Deep Q-Learning Networks
af en Deep Learning in Spiking Neural Networks Deep learning in spiking neural networks
... years, deep learning has revolutionized the field of machine learning, for computer vision in ...a deep (multilayer) artificial neural network (ANN) is trained in a supervised manner using ...
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Deep neural networks are lazy : on the inductive bias of deep learning
... for Deep Learning As in the Bayesian case, priors can be a big determinant in the models’ successes or ...Neural Networks (CNNs) on image classification and object recognition ...although deep ...
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Active learning and input space analysis for deep networks
... active learning criterion for deep ...a deep network. This information is relevant in active learning and known as margin- based active ...active learning, we assume that the decision ...
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Efficient FPGA-Based Inference Architectures for Deep Learning Networks
... Published in: IEEE International Conference on ReConFigurable Computing and FPGAs (ReConFig) 2018 [11] Abstract–Deep Neural Networks (DNNs) have gained significant popularity in several classification and ...
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Stabilizing and Enhancing Learning for Deep Complex and Real Neural Networks
... neural networks (FFNNs) such as CNNs and real-valued RNNs have shown to excel in a wide variety of applications and learning ...neural networks are characterized by a severe problem when training is ...
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Learning and Recognizing Human Action from Skeleton Movement with Deep Residual Neural Networks
... years, deep learning based approaches achieved outstanding results in image recognition and ...Among deep learning-based models for action recognition, Convolutional Neural Networks ...
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Distribution-Based Invariant Deep Networks for Learning Meta-Features
... for learning from point clouds subject to permutation invariance or ...invariant deep architecture (Dida) extends [23] as it handles (discrete or continuous) probability distributions instead of point ...
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Learning Sparse deep neural networks using efficient structured projections on convex constraints for green AI
... OTIVATION Deep neural networks have been applied recently to different domains and have shown a dramatic improvement in accuracy of image recognition [1], speech recognition [2] or natural language ...
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DeepCog: Cognitive Network Management in Sliced 5G Networks with Deep Learning
... 5G networks where the network infrastructure is divided into slices devoted to different services and customized to their ...a deep learning architecture that is explicitly designed for capacity ...
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Bidirectional Joint Representation Learning with Symmetrical Deep Neural Networks for Multimodal and Crossmodal Applications
... bidirectional learning with deep neural networks with forced ...of learning crossmodal mappings in both directions while applying restrictions to force sym- metry in deep neural ...
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Learning Activation Functions in Deep Neural Networks
... of deep neural networks (deep learning) achieved considerable success in pattern recognition and text ...of deep learning on images, video or text classification, the application ...
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Deep Predictive Coding Networks for Video Prediction and Unsupervised Learning
... using deep learning algorithms to solve supervised learning tasks, the problem of unsupervised learning — leveraging un- labeled examples to learn about the structure of a domain — remains a ...
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Learning Sparse Filters In Deep Convolutional Neural Networks With A l 1 /l 2 Pseudo-Norm
... numerous networks compression algorithms using prun- ing methods and achieving state-of-the-art results have ...versarial learning method (GAL) (not only to remove filters, but also branches and ...
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Extensive deep neural networks for transferring small scale learning to large scale systems
... The ability of an EDNN to learn to ignore, or compensate for redundant context can be explored by measuring the perfor- mance of the model when information is partially obfuscated through the application of a Gaussian ...
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Deep neural networks with transfer learning in millet crop images
... Python Deep Learning Library, (2015) ...Tangara, Deep convolution neural network for image recognition, Ecological Informatics 48 (November) (2018) ...and Deep Learning techniques and ...
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Deep learning for cloud detection
... Conclusion Deep learning offers the possibility to build really complex and robust ...convolutional networks are faster to ...lutional networks for cloud detection and to compare the resulting ...
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Deep Learning for Video Modelling
... 4.3 Related Work Other recent papers explore hybrid approaches to generative modelling. One such approach is to relax the probabilistic interpretation of the VAE model by replacing either the KL-divergence term or the ...
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Deep Quality Value (DQV) Learning
... neural networks is, when learning an action-value function with the Q ...the Q function, which can make the algorithm learn ...the Q function, because the V function is used to update ...
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Material acquisition using deep learning
... 1 Unet : O. Ronneberger, P.Fischer, and T. Brox. 2015. U-Net: Convolutional Networks for Biomedical Image Segmentation. In Medical Image Computing and Computer-Assisted Intervention (MICCAI) (LNCS), We introduce a ...
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Towards deep semi supervised learning
... This paper is focusing on the design choices that lead to the Ladder Network’s superior performance and tries to disentangle them empirically. We identify some general properties of the model that make it different from ...
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