Papers

Restricted Boltzmann Machine (RBM)

Tutorials

DLT

Learning A Deep Compact Image Representation for Visual Tracking

• intro: NIPS 2013
• project page: http://winsty.net/dlt.html

Hierarchical Convolutional Features for Visual Tracking

• intro: ICCV 2015
• project page: https://sites.google.com/site/jbhuang0604/publications/cf2
• github: https://github.com/jbhuang0604/CF2

Robust Visual Tracking via Convolutional Networks

• arxiv: http://arxiv.org/abs/1501.04505
• paper: http://kaihuazhang.net/CNT.pdf
• code: http://kaihuazhang.net/CNT_matlab.rar

SO-DLT

Transferring Rich Feature Hierarchies for Robust Visual Tracking

• arxiv: http://arxiv.org/abs/1501.04587
• slides: http://valse.mmcheng.net/ftp/20150325/RVT.pptx

MDNet

Learning Multi-Domain Convolutional Neural Networks for Visual Tracking

• intro: The Winner of The VOT2015 Challenge
• keywords: Multi-Domain Network (MDNet)
• homepage: http://cvlab.postech.ac.kr/research/mdnet/
• arxiv: http://arxiv.org/abs/1510.07945
• github: https://github.com/HyeonseobNam/MDNet

RATM: Recurrent Attentive Tracking Model

• arxiv: http://arxiv.org/abs/1510.08660
• github: https://github.com/saebrahimi/RATM

Understanding and Diagnosing Visual Tracking Systems

• intro: ICCV 2015
• project page: http://winsty.net/tracker_diagnose.html
• paper: http://winsty.net/papers/diagnose.pdf
• code(Matlab): http://120.52.72.43/winsty.net/c3pr90ntcsf0/diagnose/diagnose_code.zip

Recurrently Target-Attending Tracking

• paper: http://www.cv-foundation.org/openaccess/content_cvpr_2016/html/Cui_Recurrently_Target-Attending_Tracking_CVPR_2016_paper.html
• paper: http://www.cv-foundation.org/openaccess/content_cvpr_2016/papers/Cui_Recurrently_Target-Attending_Tracking_CVPR_2016_paper.pdf

Visual Tracking with Fully Convolutional Networks

• intro: ICCV 2015
• paper: http://202.118.75.4/lu/Paper/ICCV2015/iccv15_lijun.pdf
• github: https://github.com/scott89/FCNT

Deep Tracking: Seeing Beyond Seeing Using Recurrent Neural Networks

• intro: AAAI 2016
• arxiv: http://arxiv.org/abs/1602.00991
• github: https://github.com/pondruska/DeepTracking

Learning to Track at 100 FPS with Deep Regression Networks

• intro: ECCV 2015
• intro: GOTURN: Generic Object Tracking Using Regression Networks
• project page: http://davheld.github.io/GOTURN/GOTURN.html
• arxiv: http://arxiv.org/abs/1604.01802
• github: https://github.com/davheld/GOTURN

Learning by tracking: Siamese CNN for robust target association

• arxiv: http://arxiv.org/abs/1604.07866

Fully-Convolutional Siamese Networks for Object Tracking

• intro: ECCV 2016
• intro: State-of-the-art performance in arbitrary object tracking at 50-100 FPS with Fully Convolutional Siamese networks
• project page: http://www.robots.ox.ac.uk/~luca/siamese-fc.html
• arxiv: http://arxiv.org/abs/1606.09549
• github(official): https://github.com/bertinetto/siamese-fc
• github(official): https://github.com/torrvision/siamfc-tf
• valse-video: http://www.iqiyi.com/w_19ruirwrel.html#vfrm=8-8-0-1

Hedged Deep Tracking

• project page(paper+code): https://sites.google.com/site/yuankiqi/hdt
• paper: https://docs.google.com/viewer?a=v&pid=sites&srcid=ZGVmYXVsdGRvbWFpbnx5dWFua2lxaXxneDoxZjc2MmYwZGIzNjFhYTRl

ROLO

Spatially Supervised Recurrent Convolutional Neural Networks for Visual Object Tracking

• intro: ROLO is short for Recurrent YOLO, aimed at simultaneous object detection and tracking
• project page: http://guanghan.info/projects/ROLO/
• arxiv: http://arxiv.org/abs/1607.05781
• github: https://github.com/Guanghan/ROLO

Visual Tracking via Shallow and Deep Collaborative Model

• arxiv: http://arxiv.org/abs/1607.08040

Beyond Correlation Filters: Learning Continuous Convolution Operators for Visual Tracking

• intro: ECCV 2016
• intro: OTB-2015 (+5.1% in mean OP), Temple-Color (+4.6% in mean OP), and VOT2015 (20% relative reduction in failure rate)
• keywords: Continuous Convolution Operator Tracker (C-COT)
• project page: http://www.cvl.isy.liu.se/research/objrec/visualtracking/conttrack/index.html
• arxiv: http://arxiv.org/abs/1608.03773
• github(MATLAB): https://github.com/martin-danelljan/Continuous-ConvOp

