A small neural network including the weights
Quantum Inspired Neural Network with Quantum Weights and Real Weights
... of neural networks, by introducing quantum rotation gates to the traditional BP networks, a novel quantum-inspired neural network model is proposed in this ...
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Neural Networks with Small Weights and Depth-Separation Barriers
... of neural networks, an important question is whether there are functions which can only be approximated by sufficiently deep networks, assuming their size is ...super-polynomial weights in order to ...
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Using Artificial Neural Network to Predict Body Weights of Rabbits
... artificial neural network (ANN) models for predicting body weight using various independent (input) variables in eight-week old New Zealand white pure- bred and crossbred ...the neural network ...
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Simultaneous Evolution of Architecture and Connection Weights in Artificial Neural Network
... for neural network decoding. But sometimes, the network can be pre-structured architectures, which makes the search space is very much ...some network parameters like number of layers, the ...
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Evolving binary-weights neural network using hybrid optimization algorithm for color space conversion
... In this study, a method using neuroevolution strategy to realize multiplierless CSC architecture will be introduced. The complementary characteristics between PSO and GA are used to construct high e- ciency hybrid ...
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APPLICATION OF GENETIC ALGORITHM OPTIMIZED NEURAL NETWORK CONNECTION WEIGHTS FOR MEDICAL DIAGNOSIS OF PIMA INDIANS DIABETES
... the network. BPN uses a gradient method for finding weights and is prone to lead to troubles such as local minimum problem, slow convergence pace and convergence unsteadiness in its training ...the ...
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Quantized Neural Networks: Training Neural Networks with Low Precision Weights and Activations
... the weights and ...the weights and activations in a trained network naturally have non-uniform ...1-bit weights, 2-bit activations and 6-bit gradients they can achieve ...the weights, ...
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Searching for Low-Bit Weights in Quantized Neural Networks
... deep neural networks, a series of methods are explored, including efficient block design, network pruning, weight decomposition, and network ...between weights and filters and explore ...
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Reusing Weights in Subword Aware Neural Language Models
... the small word-level and all other networks respectively ex- cept CharCNN, for which it is ...the small and medium networks, except the forget bias of the word-level LSTM, which is initialized to 1, and the ...
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Using Feature Weights to Improve Performance of Neural Networks
... Abstract Different features have different relevance to a particular learning problem. Some features are less relevant; while some very important. Instead of selecting the most relevant features using feature selection, ...
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Neurons vs Weights Pruning in Artificial Neural Networks
... all weights or neurons in input and hidden layers we calculate classification error for network operating without ...nullifying weights.) When neuron/weight, which upon removal gives network ...
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Initialization of Weights in Deep Belief Neural Network Based on Standard Deviation of Feature Values in Training Data Vectors
... Department of Computer / Science and Research Branch of, Islamic Azad University, Tehran, Iran Abstract - Nowadays, the feature engineering approach has become very popular in deep neural networks. The purpose of ...
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Network of interacting neurons with random synaptic weights
... In contrast with more standard particle systems like those addressed in [24] or (say) of the same form as in (5), it is here possible to have several neurons hitting the firing potential at the same instant of time, and, ...
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Neural network based image representation for small scale object recognition
... size, small number of training samples, too clean backgrounds, and thus later ones were proposed to improve the earlier ...deep neural networks, while can also be used to enhance algorithms applied to other ...
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Optimizing Weights of Artificial Neural Networks using Genetic Algorithms
... Multilayer Neural Network (NN) using Genetic Algorithms (GAs) ...the neural controller, as well as training the network to minimize the error between the output of the plant and the output of ...
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Mutual Information Based Modified Randomized Weights Neural Networks
... randomized weights neural networks algorithm has faster learning speed, which has been successfully applied [4] ...input weights is an open issue. Study shows that small norm of the ...
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Approximation by neural networks with weights varying on a finite set of directions
... of weights and thresholds (see ...of weights can also have the ...layer network with a polygonal, polynomial spline or analytic activation function and a bounded set of weights has the ...only ...
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Capacity of two-layer feedforward neural networks with binary weights
... two-layer network is then constructed as described in Section III-A using the samples in Class ...the network one by one. A sample is classified correctly by the network if its actual label assigned ...
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Performance Analysis of Fixed-Random Weights in Artificial Neural Networks
... TT-ELM network. For each network evaluated on, the last FC layer is left at a constant 1,000 neurons, the lambda regularization rate is adjusted to values of ...Each network is averaged over 10 runs ...
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Learning Algorithm and Application of Quantum Neural Networks with Quantum Weights
... the neural network applied quantum dots [5], the common neural network applied quantum transform function, and the quantum Hopfield networks and so ...proposed neural networks with ...
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