Top PDF Stabilizing and Enhancing Learning for Deep Complex and Real Neural Networks
Stabilizing and Enhancing Learning for Deep Complex and Real Neural Networks
... feed-forward neural networks (FFNNs) such as CNNs and real-valued RNNs have shown to excel in a wide variety of applications and learning ...task. For instance, recurrent ...
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Deep Bilateral Learning for Real-Time Image Enhancement
... novel neural network architecture can reproduce sophisticated image enhancements with inference running in real time at full HD resolution on mobile ...enhancements and enable real-time ...
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Quantization and Deployment of Deep Neural Networks on Microcontrollers
... machine learning and hardware design. Presently, deep neural networks can be deployed on embedded targets to perform different tasks such as speech recognition, object detection or ...
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Enhancing the reusability and interoperability of artificial neural networks with DEVS modeling and simulation
... Artificial Neural Networks (ANN) ANN models the way biological neurons process information to solve complex non-algorithmic problems like recognizing patterns, classifying into groups, series ...
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Applications of complex numbers to deep neural networks
... results for the automatic music transcription (AMT) ...exploit complex operations as presented earlier in the ...[51]. For computational efficiency we resampled the original input from the original ...
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Extensive deep neural networks for transferring small scale learning to large scale systems
... a complex, many-body ...EDNN, and domain decomposition is handled ...particles, and in some cases, an implicit solvation ...angles, and partial charges are used as features, and the ...
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Learning and Recognizing Human Action from Skeleton Movement with Deep Residual Neural Networks
... lighting and so ...in real- ...simpler and faster. For that reason, exploiting the 3D human joint positions from depth cameras for recognizing human ac- tion is a very effective ...
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Geodesic Convolutional Neural Network for 3D Deep-Learning based Surrogate Modeling and Optimization
... The reported approach is beneficial on many critical aspects: first, it allows us to compute approx- imate solutions orders of magnitude faster than the typical numerical simulators (tens of millisec- onds instead of ...
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On Deep Multiscale Recurrent Neural Networks
... Hochreiter and Schmidhuber , 1997 ) employ the multiscale update con- cept, where the hidden units have different forget and update rates and thus can operate with different ...self-loop for ...
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Augmenting physics simulators with neural networks for model learning and control
... Our work mainly focuses on leveraging prior models (like analytical dynamics models, physics engine) to learn residual dynamics model between the prior model and r[r] ...
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Assembly output codes for learning neural networks
... CONCLUSIONS AND PERSPECTNES A way to represent categories in multi-class problems is presented, that departs itself from the usual "grandmother cell" ...output for a neural ...
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Deep Predictive Coding Networks for Video Prediction and Unsupervised Learning
... expected for evaluating the pixel-level ...pan and roll angles, the pan and roll velocities, and the prin- cipal component (PC) values, which control the “identity” of the ...
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Clinical event prediction and understanding with deep neural networks
... In addition, we compare these representations along with both long short-term memory networks (LSTM) and convolutional neural networks (CNN) for prediction of five i[r] ...
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De-noising and de-blurring of images using deep neural networks
... In this paper, we test using a Learning Synthesis Deep Neural Network (LS-DNN) [2] in combination with BM3D [3], an off the shelf de-noising tool, to generate images, att[r] ...
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Toward robust deep neural networks
... sets and in-distribution rejection rate ...TNR and FNR of these methods are the same concept as OOD rejection rate and in-distribution rejection rate, ...each, for in-distribution and ...
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Entropy and mutual information in models of deep neural networks
... −1 for x<−1, x for −1<x<1, and 1 for x>1, for which the integrals in the replica formula can be evaluated faster than for the ...linear and hardtanh case, the ...
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Mean-field Langevin System, Optimal Control and Deep Neural Networks
... method for the deep neural ...layer) neural networks using the mean-field Langevin ...the deep neural ...the deep neural network under mild ...(1.7) ...
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Compression of Deep Neural Networks for Image Instance Retrieval
... as networks get larger, it is not feasible to train them on a single ...Large neural networks are trained across multiple machines, and one of the key bottlenecks in training is the ...
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Learning visual representations with neural networks for video captioning and image generation
... a deep model with many hidden layers has been quite difficult in ...that learning has to solve becomes harder and harder, either due to bad local minima or ...points and their impact to ...
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Deep Learning and Reinforcement Learning for Inventory Control
... structure and rules of playing the game are very simple, the complex behavior of this dynamic system is ...Croson and Donohue ...(1997) and Sterman (1989) explained some rational and ...
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