... 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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... 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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... 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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... 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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... 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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... 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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... 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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... 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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... 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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... 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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... 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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... 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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... 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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... 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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... 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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... **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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... 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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... 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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... 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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... 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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