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Computer_Science		Computer_Science
Cryptography		Cryptography
Design		Design
Event_Driven_Programming		Event_Driven_Programming
Functional_Programming		Functional_Programming
Graphics		Graphics
JavaScript		JavaScript
Machine_Learning		Machine_Learning
Philosophy		Philosophy
Programming_Language_Theory		Programming_Language_Theory
Research_Experiments_Projects_Studies		Research_Experiments_Projects_Studies
Web		Web
_MIT		_MIT
__papers-collection		__papers-collection
rust		rust
.gitignore		.gitignore
1406 Dropout A Simple Way to Prevent Neural Networks from Overfitting.pdf		1406 Dropout A Simple Way to Prevent Neural Networks from Overfitting.pdf
1406 Generative Adversarial Networks.pdf		1406 Generative Adversarial Networks.pdf
1406 On the Number of Linear Regions of Deep Neural Networks.pdf		1406 On the Number of Linear Regions of Deep Neural Networks.pdf
1409 Very Deep Convolutional Networks for Large-Scale Image Recognition.pdf		1409 Very Deep Convolutional Networks for Large-Scale Image Recognition.pdf
1410 Multiobjective Reinforcement Learning OverView.pdf		1410 Multiobjective Reinforcement Learning OverView.pdf
1412 Adam - A Method for Stochastic Optimization.pdf		1412 Adam - A Method for Stochastic Optimization.pdf
1412 Loss Surfaces of Multi Layer Networks.pdf		1412 Loss Surfaces of Multi Layer Networks.pdf
1502 Batch Normalization - Accelerating Deep Learning Training by Reducing Internal Covariate Shift.pdf		1502 Batch Normalization - Accelerating Deep Learning Training by Reducing Internal Covariate Shift.pdf
1502 He Initialization.pdf		1502 He Initialization.pdf
1503 A Review of Relational Machine Learning for Knowledge Graphs.pdf		1503 A Review of Relational Machine Learning for Knowledge Graphs.pdf
1503 Distilling the Knowledge in a Neural Network.pdf		1503 Distilling the Knowledge in a Neural Network.pdf
1503 Optimizing Neural Networks with Kronecker-factored Approximate Curvature.pdf		1503 Optimizing Neural Networks with Kronecker-factored Approximate Curvature.pdf
1505 U-Net - Convolutional Networks for Biomedical Image Segmentation.pdf		1505 U-Net - Convolutional Networks for Biomedical Image Segmentation.pdf
1506 Convolutional Neural Networks at Constrained Time Cost.pdf		1506 Convolutional Neural Networks at Constrained Time Cost.pdf
1506 Cyclical Learning Rates for Training Neural Networks.pdf		1506 Cyclical Learning Rates for Training Neural Networks.pdf
1506 Hidden Technical Debt in Machine Learning Systems.pdf		1506 Hidden Technical Debt in Machine Learning Systems.pdf
1506 Spatial Transformer Networks.pdf		1506 Spatial Transformer Networks.pdf
1508 A Neural Algorithm of Artistic Style.pdf		1508 A Neural Algorithm of Artistic Style.pdf
1510 Modeling User Exposure in Recommendation.pdf		1510 Modeling User Exposure in Recommendation.pdf
1511 Accurate Image Super-Resolution Using Very Deep Convolutional Networks.pdf		1511 Accurate Image Super-Resolution Using Very Deep Convolutional Networks.pdf
1511 All You Need is Good Initialization.pdf		1511 All You Need is Good Initialization.pdf
1609 Deep Neural Networks for YouTube Recommendations.pdf		1609 Deep Neural Networks for YouTube Recommendations.pdf
1609 Neural Photo Editing with Introspective Adversarial Networks.pdf		1609 Neural Photo Editing with Introspective Adversarial Networks.pdf
1609 Photo-Realistic Single Image Super-Resolution Using a Generative Adversarial Network.pdf		1609 Photo-Realistic Single Image Super-Resolution Using a Generative Adversarial Network.pdf
1609 The Gaussian Graphical Model in Cross-sectional and Time-series Data.pdf		1609 The Gaussian Graphical Model in Cross-sectional and Time-series Data.pdf
1711 Toward Multimodal Image-to-Image Translation.pdf		1711 Toward Multimodal Image-to-Image Translation.pdf
1712 Adversarial Examples - Attacks and Defenses for Deep Learning.pdf		1712 Adversarial Examples - Attacks and Defenses for Deep Learning.pdf
1712 Deep Neuroevolution - Genetic Algorithms Are a Competitive Alternative for Training Deep Neural Networks for Reinforcement Learning.pdf		1712 Deep Neuroevolution - Genetic Algorithms Are a Competitive Alternative for Training Deep Neural Networks for Reinforcement Learning.pdf
1712 Improving Generalization Performance by Switching from Adam to SGD.pdf		1712 Improving Generalization Performance by Switching from Adam to SGD.pdf
1712 Noisy Natural Gradient as Variational Inference.pdf		1712 Noisy Natural Gradient as Variational Inference.pdf
1712 Visualizing the Loss Landscape of Neural Nets.pdf		1712 Visualizing the Loss Landscape of Neural Nets.pdf
1801 Adaptive Graph Convolutional Neural Networks.pdf		1801 Adaptive Graph Convolutional Neural Networks.pdf
1801 Dendritic error backpropagation in deep cortical microcircuits.pdf		1801 Dendritic error backpropagation in deep cortical microcircuits.pdf
1801 MobileNetV2 - Inverted Residuals and Linear Bottlenecks.pdf		1801 MobileNetV2 - Inverted Residuals and Linear Bottlenecks.pdf
1802 A Closed-form Solution to Photorealistic Image Stylization.pdf		1802 A Closed-form Solution to Photorealistic Image Stylization.pdf
1802 Adversarial Audio Synthesis.pdf		1802 Adversarial Audio Synthesis.pdf
1802 Deep Versus Wide Convolutional Neural Networks for Object Recognition on Neuromorphic System.pdf		1802 Deep Versus Wide Convolutional Neural Networks for Object Recognition on Neuromorphic System.pdf
1802 From Hashing to CNNs - Training BinaryWeight Networks via Hashing.pdf		1802 From Hashing to CNNs - Training BinaryWeight Networks via Hashing.pdf
1802 Kronecker-factored Curvature Approximations for Recurrent Neural Networks.pdf		1802 Kronecker-factored Curvature Approximations for Recurrent Neural Networks.pdf
1802 Loss Surfaces, Mode Connectivity, and Fast Ensembling of DNNs.pdf		1802 Loss Surfaces, Mode Connectivity, and Fast Ensembling of DNNs.pdf
1802 Regularized Evolution for Image Classifier Architecture Search.pdf		1802 Regularized Evolution for Image Classifier Architecture Search.pdf
README.md		README.md
scrap.md		scrap.md

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