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カメラや写真やディープラーニングや人工知能のお話(2017年9月)

 

Google Research Blog

MatConvNet

========== Machine Learning ==========

パターン認識と機械学習の学習

The Elements of Statistical Learning : Data Mining, Inference, and Prediction Second Edition

Statistical Learning with Sparsity : The Lasso and Generalizations

 

【Transfer Learning】

【CNN】

Res netと派生研究の紹介

Convolutional Neural Networksのトレンド

●Inception v1 (GoogLeNet)
Going Deeper with Convolutions
References
-[1]Know your meme: We need to go deeper.
-[2]Provable Bounds for Learning Some Deep Representations
-[3]On Two-Dimensional Sparse Matrix Partitioning: Models, Methods, and a Recipe.
-[4]Large Scale Distributed Deep Networks
-[5]Scalable Object Detection using Deep Neural Networks (R-CNN)
-[6]Rich feature hierarchies for accurate object detection and semantic segmentation
-[7]Improving neural networks by preventing co-adaptation of feature detectors
-[8]Some Improvements on Deep Convolutional Neural Network Based Image Classification
-[9]ImageNet Classification with Deep Convolutional Neural Networks (AlexNet)
-[10]Backpropagation Applied to Handwritten Zip Code Recognition
-[11]Gradient-Based Learning Applied to Document Recognition
-[12]Network in Network
-[13]Acceleration of Stochastic Approximation by Averaging
-[14]OverFeat: Integrated Recognition, Localization and Detection using Convolutional Networks
-[15]Robust Object Recognition with Cortex-Like Mechanisms
-[16]Scaling Up Matrix Computations on Shared-Memory Manycore Systems with 1000 CPU Cores
-[17]On the importance of initialization and momentum in deep Learning
-[18]Deep Neural Networks for Object Detection
-[19]DeepPose: Human Pose Estimation via Deep Neural Networks
-[20]Segmentation as Selective Search for Object Recognition
-[21]Visualizing and Understanding Convolutional Networks

Inception-v4, Inception-ResNet and the Impact of Residual Connections on Learning

[Survey]Inception-v4, Inception-ResNet and the Impact of Residual Connections on Learning

 

●Residual Network (ResNet)
Deep Residual Learning for Image Recognition
References
-[1]
-[2]

Deep Residual Learning for Image Recognition

●Xception
Xception: Deep Learning with Depthwise Separable Convolutions
References
-[1]
-[2]

[DL輪読会]Xception: Deep Learning with Depthwise Separable Convolutions

●VGG-VD
Very Deep Convolutional Networks for Large-Scalse Image Recognition
References
-[1]
-[2]

●VGG-S,M,F
Return of the Devil in the Details: Delving Deep into Convolutional Nets
References
-[1]
-[2]

●AlexNet
ImageNet Classification with Deep Convolutional Neural Networks
References
-[1]
-[2]

【R-CNN】

●R-CNN
Rich feature hierarchies for accurate object detection and semantic segmentation
References
-[1]
-[2]

 

●Fast R-CNN

●Faster R-CNN

●Single Shot Multibox Detector

●YOLOv2

●Edge Boxes

【RNN】

【SAE】

【Deep Q-Network】

最近のDQN

【Deep Boltzmann Machines】

Deep Boltzmann Machines

 

 

【正則化】

【SVM】

【カーネル法】

【グラフィカルモデル】

【アンサンブル学習】

【強化学習】

ゼロからDeepまで学ぶ強化学習

【フレームワーク系】

TensorFlow

keras

Preferred Networks

【その他】

========== Computational Fluid Dynamics ==========

【界面追跡】

【界面捕獲】

【MPS】

【SPH】

【Lattice Boltzmann】

【Non-Newtonian Fluid】

公開日:
最終更新日:2017/10/13