Predictive Vision Model (PVM)

Unsupervised Learning from Continuous Video in a Scalable Predictive Recurrent Network

• arxiv: http://arxiv.org/abs/1607.06854
• github: https://github.com/braincorp/PVM

Modeling and Propagating CNNs in a Tree Structure for Visual Tracking

• arxiv: http://arxiv.org/abs/1608.07242

Robust Scale Adaptive Kernel Correlation Filter Tracker With Hierarchical Convolutional Features

• paper: http://ieeexplore.ieee.org/document/7496863/

Deep Tracking on the Move: Learning to Track the World from a Moving Vehicle using Recurrent Neural Networks

• arxiv: https://arxiv.org/abs/1609.09365

OTB Results: visual tracker benchmark results

• github: https://github.com/foolwood/benchmark_results

Convolutional Regression for Visual Tracking

• arxiv: https://arxiv.org/abs/1611.04215

Semantic tracking: Single-target tracking with inter-supervised convolutional networks

• arxiv: https://arxiv.org/abs/1611.06395

SANet: Structure-Aware Network for Visual Tracking

• arxiv: https://arxiv.org/abs/1611.06878

ECO: Efficient Convolution Operators for Tracking

• intro: CVPR 2017
• project page: http://www.cvl.isy.liu.se/research/objrec/visualtracking/ecotrack/index.html
• arxiv: https://arxiv.org/abs/1611.09224
• github: https://github.com/martin-danelljan/ECO

Dual Deep Network for Visual Tracking

• arxiv: https://arxiv.org/abs/1612.06053

Deep Motion Features for Visual Tracking

• intro: ICPR 2016. Best paper award in the “Computer Vision and Robot Vision” track
• arxiv: https://arxiv.org/abs/1612.06615

Globally Optimal Object Tracking with Fully Convolutional Networks

• arxiv: https://arxiv.org/abs/1612.08274

Robust and Real-time Deep Tracking Via Multi-Scale Domain Adaptation

• arxiv: https://arxiv.org/abs/1701.00561
• bitbucket: https://bitbucket.org/xinke_wang/msdat

Tracking The Untrackable: Learning To Track Multiple Cues with Long-Term Dependencies

• arxiv: https://arxiv.org/abs/1701.01909

Large Margin Object Tracking with Circulant Feature Maps

• intro: CVPR 2017
• intro: The experimental results demonstrate that the proposed tracker performs superiorly against several state-of-the-art algorithms on the challenging benchmark sequences while runs at speed in excess of 80 frames per secon
• arxiv: https://arxiv.org/abs/1703.05020
• notes: https://zhuanlan.zhihu.com/p/25761718

DCFNet: Discriminant Correlation Filters Network for Visual Tracking

• arxiv: https://arxiv.org/abs/1704.04057
• github: https://github.com/foolwood/DCFNet

End-to-end representation learning for Correlation Filter based tracking

• intro: CVPR 2017. University of Oxford
• intro: Training a Correlation Filter end-to-end allows lightweight networks of 2 layers (600 kB) to achieve state-of-the-art performance in tracking, at high-speed.
• project page: http://www.robots.ox.ac.uk/~luca/cfnet.html
• arxiv: https://arxiv.org/abs/1704.06036
• gtihub: https://github.com/bertinetto/cfnet

Context-Aware Correlation Filter Tracking

• intro: CVPR 2017 Oral
• project page: https://ivul.kaust.edu.sa/Pages/pub-ca-cf-tracking.aspx
• paper: https://ivul.kaust.edu.sa/Documents/Publications/2017/Context-Aware%20Correlation%20Filter%20Tracking.pdf
• github: https://github.com/thias15/Context-Aware-CF-Tracking

Robust Multi-view Pedestrian Tracking Using Neural Networks

https://arxiv.org/abs/1704.06370

Re3 : Real-Time Recurrent Regression Networks for Object Tracking

• intro: University of Washington
• arxiv: https://arxiv.org/abs/1705.06368
• demo: https://www.youtube.com/watch?v=PC0txGaYz2I

Robust Tracking Using Region Proposal Networks

https://arxiv.org/abs/1705.10447

Hierarchical Attentive Recurrent Tracking

• intro: NIPS 2017. University of Oxford
• arxiv: https://arxiv.org/abs/1706.09262
• github: https://github.com/akosiorek/hart
• results: https://youtu.be/Vvkjm0FRGSs

Siamese Learning Visual Tracking: A Survey

https://arxiv.org/abs/1707.00569

Robust Visual Tracking via Hierarchical Convolutional Features

• project page: https://sites.google.com/site/chaoma99/hcft-tracking
• arxiv: https://arxiv.org/abs/1707.03816
• github: https://github.com/chaoma99/HCFTstar

CREST: Convolutional Residual Learning for Visual Tracking

• intro: ICCV 2017
• project page: http://www.cs.cityu.edu.hk/~yibisong/iccv17/index.html
• arxiv: https://arxiv.org/abs/1708.00225
• github: https://github.com/ybsong00/CREST-Release

