Top PDF Learning Activation Functions in Deep Neural Networks
Learning Activation Functions in Deep Neural Networks
... Convolutional Neural Networks (CNNs). In this chapter, we first begin by considering the functional conncepts of the MLP mo- del, including the architechture, parameters of the basic model, ...
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af en Deep Learning in Spiking Neural Networks Deep learning in spiking neural networks
... 1 In recent years, deep learning has revolutionized the field of machine learning, for computer vision in ...particular. In this approach, a deep (multilayer) artificial ...
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Deep neural networks with transfer learning in millet crop images
... items in the reduction of quality and quantity in ...datasets in Deep Learning is a major scientific ...transfer learning aims to resolve this problem by recognizing and applying ...
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Learning Sparse Filters In Deep Convolutional Neural Networks With A l 1 /l 2 Pseudo-Norm
... An important factor for the compression of a model is its sparsity i.e. the number of parameters set to zero. However, this sparsity must be structured in order to be memory-efficient and time-efficient. Liu et ...
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Deep neural networks for direct, featureless learning through observation: the case of two-dimensional spin models
... convolutional deep neural network in predicting the nearest-neighbor energy of the 4 × 4 Ising ...the deep neural network can learn the nearest-neighbor Ising Hamiltonian after only ...
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Auto-Encoders, Distributed Training and Information Representation in Deep Neural Networks
... feature learning algorithms can be stacked to form deeper and more abstract ...representations. Deep learning algorithms learn multiple levels of representation, where the number of levels is ...
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Comparing learned representations of deep neural networks
... features in in- put images, and have very high adversarial example ...desirable in certain use-cases where consistency is preferred, or particularly undesirable in avoiding adversarial example ...
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Extensive deep neural networks for transferring small scale learning to large scale systems
... approach in the computer vision/articial intelligence ...the learning process can restrict it to local minima), and from somewhat of a philosophical point of ...view. In deep learning, ...
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A mathematical approach to unsupervised learning in recurrent neural networks
... the activation of a ...calcium in the ...dumped in the synaptic ...ion-channels in the post-synaptic membrane which cause the injection of a positive or negative current depending on the ...
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Deep neural networks for audio scene recognition
... consists in determining automatically the context or environment around a device ...music. In this way, time- domain (zero-crossing rate), frequency-domain (band-energy ration, spectral centroid, spectral ...
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Unsupervised Layer-Wise Model Selection in Deep Neural Networks
... investigate in more depth these findings, specifically examining the properties of abstraction of the hidden lay- ers in an Information Theoretical perspective and taking inspiration from ...lum ...
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Generalisation dynamics of online learning in over-parameterised neural networks
... discussed in the previous section becomes apparent: two of the hidden units of the student each align almost perfectly with a different hidden unit of the teacher, such that R 01 = R 20 ≈ 1, while the weights of ...
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Unsupervised post-tuning of deep neural networks
... Terms—deep learning, unsupervised training, regular- ization, natural language processing ...for deep learning classifiers is to move beyond traditional supervised training and exploit the ...
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Learning Sparse deep neural networks using efficient structured projections on convex constraints for green AI
... that in practice, relatively few network weights are actually necessary to accurately learn data ...proposed in order to remove network weights (weight sparsification) either on pre-trained models or during ...
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Bidirectional Joint Representation Learning with Symmetrical Deep Neural Networks for Multimodal and Crossmodal Applications
... Recently, deep neural networks, especially deep autoencoders, have proven promising both for crossmodal translation and for early fusion via multi- modal ...embedding. In this work, we ...
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Applications of complex numbers to deep neural networks
... networks on toy ...for learning unitary matrices and they applied their method on toy tasks and on a real-world speech ...weights in neural networks also has biological ...
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Classification of Hate Speech Using Deep Neural Networks
... powerful in classifying hate speech (Mohaouchane et ...the deep-learning based approaches has outperformed the classical machine learning techniques such as Support Vector Machines (SVM), ...
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Singing voice detection with deep recurrent neural networks
... Artificial Neural Networks (ANNs) ...step: in [2] features are computed from a signal with vocal components enhanced by a Harmonic/Percussive Source Separation (HPSS) technique proposed by Ono et ...
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Deep Captioning with Multimodal Recurrent Neural Networks (m-RNN)
... ORK Deep model for computer vision and natural ...the deep neural network developed rapidly in recent years in both the field of computer vision and natural lan- ...a deep ...
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Quantization and Deployment of Deep Neural Networks on Microcontrollers
... convolutional neural network, the convolutional and fully connected layers require a quantization-aware training for the ...normalization in our experiments, it has not been ...ReLU activation which ...
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