Gum-Disease

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

Inscrutable

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

magenta

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

Substance

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