Learning Policies for Adaptive Tracking with Deep Feature Cascades

• intro: ICCV 2017 Spotlight
• arxiv: https://arxiv.org/abs/1708.02973

Recurrent Filter Learning for Visual Tracking

• intro: ICCV 2017 Workshop on VOT
• arxiv: https://arxiv.org/abs/1708.03874

Correlation Filters with Weighted Convolution Responses

• intro: ICCV 2017 workshop. 5th visual object tracking(VOT) tracker CFWCR
• paper: http://openaccess.thecvf.com/content_ICCV_2017_workshops/papers/w28/He_Correlation_Filters_With_ICCV_2017_paper.pdf
• github: https://github.com/he010103/CFWCR

Semantic Texture for Robust Dense Tracking

https://arxiv.org/abs/1708.08844

Learning Multi-frame Visual Representation for Joint Detection and Tracking of Small Objects

https://arxiv.org/abs/1709.04666

Tracking Persons-of-Interest via Unsupervised Representation Adaptation

• intro: Northwestern Polytechnical University & Virginia Tech & Hanyang University
• keywords: Multi-face tracking
• project page: http://vllab1.ucmerced.edu/~szhang/FaceTracking/
• arxiv: https://arxiv.org/abs/1710.02139

End-to-end Flow Correlation Tracking with Spatial-temporal Attention

https://arxiv.org/abs/1711.01124

UCT: Learning Unified Convolutional Networks for Real-time Visual Tracking

• intro: ICCV 2017 Workshops
• arxiv: https://arxiv.org/abs/1711.04661

Pixel-wise object tracking

https://arxiv.org/abs/1711.07377

MAVOT: Memory-Augmented Video Object Tracking

https://arxiv.org/abs/1711.09414

Learning Hierarchical Features for Visual Object Tracking with Recursive Neural Networks

https://arxiv.org/abs/1801.02021

Parallel Tracking and Verifying

https://arxiv.org/abs/1801.10496

Saliency-Enhanced Robust Visual Tracking

https://arxiv.org/abs/1802.02783

A Twofold Siamese Network for Real-Time Object Tracking

• intro: CVPR 2018
• arxiv: https://arxiv.org/abs/1802.08817

Learning Dynamic Memory Networks for Object Tracking

https://arxiv.org/abs/1803.07268

Context-aware Deep Feature Compression for High-speed Visual Tracking

• intro: CVPR 2018
• arxiv: https://arxiv.org/abs/1803.10537

VITAL: VIsual Tracking via Adversarial Learning

• intro: CVPR 2018 Spotlight
• arixv: https://arxiv.org/abs/1804.04273

Unveiling the Power of Deep Tracking

https://arxiv.org/abs/1804.06833

A Novel Low-cost FPGA-based Real-time Object Tracking System

• intro: ASICON 2017
• arxiv: https://arxiv.org/abs/1804.05535

MV-YOLO: Motion Vector-aided Tracking by Semantic Object Detection

https://arxiv.org/abs/1805.00107

Multi-Object Tracking (MOT)

Virtual Worlds as Proxy for Multi-Object Tracking Analysis

• arxiv: http://arxiv.org/abs/1605.06457
• dataset(Virtual KITTI): http://www.xrce.xerox.com/Research-Development/Computer-Vision/Proxy-Virtual-Worlds

Multi-Person Tracking by Multicut and Deep Matching

• arxiv: http://arxiv.org/abs/1608.05404

Multi-Class Multi-Object Tracking using Changing Point Detection

• intro: changing point detection, entity transition, object detection from video, convolutional neural network
• arxiv: http://arxiv.org/abs/1608.08434

POI: Multiple Object Tracking with High Performance Detection and Appearance Feature

• intro: ECCV workshop BMTT 2016. Sensetime
• keywords: KDNT
• arxiv: https://arxiv.org/abs/1610.06136

Simple Online and Realtime Tracking with a Deep Association Metric

• arxiv: https://arxiv.org/abs/1703.07402
• mot challenge: https://motchallenge.net/tracker/DeepSORT_2
• github(official, Python): https://github.com/nwojke/deep_sort
• github(C++): https://github.com/oylz/ds

Deep Network Flow for Multi-Object Tracking

• intro: CVPR 2017
• arxiv: https://arxiv.org/abs/1706.08482

Online Multi-Object Tracking Using CNN-based Single Object Tracker with Spatial-Temporal Attention Mechanism

https://arxiv.org/abs/1708.02843

Recurrent Autoregressive Networks for Online Multi-Object Tracking

https://arxiv.org/abs/1711.02741

SOT for MOT

• intro: Tsinghua University & Megvii Inc. (Face++)
• arxiv: https://arxiv.org/abs/1712.01059

Multi-Target, Multi-Camera Tracking by Hierarchical Clustering: Recent Progress on DukeMTMC Project

https://arxiv.org/abs/1712.09531

Multiple Target Tracking by Learning Feature Representation and Distance Metric Jointly

https://arxiv.org/abs/1802.03252

Tracking Noisy Targets: A Review of Recent Object Tracking Approaches

https://arxiv.org/abs/1802.03098

Machine Learning Methods for Solving Assignment Problems in Multi-Target Tracking

