large neural networks training
Layer-wise compressive training for convolutional neural networks
... Abstract: Convolutional Neural Networks (CNNs) are brain-inspired computational models designed to recognize patterns. Recent advances demonstrate that CNNs are able to achieve, and often exceed, human ...
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Robust Large Margin Deep Neural Networks
... of training samples are used (5000, 20000, ...The networks were trained using the stochastic gradient descent (SGD) with momentum, which was set to ...The networks were trained for 120 epochs in ...
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Noisy training for deep neural networks in speech recognition
... Although promising, the NN-based approach, either by the hybrid setting or the tandem setting, did not deliver overwhelming superiority over the conventional approaches based on MFCCs and GMMs. The revo- lution took ...
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Domain-Adversarial Training of Neural Networks
... deep neural network architectures, that have already brought impressive advances to the state-of-the-art across a wide variety of machine-learning tasks and appli- ...obtain training sets that are big ...
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Pre-training in convolutional neural networks
... modern networks, the ideas of convolution and pooling are still the fundamental of modern convolutional ...convolutional neural network applied to zip code recognition [37] was the very rst practical ...
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Quantized Neural Networks: Training Neural Networks with Low Precision Weights and Activations
... promising results on the ImageNet dataset. Note that their method, named Xnor-Net, re- quires additional multiplication by a different scaling factor for each patch in each sample (Rastegari et al., 2016) Section 3.2 Eq. ...
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Analysis and Comparison of Algorithms for Training Recurrent Neural Networks
... the training data comprises a representative set of ...the training error but does not appear to generalize in the desired ...is large because then there are many weight configurations which could ...
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Deep machine learning provides state of the art performance in image based plant phenotyping
... on large image sets in order to aid genetic ...artificial neural networks, deep approaches have many more hidden layers in the network, and hence have greater discriminative and predictive ...
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Phone recognition with hierarchical convolutional deep maxout networks
... convolutional neural networks (CNNs) have recently been shown to outperform fully connected deep neural networks (DNNs) both on low-resource and on large-scale speech ...convolutional ...
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On Deep Multiscale Recurrent Neural Networks
... (2) Data Sparsity There is a further technical reason why much of previous re- search on machine translation has considered words as a basic unit. This is mainly due to the fact that major components in the existing ...
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Survey on Software Tools that Implement Deep Learning Algorithms on Intel/x86 and IBM/Power8/Power9 Platforms
... a large number of frameworks for training neural networks, the most pop- ular of which are TensorFlow, Caffe, Caffe2, Torch, PyTorch, MXNet, Theano, PaddlePaddle, Microsoft Cognitive Toolkit, ...
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Towards Robust Design and Training of Deep Neural Networks
... impairs training by altering descent trajectory, increasing the cycles required for convergence, or making it outright ...with large HNNs currently ...
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Towards Neural Network Model for Insulin/Glucose in Diabetics-II
... every training sample and hence, all parameters of the network contribute to the generation of the output ...RBF networks) when the data is cheap or plentiful like in adaptive control or some signal ...
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Convolutional Neural Network with Biologically Inspired Retinal Structure
... In this paper, we propose a new Convolutional Neural Network (CNN) with biologically inspired retinal structure and ON/OFF Rectified Linear Unit (ON/OFF ReLU). Retinal structure enhances input images by center ...
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A general purpose intelligent surveillance system for mobile devices using deep learning
... More specifically, a data layer is used that prepares the mini-batches and feeds them into the input layer. The reason a dedicated data layer is used is that by asynchronously building the next mini-batch while the ...
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Using artificial neural network to monitor and predict induction motor bearing (IMB) failure
... artificial neural network (ANN) model of induction motor bearing (IMB) failure ...two networks were tested; Feedforward Neural Network (FFNN) and Elman Network for the performance of training, ...
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Approximating the Predictive Distribution via Adversarially-Trained Hypernetworks
... invertible neural networks, that make use of the change of variables formula to compute the density of network outputs given the probability density function (pdf) of their ...Such networks need to ...
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Determination of Corrosion Types from Electrochemical Noise by Artificial Neural Networks
... Artificial neural network is a new approach to differentiate corrosion types. Although ANN has been developed extensively over many years, especially in the field of forecast, fitting, pattern recognition, few ...
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Indoor home scene recognition using capsule neural networks
... Limin Wang et al. [6] developed Multi-Resolution CNNs (MR-CNN), which is a combination of coarse and fine resolution CNNs and are said to be complementary to each other. The network is trained using ImageNet, Places and ...
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The Need for Knowledge Extraction: Understanding Harmful Gambling Behavior with Neural Networks
... paper, neural network models are analyzed to help predict the outcome of a partial proxy for harmful gambling behavior: when a gambler “self-excludes”, requesting a gambling operator to prevent them from accessing ...
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