阅读计划

时间：2019-03-20 10:46:55 阅读：158 评论：0 收藏：0 [点我收藏+]

整理一下要读的已读的书籍论文，加粗为还没有读的

神经网络通用理论

Understanding the difficulty of training deep feedforward neural networks (AISTATS 2010)
Dropout: A Simple Way to Prevent Neural Networks from Overfitting (JMLR 2014)
Adam: A Method for Stochastic Optimization (ICLR 2015)
An overview of gradient descent optimization algorithms (arXiv 2017)
The Shattered Gradients Problem: If resnets are the answer, then what is the question? (ICML 2017)
Bag of Tricks for Image Classification with Convolutional Neural Networks (arXiv 2018)

Efficient Estimation of Word Representations in Vector Space (aiXiv 2013)
Distributed Representations of Words and Phrases and their Compositionality (NIPS 2013)
Word2vec Parameter Learning Explained (arXiv 2014)
GloVe: Global Vectors for Word Representation (EMNLP 2014)
Improving Distributional Similarity with Lessons Learned from Word Embeddings (TACL 2015)
Evaluation methods for unsupervised word embeddings (EMNLP 2015)
A Latent Variable Model Approach to PMI-based Word Embeddings (TACL 2016)
Linear Algebraic Structure of Word Senses, with Applications to Polysemy (TACL 2018)
On the Dimensionality of Word Embedding (NIPS 2018)

(LeNet) Gradient Based Learning Applied to Document Recognition (PROC OF THE IEEE 1998)
(AlexNet) ImageNet Classification with Deep Convolutional Neural Networks.
Network In Network (NIPS 2012)
(VGGNet) Very Deep Convolutional Networks for Large-Scale Image Recognition (ICLR 2015)
(GoogLeNetV1) Going deeper with convolutions (CVPR 2015)
(GoogLeNetV2) Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift (ICML 2015)
(GoogLeNetV3) Rethinking the Inception Architecture for Computer Vision (CVPR 2016)
(GoogLeNetV4) Inception-v4, Inception-ResNet and the Impact of Residual Connections on Learning (AAAI 2017)
Highway Networks (ICML Workshop 2015)
(ResNet)Deep Residual Learning for Image Recognition (CVPR 2016)
(ResNetV2) Identity Mappings in Deep Residual Networks (ECCV 2016)
(DenseNet) Densely Connected Convolutional Networks (CVPR 2017)
Xception: Deep Learning with Depthwise Separable Convolutions (CVPR 2017)
MobileNets: Efficient Convolutional Neural Networks for Mobile Vision (arXiv 2017)
MobileNetV2: Inverted Residuals and Linear Bottlenecks (CVPR 2018)
Squeeze-and-Excitation Networks (CVPR 2018)
ShuffleNet: An Extremely Efficient Convolutional Neural Network for Mobile (CVPR 2018)
ShuffleNet V2: Practical Guidelines for Efficient CNN Architecture Design (ECCV 2018)

(R-CNN) Rich feature hierarchies for accurate object detection and semantic segmentation (CVPR 2014)
Fast R-CNN (ICCV 2015)
(Faster R-CNN) Faster R-CNN: Towards Real-Time Object Detection with Region Proposal Networks (NIPS 2015)
(SSD) SSD: Single Shot MultiBox Detector (ECCV 2016)
(SPPNet) Spatial Pyramid Pooling in Deep Convolutional Networks for Visual Recognition (TPAMI 2015)
(YOLOV1) You Only Look Once: Unified, Real-Time Object Detection (CVPR 2016)
(YOLOV2) YOLO9000: Better, Faster, Stronger (CVPR 2017)
(YOLOV3) YOLOv3: An Incremental Improvement (arXiv 2018)
Feature Pyramid Networks for Object Detection (CVPR 2017)
Focal Loss for Dense Object Detection (ICCV 2017)
Mask R-CNN (ICCV 2017)

Generative Adversarial Network (NIPS 2014)
Unsupervised Representation Learning with Deep Convolutional Generative Adversarial Networks (arXiv 2016)
InfoGAN: Interpretable Representation Learning by Information Maximizing Generative Adversarial Nets (NIPS 2016)
Adversarial Feature Learning (ICLR 2017)
Unpaired Image-to-Image Translation using Cycle-Consistent Adversarial Networks (ICCV 2017)
Wasserstein GAN (ICML 2017)
(ACGAN) Conditional Image Synthesis With Auxiliary Classifier GANs (ICML 2017)
Boundary-Seeking Generative Adversarial Networks (ICLR 2018)

Auto-Encoding Variational Bayes
Stochastic Backpropagation and Approximate Inference in Deep Generative Models
Recent Advances in Autoencoder-Based Representation Learning
Hyperspherical Variational Auto-Encoders
VAE with a VampPrior
beta-VAE: Learning basic visual concepts with a constrained variational framework
InfoVAE: Information maximizing variational autoencoders
Variational inference of disentangled latent concepts from unlabeled observations
Information constraints on auto-encoding variational bayes
Structured disentangled representations
PixelVAE: A latent variable model for natural images
Learning hierarchical features from deep generative models
Semi-supervised learning with deep generative models
PixelGAN autoencoders
GO GRADIENT FOR EXPECTATION-BASED OBJECTIVES
Neural Variational Inference and Learning in Belief Networks

Tensor Decompositions and Applications. Tamara G. Kolda, Brett W. Bader. SIAM REVIEW
Tensor Decompositions for Learning Latent Variable Models. JMLR

原文：https://www.cnblogs.com/cuhm/p/10563200.html

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