• intro: University of Florida
• arxiv: https://arxiv.org/abs/1802.06897

Tracking by Prediction: A Deep Generative Model for Mutli-Person localisation and Tracking

• intro: WACV 2018. Queensland University of Technology (QUT)
• arxiv: https://arxiv.org/abs/1803.03347

Learning to Detect and Track Visible and Occluded Body Joints in a Virtual World

• intro: University of Modena and Reggio Emilia
• arxiv: https://arxiv.org/abs/1803.08319

Features for Multi-Target Multi-Camera Tracking and Re-Identification

• intro: CVPR 2018
• intro: https://arxiv.org/abs/1803.10859

Trajectory Factory: Tracklet Cleaving and Re-connection by Deep Siamese Bi-GRU for Multiple Object Tracking

• intro: Peking University
• arxiv: https://arxiv.org/abs/1804.04555

Tracking with Reinforcement Learning

Deep Reinforcement Learning for Visual Object Tracking in Videos

• intro: University of California at Santa Barbara & Samsung Research America
• arxiv: https://arxiv.org/abs/1701.08936

Visual Tracking by Reinforced Decision Making

• arxiv: https://arxiv.org/abs/1702.06291

End-to-end Active Object Tracking via Reinforcement Learning

https://arxiv.org/abs/1705.10561

Action-Decision Networks for Visual Tracking with Deep Reinforcement Learning

• project page: https://sites.google.com/view/cvpr2017-adnet
• paper: https://drive.google.com/file/d/0B34VXh5mZ22cZUs2Umc1cjlBMFU/view?usp=drive_web

Tracking as Online Decision-Making: Learning a Policy from Streaming Videos with Reinforcement Learning

https://arxiv.org/abs/1707.04991

Detect to Track and Track to Detect

• intro: ICCV 2017
• project page: https://www.robots.ox.ac.uk/~vgg/research/detect-track/
• arxiv: https://arxiv.org/abs/1710.03958
• github: https://github.com/feichtenhofer/Detect-Track

Projects

Tensorflow_Object_Tracking_Video

• intro: Object Tracking in Tensorflow ( Localization Detection Classification ) developed to partecipate to ImageNET VID competition
• github: https://github.com/DrewNF/Tensorflow_Object_Tracking_Video

Papers

Papers

Types of RNN

1) Plain Tanh Recurrent Nerual Networks

2) Gated Recurrent Neural Networks (GRU)

3) Long Short-Term Memory (LSTM)

Tutorials

The Unreasonable Effectiveness of Recurrent Neural Networks

• blog: http://karpathy.github.io/2015/05/21/rnn-effectiveness/

Understanding LSTM Networks

• blog: http://colah.github.io/posts/2015-08-Understanding-LSTMs/
• blog(zh): http://www.jianshu.com/p/9dc9f41f0b29

A Beginner’s Guide to Recurrent Networks and LSTMs

http://deeplearning4j.org/lstm.html

A Deep Dive into Recurrent Neural Nets

http://nikhilbuduma.com/2015/01/11/a-deep-dive-into-recurrent-neural-networks/

Exploring LSTMs

http://blog.echen.me/2017/05/30/exploring-lstms/

A tutorial on training recurrent neural networks, covering BPPT, RTRL, EKF and the “echo state network” approach

• paper: http://minds.jacobs-university.de/sites/default/files/uploads/papers/ESNTutorialRev.pdf
• slides: http://deeplearning.cs.cmu.edu/notes/shaoweiwang.pdf

Long Short-Term Memory: Tutorial on LSTM Recurrent Networks

http://people.idsia.ch/~juergen/lstm/index.htm

LSTM implementation explained

http://apaszke.github.io/lstm-explained.html

Recurrent Neural Networks Tutorial

• Part 1(Introduction to RNNs): http://www.wildml.com/2015/09/recurrent-neural-networks-tutorial-part-1-introduction-to-rnns/
• Part 2(Implementing a RNN using Python and Theano): http://www.wildml.com/2015/09/recurrent-neural-networks-tutorial-part-2-implementing-a-language-model-rnn-with-python-numpy-and-theano/
• Part 3(Understanding the Backpropagation Through Time (BPTT) algorithm): http://www.wildml.com/2015/10/recurrent-neural-networks-tutorial-part-3-backpropagation-through-time-and-vanishing-gradients/
• Part 4(Implementing a GRU/LSTM RNN): http://www.wildml.com/2015/10/recurrent-neural-network-tutorial-part-4-implementing-a-grulstm-rnn-with-python-and-theano/

Recurrent Neural Networks in DL4J

http://deeplearning4j.org/usingrnns.html

Learning RNN Hierarchies

• github: https://github.com/pranv/lrh/blob/master/about.md

Element-Research Torch RNN Tutorial for recurrent neural nets : let’s predict time series with a laptop GPU

