Reading List
Deep Analysis:
- QUALITATIVELY CHARACTERIZING NEURAL NETWORK OPTIMIZATION PROBLEMS
- EXPLAINING AND HARNESSING ADVERSARIAL EXAMPLES
- Intriguing properties of neural networks
- Multilayer feedforward networks are universal approximators
- Unsupervised Learning on Neural Network Outputs
- Inverting Convolutional Networks with Convolutional Networks
- Visualizing and understanding convolutional networks
- Understanding Deep Image Representations by Inverting Them
- Visualizing and Understanding Recurrent Networks
Deep Nets:
- A Critical Review of Recurrent Neural Networks for Sequence Learning
- Maxout networks
- Supervised Sequence Labelling with Recurrent Neural Networks
- Stacked What-Where Auto-encoders
- A Recurrent Latent Variable Model for Sequential Data
- Distilling the Knowledge in a Neural Network
- Geoff Hinton – Recent Developments in Deep Learning
- Learning to Generate Chairs with Convolutional Neural Networks
- Constrained Convolutional Neural Networks for Weakly Supervised Segmentation
Deep Net Application:
- DRAW: A Recurrent Neural Network For Image Generation
- The Long-Short Story of Movie Description
- Decoupled Deep Neural Network for Semi-supervised Semantic Segmentation
- DeepStereo: Learning to Predict New Views from the World’s Imagery
Temporal Consistency
- Learning to track for spatio-temporal action localization
- Beyond Temporal Pooling: Recurrence and Temporal Convolutions for Gesture Recognition in Video
- Scheduled Sampling for Sequence Prediction with Recurrent Neural Networks
- P-CNN: Pose-based CNN Features for Action Recognition
- Convolutional LSTM Network: A Machine Learning Approach for Precipitation Nowcasting
- Unsupervised Learning of Video Representations using LSTMs
- An Empirical Exploration of Recurrent Network Architectures
- Sequence to Sequence Learning with Neural Networks
- Modeling Deep Temporal Dependencies with Recurrent “Grammar Cells”
- Describing Multimedia Content using Attention-based Encoder–Decoder Networks
- How a Kalman filter works, in pictures
- Online Representation Learning in Recurrent Neural Language Models
- Gated Feedback Recurrent Neural Networks
- Recurrent Network Models for Kinematic Tracking
- A Recurrent Latent Variable Model for Sequential Data
- Generative Image Modeling Using Spatial LSTMs
- GMM: Generating Sequences With Recurrent Neural Networks
- [RNN code] opinion mining with deep recurrent nets
- RNNs in Tensorflow, a Practical Guide and Undocumented Features
Variational Inference
- Variational Inference
- Tutorial on Variational Autoencoders
- Auto-Encoding Variational Bayes
- Variational Inference: A Review for Statisticians
- Morphing Faces
- reddit on VA
- Variational Autoencoder in TensorFlow
- A Beginner’s Guide to Variational Methods: Mean-Field Approximation
Tracking
- Reliable Patch Trackers: Robust Visual Tracking by Exploiting Reliable Patches
- Clustering of Static-Adaptive Correspondences for Deformable Object Tracking
- Real-time part-based visual tracking via adaptive correlation filters
- Single target tracking using adaptive clustered decision trees and dynamic multi-level appearance models
- Multihypothesis Trajectory Analysis for Robust Visual Tracking
- Long-term Correlation Tracking
- Object Tracking Benchmark
- Online Object Tracking with Proposal Selection
- MEEM: Robust Tracking via Multiple Experts using Entropy Minimization
Ensemble Learning
- Importance Sampled Learning Ensembles
- Ensemble-Based Tracking: Aggregating Crowdsourced Structured Time Series Data
- AN ENSEMBLE OF DEEP NEURAL NETWORKS FOR OBJECT TRACKING
