The recurrent network can perform well even with less than 50% of features. This brings significant benefits in scenarios, where the neural network is used as a blackbox model of thermal comfort, which is called by an optimizer that minimizes the deviance from a target value. The reduction ...
Deep and recurrent neural networks (DNNs and RNNs respectively) are powerful models that were considered to be almost impossible to train using stochastic gradient descent with momentum. In this paper, we show that when stochastic gradient descent with momentum uses a well-designed random initializati...
(Fig.1b). As the comparison, we also implemented a traditional machine learning method, SVM (SupportVectorMachine)39and an alternative deep learning method CNN-RNN (ConvolutionalNeuralNetwork-RecurrentNeuralNetwork)40to modeling the m6A modification deposition for the same training and testing datasets ...
pattern recognition are applications of CNNs [3]. ANNS, usually simply called neural networks (NNs) or neural nets, are computing systems inspired by the biological neural networks that constitute animal brains. Application areas include system identification and control (vehicle control, trajectory pre...
On the importance of initialization and momentum in deep learning Ilya Sutskever1 ilyasu@ James Martens jmartens@ George Dahl gdahl@ Geo↵rey Hinton hinton@ Abstract widepread use until fairly recently. DNNs became the subject of renewed attention following the work Deep and recurrent neural ...
We showcase the generality of our method by testing it on both image classification and language modeling tasks using deep convolutional and recurrent neural networks. In particular, in case of CIFAR10 we reach 10% classification error 50 epochs faster than when using uniform sampling. 展开 ...
As time-dependent properties are crucial, various Recurrent Neural Network (RNN) models, including long-short term memory (LSTM), Bidirectional LSTM (Bi-LSTM)9 and deep LSTM frameworks10,11 are widely adopted to retain historical equipment states for evaluating current health conditions. Convolutional...
aEmergence of hierarchical structure mirroring linguistic composition in a recurrent neural network Emergence of hierarchical structure mirroring linguistic composition in a recurrent neural network[translate] a音乐气息非常浓厚 Music breath is extremely thick[translate] ...
On the importance of initialization and momentum in deep learning Ilya Sutskever1 ilyasu@google James Martens jmartens@cs.toronto.edu George Dahl gdahl@cs.toronto.edu Geo↵rey Hinton hinton@cs.toronto.edu Abstract Deep and recurrent neural networks (DNNs and RNNs respectively) are powerful mod...
There aremajor domainsin MLjobtrends like supervising machines, writing algorithms for spam detection, codeing, clustering, classifying, logical regression, Keras, Scratch, PyTorch, image processing, understanding text and speech processing, knowledge in Recurrent Neural Networks and many more. Companies ...