• blog: https://christopher5106.github.io/deep/learning/2016/07/14/element-research-torch-rnn-tutorial.html

RNNs in Tensorflow, a Practical Guide and Undocumented Features

• blog: http://www.wildml.com/2016/08/rnns-in-tensorflow-a-practical-guide-and-undocumented-features/

Learning about LSTMs using Torch

• blog: http://kbullaughey.github.io/lstm-play/
• github: https://github.com/kbullaughey/lstm-play

Build a Neural Network (LIVE)

• intro: LSTM
• youtube: https://www.youtube.com/watch?v=KvoZU-ItDiE
• mirror: https://pan.baidu.com/s/1i4KoumL
• github: https://github.com/llSourcell/build_a_neural_net_live

Deriving LSTM Gradient for Backpropagation

http://wiseodd.github.io/techblog/2016/08/12/lstm-backprop/

TensorFlow RNN Tutorial

https://svds.com/tensorflow-rnn-tutorial/

RNN Training Tips and Tricks

https://github.com/karpathy/char-rnn#tips-and-tricks

Tips for Training Recurrent Neural Networks

http://danijar.com/tips-for-training-recurrent-neural-networks/

A Tour of Recurrent Neural Network Algorithms for Deep Learning

http://machinelearningmastery.com/recurrent-neural-network-algorithms-for-deep-learning/

Fundamentals of Deep Learning – Introduction to Recurrent Neural Networks

https://www.analyticsvidhya.com/blog/2017/12/introduction-to-recurrent-neural-networks/

Essentials of Deep Learning : Introduction to Long Short Term Memory

https://www.analyticsvidhya.com/blog/2017/12/fundamentals-of-deep-learning-introduction-to-lstm/

How to build a Recurrent Neural Network in TensorFlow

How to build a Recurrent Neural Network in TensorFlow (1/7)

https://medium.com/@erikhallstrm/hello-world-rnn-83cd7105b767#.2vozogqf7

Using the RNN API in TensorFlow (2/7)

https://medium.com/@erikhallstrm/tensorflow-rnn-api-2bb31821b185#.h0ycrjuo3

Using the LSTM API in TensorFlow (3/7)

https://medium.com/@erikhallstrm/using-the-tensorflow-lstm-api-3-7-5f2b97ca6b73#.k7aciqaxn

Using the Multilayered LSTM API in TensorFlow (4/7)

https://medium.com/@erikhallstrm/using-the-tensorflow-multilayered-lstm-api-f6e7da7bbe40#.dj7dy92m5

Using the DynamicRNN API in TensorFlow (5/7)

https://medium.com/@erikhallstrm/using-the-dynamicrnn-api-in-tensorflow-7237aba7f7ea#.49qw259ks

Using the Dropout API in TensorFlow (6/7)

https://medium.com/@erikhallstrm/using-the-dropout-api-in-tensorflow-2b2e6561dfeb#.a7mc3o9aq

Unfolding RNNs

Unfolding RNNs: RNN : Concepts and Architectures

• blog: http://suriyadeepan.github.io/2017-01-07-unfolding-rnn/

Unfolding RNNs II: Vanilla, GRU, LSTM RNNs from scratch in Tensorflow

• blog: http://suriyadeepan.github.io/2017-02-13-unfolding-rnn-2/
• github: https://github.com/suriyadeepan/rnn-from-scratch

Train RNN

On the difficulty of training Recurrent Neural Networks

• author: Razvan Pascanu, Tomas Mikolov, Yoshua Bengio
• arxiv: http://arxiv.org/abs/1211.5063
• video talks: http://techtalks.tv/talks/on-the-difficulty-of-training-recurrent-neural-networks/58134/

A Simple Way to Initialize Recurrent Networks of Rectified Linear Units

• arxiv: http://arxiv.org/abs/1504.00941
• gitxiv: http://gitxiv.com/posts/7j5JXvP3kn5Jf8Waj/irnn-experiment-with-pixel-by-pixel-sequential-mnist
• github: https://github.com/fchollet/keras/blob/master/examples/mnist_irnn.py
• github: https://gist.github.com/GabrielPereyra/353499f2e6e407883b32
• blog(“Implementing Recurrent Neural Net using chainer!”): http://t-satoshi.blogspot.jp/2015/06/implementing-recurrent-neural-net-using.html
• reddit: https://www.reddit.com/r/MachineLearning/comments/31rinf/150400941_a_simple_way_to_initialize_recurrent/
• reddit: https://www.reddit.com/r/MachineLearning/comments/32tgvw/has_anyone_been_able_to_reproduce_the_results_in/

Batch Normalized Recurrent Neural Networks

• arxiv: https://arxiv.org/abs/1510.01378

Sequence Level Training with Recurrent Neural Networks

• intro: ICLR 2016
• arxiv: http://arxiv.org/abs/1511.06732
• github: https://github.com/facebookresearch/MIXER
• notes: https://www.evernote.com/shard/s189/sh/ada01a82-70a9-48d4-985c-20492ab91e84/8da92be19e704996dc2b929473abed46

Training Recurrent Neural Networks (PhD thesis)