Reinforcement Learning:
- Andrej Karpathy’s blog: Deep Reinforcement Learning: Pong from Pixels
- David Silver’s course at UCL
- Benjamin Van Roy’s course at Standord
- Sergey Levine’s course at UC Berkeley
- Deep Reinforcement Learning Course
- Reinforcement Learning: An Introduction
- Reinforcement Learning Course
- Playing Atari with Deep Reinforcement Learning
- Human-level control through deep reinforcement learning
- Reinforcement Learning for Robust and Efficient Real-World Tracking
- Action-Conditional Video Prediction using Deep Networks in Atari Games
- Reinforcement Learning Ware House
- Reinforcement Learning-Based Feature Learning for Object Tracking
- Advanced Topics: RL
- Learning to Track: Online Multi-Object Tracking by Decision Making
Programming:
- Theano+DeepLearning
- Applied Deep Learning for Computer Vision with Torch
- Chainer, A Powerful, Flexible, and Intuitive Framework of Neural Networks
- Inceptionism using IPython Notebook
- Python Easy Neural Network Extruder
- C++ codes for rnn
- Popular Deep Learning Tools – a review
- Tips on working with Theano
- 跟我一起写Makefile
- Learning TensorFlow (non official)
New Stuff
- Learning Transferable Features with Deep Adaptation Networks
- Grid Long Short-Term Memory
- Understanding Intra-Class Knowledge Inside CNN
- Feature Representation In Convolutional Neural Networks
- Deep Generative Image Models using a Laplacian Pyramid of Adversarial Networks
- Distributional Smoothing by Virtual Adversarial Examples
- Show, Attend and Tell: Neural Image Caption Generation with Visual Attention
- Building a Deep Learning (Dream) Machine
CUDA:
Tutorial:
- Deep Learning Video Tutorials
- Session with Yoshua Bengio on Deep Learning and Others
- An introduction to machine learning
- A curated list of deep learning resources for computer vision
- Convex Optimization
- Microsoft Faculty Summit
- Yann LeCun - The Unreasonable Effectiveness of Deep Learning
- Learn Data Science the Hard Way
- Deep Learning Course
- Machine Learning / Deep Learning Tutorial
- Awesome Recurrent Neural Networks
- CVPR 2015 Oral
- Nvidia Deep Learning Courses
- Machine Learning Trick of the Day
- Statistical Machine Learning from CMU
- Deep Learning Summer School
- Machine Learning and Probabilistic Graphical Models Course from University at Buffalo
- Andrej Karpathy昨天在伦敦深度学习研讨会上关于Recurrent Neural Networks的讲座录像
- Dive into Machine Learning
- Open Resources
- Deep Learning Summer School
- ICCV2015 Oral Videos
- Deep Learning Resources
Deep Learning Material
- Self-Paced Courses for Deep Learning
- CS224d: Deep Learning for Natural Language Processing
- LeCun’s Deep Learning Course
- UFLDL
- Xiaogang’s Deep Learning Course
- CS231n Convolutional Neural Networks for Visual Recognition
- Neural Networks and Deep Learning (freee online book)
Cool
- Paperscape
- nbviewer
- jupyter
- 5 secrets to surviving (and thriving in) a PhD program
- GitXiv: arXiv + Github + Links + Discussion
- What a Deep Neural Network thinks about your #selfie
- Neural Stack
Mathematical Paper Writing
- Mathematical English Usage - a Dictionary
- Advice for amateur mathematicians on writing and publishing papers
- Writing a Research Paper in Mathematics
- Mathematical Writing
- A Guide to Writing Mathematics
- GUIDELINES FOR GOOD MATHEMATICAL WRITING
- Advice on Writing Papers
- Notation and Mathematical Conventions
- Wikipedia:Manual of Style/Mathematics
Books
- Understanding Machine Learning: From Theory to Algorithms
- Bayesian Reasoning and Machine Learning
- PRIDE AND PREJUDICE
- Convex Optimization: Algorithms and Complexity
Sleep
TR List