• atuhor: Ilya Sutskever
• thesis: https://www.cs.utoronto.ca/~ilya/pubs/ilya_sutskever_phd_thesis.pdf

Deep learning for control using augmented Hessian-free optimization

• blog: https://studywolf.wordpress.com/2016/04/04/deep-learning-for-control-using-augmented-hessian-free-optimization/
• github: https://github.com/studywolf/blog/blob/master/train_AHF/train_hf.py

Hierarchical Conflict Propagation: Sequence Learning in a Recurrent Deep Neural Network

• arxiv: http://arxiv.org/abs/1602.08118

Recurrent Batch Normalization

• arxiv: http://arxiv.org/abs/1603.09025
• github: https://github.com/iassael/torch-bnlstm
• github: https://github.com/cooijmanstim/recurrent-batch-normalization
• github(“LSTM with Batch Normalization”): https://github.com/fchollet/keras/pull/2183
• github: https://github.com/jihunchoi/recurrent-batch-normalization-pytorch
• notes: http://www.shortscience.org/paper?bibtexKey=journals/corr/CooijmansBLC16

Batch normalized LSTM for Tensorflow

• blog: http://olavnymoen.com/2016/07/07/rnn-batch-normalization
• github: https://github.com/OlavHN/bnlstm

Optimizing Performance of Recurrent Neural Networks on GPUs

• arxiv: http://arxiv.org/abs/1604.01946
• github: https://github.com/parallel-forall/code-samples/blob/master/posts/rnn/LSTM.cu

Path-Normalized Optimization of Recurrent Neural Networks with ReLU Activations

• arxiv: http://arxiv.org/abs/1605.07154

Explaining and illustrating orthogonal initialization for recurrent neural networks

• blog: http://smerity.com/articles/2016/orthogonal_init.html

Professor Forcing: A New Algorithm for Training Recurrent Networks

• intro: NIPS 2016
• arxiv: https://arxiv.org/abs/1610.09038
• github: https://github.com/anirudh9119/LM_GANS

Phased LSTM: Accelerating Recurrent Network Training for Long or Event-based Sequences

• intro: Selected for an oral presentation at NIPS, 2016. University of Zurich and ETH Zurich
• arxiv: https://arxiv.org/abs/1610.09513
• github: https://github.com/dannyneil/public_plstm
• github: https://github.com/Enny1991/PLSTM
• github: https://github.com/philipperemy/tensorflow-phased-lstm
• github: https://www.tensorflow.org/api_docs/python/tf/contrib/rnn/PhasedLSTMCell
• reddit: https://www.reddit.com/r/MachineLearning/comments/5bmfw5/r_phased_lstm_accelerating_recurrent_network/

Tuning Recurrent Neural Networks with Reinforcement Learning (RL Tuner)

• paper: http://openreview.net/pdf?id=BJ8fyHceg
• blog: https://magenta.tensorflow.org/2016/11/09/tuning-recurrent-networks-with-reinforcement-learning/
• github: https://github.com/tensorflow/magenta/tree/master/magenta/models/rl_tuner

Capacity and Trainability in Recurrent Neural Networks

• intro: Google Brain
• arxiv: https://arxiv.org/abs/1611.09913

Learn To Execute Programs

Learning to Execute

• arxiv: http://arxiv.org/abs/1410.4615
• github: https://github.com/wojciechz/learning_to_execute
• github(Tensorflow): https://github.com/raindeer/seq2seq_experiments

Neural Programmer-Interpreters

• intro: Google DeepMind. ICLR 2016 Best Paper
• arxiv: http://arxiv.org/abs/1511.06279
• project page: http://www-personal.umic (Google DeepMind. ICLR 2016 Best Paper)h.edu/~reedscot/iclr_project.html
• github: https://github.com/mokemokechicken/keras_npi

A Programmer-Interpreter Neural Network Architecture for Prefrontal Cognitive Control

• paper: https://www.researchgate.net/publication/273912337_A_ProgrammerInterpreter_Neural_Network_Architecture_for_Prefrontal_Cognitive_Control

Convolutional RNN: an Enhanced Model for Extracting Features from Sequential Data

• arxiv: http://arxiv.org/abs/1602.05875

Neural Random-Access Machines

• arxiv: http://arxiv.org/abs/1511.06392

Attention Models

Recurrent Models of Visual Attention

• intro: Google DeepMind. NIPS 2014
• arxiv: http://arxiv.org/abs/1406.6247
• data: https://github.com/deepmind/mnist-cluttered
• github: https://github.com/Element-Research/rnn/blob/master/examples/recurrent-visual-attention.lua

Recurrent Model of Visual Attention

• intro: Google DeepMind
• paper: http://arxiv.org/abs/1406.6247
• gitxiv: http://gitxiv.com/posts/ZEobCXSh23DE8a8mo/recurrent-models-of-visual-attention
• blog: http://torch.ch/blog/2015/09/21/rmva.html
• github: https://github.com/Element-Research/rnn/blob/master/scripts/evaluate-rva.lua

Show, Attend and Tell: Neural Image Caption Generation with Visual Attention

• arxiv: http://arxiv.org/abs/1502.03044
• github: https://github.com/kelvinxu/arctic-captions

A Neural Attention Model for Abstractive Sentence Summarization

• intro: EMNLP 2015. Facebook AI Research
• arxiv: http://arxiv.org/abs/1509.00685
• github: https://github.com/facebook/NAMAS

Effective Approaches to Attention-based Neural Machine Translation

• intro: EMNLP 2015
• paper: http://nlp.stanford.edu/pubs/emnlp15_attn.pdf
• project: http://nlp.stanford.edu/projects/nmt/
• github: https://github.com/lmthang/nmt.matlab

Generating Images from Captions with Attention

• arxiv: http://arxiv.org/abs/1511.02793
• github: https://github.com/emansim/text2image
• demo: http://www.cs.toronto.edu/~emansim/cap2im.html

Attention and Memory in Deep Learning and NLP

• blog: http://www.wildml.com/2016/01/attention-and-memory-in-deep-learning-and-nlp/

Survey on the attention based RNN model and its applications in computer vision

• arxiv: http://arxiv.org/abs/1601.06823

Attention in Long Short-Term Memory Recurrent Neural Networks

• blog: http://machinelearningmastery.com/attention-long-short-term-memory-recurrent-neural-networks/

How to Visualize Your Recurrent Neural Network with Attention in Keras

• blog: https://medium.com/datalogue/attention-in-keras-1892773a4f22
• github: https://github.com/datalogue/keras-attention

Papers

Generating Sequences With Recurrent Neural Networks

• arxiv: http://arxiv.org/abs/1308.0850
• github: https://github.com/hardmaru/write-rnn-tensorflow
• github: https://github.com/szcom/rnnlib
• blog: http://blog.otoro.net/2015/12/12/handwriting-generation-demo-in-tensorflow/

A Clockwork RNN

• arxiv: https://arxiv.org/abs/1402.3511
• github: https://github.com/makistsantekidis/clockworkrnn
• github: https://github.com/zergylord/ClockworkRNN

Unsupervised Learning of Video Representations using LSTMs

• intro: ICML 2015
• project page: http://www.cs.toronto.edu/~nitish/unsupervised_video/
• arxiv: http://arxiv.org/abs/1502.04681
• code: http://www.cs.toronto.edu/~nitish/unsupervised_video/unsup_video_lstm.tar.gz
• github: https://github.com/emansim/unsupervised-videos

An Empirical Exploration of Recurrent Network Architectures

• paper: http://jmlr.org/proceedings/papers/v37/jozefowicz15.pdf

Improved Semantic Representations From Tree-Structured Long Short-Term Memory Networks

• intro: ACL 2015. Tree RNNs aka Recursive Neural Networks
• arxiv: https://arxiv.org/abs/1503.00075
• slides: http://lit.eecs.umich.edu/wp-content/uploads/2015/10/tree-lstms.pptx
• gitxiv: http://www.gitxiv.com/posts/esrArT2iLmSfNRrto/tree-structured-long-short-term-memory-networks
• github: https://github.com/stanfordnlp/treelstm
• github: https://github.com/ofirnachum/tree_rnn

LSTM: A Search Space Odyssey

• arxiv: http://arxiv.org/abs/1503.04069
• notes: https://www.evernote.com/shard/s189/sh/48da42c5-8106-4f0d-b835-c203466bfac4/50d7a3c9a961aefd937fae3eebc6f540
• blog(“Dissecting the LSTM”): https://medium.com/jim-fleming/implementing-lstm-a-search-space-odyssey-7d50c3bacf93#.crg8pztop
• github: https://github.com/jimfleming/lstm_search

Inferring Algorithmic Patterns with Stack-Augmented Recurrent Nets

• arxiv: http://arxiv.org/abs/1503.01007
• github: https://github.com/facebook/Stack-RNN

A Critical Review of Recurrent Neural Networks for Sequence Learning

• arxiv: http://arxiv.org/abs/1506.00019
• review: http://blog.terminal.com/a-thorough-and-readable-review-on-rnns/

Visualizing and Understanding Recurrent Networks

• intro: ICLR 2016. Andrej Karpathy, Justin Johnson, Fei-Fei Li
• arxiv: http://arxiv.org/abs/1506.02078
• slides: http://www.robots.ox.ac.uk/~seminars/seminars/Extra/2015_07_06_AndrejKarpathy.pdf
• github: https://github.com/karpathy/char-rnn

Scheduled Sampling for Sequence Prediction with Recurrent Neural Networks

• intro: Winner of MSCOCO image captioning challenge, 2015
• arxiv: http://arxiv.org/abs/1506.03099

Convolutional LSTM Network: A Machine Learning Approach for Precipitation Nowcasting

• arxiv: https://arxiv.org/abs/1506.04214
• github: https://github.com/loliverhennigh/Convolutional-LSTM-in-Tensorflow

Grid Long Short-Term Memory

• arxiv: http://arxiv.org/abs/1507.01526
• github(Torch7): https://github.com/coreylynch/grid-lstm/

Depth-Gated LSTM

• arxiv: http://arxiv.org/abs/1508.03790
• github: GitHub(dglstm.h+dglstm.cc)

Deep Knowledge Tracing

• paper: https://web.stanford.edu/~cpiech/bio/papers/deepKnowledgeTracing.pdf
• github: https://github.com/chrispiech/DeepKnowledgeTracing

Top-down Tree Long Short-Term Memory Networks

• arxiv: http://arxiv.org/abs/1511.00060
• github: https://github.com/XingxingZhang/td-treelstm

Improving performance of recurrent neural network with relu nonlinearity

• intro: ICLR 2016
• arxiv: https://arxiv.org/abs/1511.03771

Alternative structures for character-level RNNs

• intro: INRIA & Facebook AI Research. ICLR 2016
• arxiv: http://arxiv.org/abs/1511.06303
• github: https://github.com/facebook/Conditional-character-based-RNN

Long Short-Term Memory-Networks for Machine Reading

• arxiv: http://arxiv.org/abs/1601.06733
• github: https://github.com/cheng6076/SNLI-attention

Lipreading with Long Short-Term Memory

• arxiv: http://arxiv.org/abs/1601.08188

Associative Long Short-Term Memory

• arxiv: http://arxiv.org/abs/1602.03032
• github: https://github.com/mohammadpz/Associative_LSTM

Representation of linguistic form and function in recurrent neural networks

• arxiv: http://arxiv.org/abs/1602.08952

Architectural Complexity Measures of Recurrent Neural Networks

• arxiv: http://arxiv.org/abs/1602.08210

Easy-First Dependency Parsing with Hierarchical Tree LSTMs

• arxiv: http://arxiv.org/abs/1603.00375

Training Input-Output Recurrent Neural Networks through Spectral Methods

• arxiv: http://arxiv.org/abs/1603.00954

Sequential Neural Models with Stochastic Layers

• arxiv: https://arxiv.org/abs/1605.07571
• github: https://github.com/marcofraccaro/srnn

Neural networks with differentiable structure

• arxiv: http://arxiv.org/abs/1606.06216
• github: https://github.com/ThomasMiconi/DiffRNN

What You Get Is What You See: A Visual Markup Decompiler

• project page: http://lstm.seas.harvard.edu/latex/
• arxiv: http://arxiv.org/abs/1609.04938
• github: https://github.com/harvardnlp/im2markup
• github(Tensorflow): https://github.com/ssampang/im2latex
• github: https://github.com/opennmt/im2text
• github: https://github.com/ritheshkumar95/im2latex-tensorflow

Hybrid computing using a neural network with dynamic external memory

• intro: Nature 2016
• keywords: Differentiable Neural Computer (DNC) https://www.nature.com/articles/nature20101.epdf?author_access_token=ImTXBI8aWbYxYQ51Plys8NRgN0jAjWel9jnR3ZoTv0MggmpDmwljGswxVdeocYSurJ3hxupzWuRNeGvvXnoO8o4jTJcnAyhGuZzXJ1GEaD-Z7E6X_a9R-xqJ9TfJWBqz
• github: https://github.com/deepmind/dnc

Skip RNN: Learning to Skip State Updates in Recurrent Neural Networks

• project page: https://imatge-upc.github.io/skiprnn-2017-telecombcn/
• arxiv: https://arxiv.org/abs/1708.06834

Dilated Recurrent Neural Networks

• intro: NIPS 2017. IBM & University of Illinois at Urbana-Champaign
• keywords: DilatedRNN
• arxiv: https://arxiv.org/abs/1710.02224
• github(Tensorflow): https://github.com/code-terminator/DilatedRNN https://github.com/zalandoresearch/pt-dilate-rnn

Excitation Backprop for RNNs

https://arxiv.org/abs/1711.06778

Recurrent Relational Networks for Complex Relational Reasoning

• project page: https://rasmusbergpalm.github.io/recurrent-relational-networks/
• arxiv: https://arxiv.org/abs/1711.08028
• github: https://github.com//rasmusbergpalm/recurrent-relational-networks

Learning Compact Recurrent Neural Networks with Block-Term Tensor Decomposition

• intro: University of Electronic Science and Technology of China & Brown University & University of Utah & XJERA LABS PTE.LTD
• arxiv: https://arxiv.org/abs/1712.05134

LSTMVis

Visual Analysis of Hidden State Dynamics in Recurrent Neural Networks

• homepage: http://lstm.seas.harvard.edu/
• demo: http://lstm.seas.harvard.edu/client/index.html
• arxiv: https://arxiv.org/abs/1606.07461
• github: https://github.com/HendrikStrobelt/LSTMVis

Recurrent Memory Array Structures

• arxiv: https://arxiv.org/abs/1607.03085
• github: https://github.com/krocki/ArrayLSTM

Recurrent Highway Networks

• author: Julian Georg Zilly, Rupesh Kumar Srivastava, Jan Koutník, Jürgen Schmidhuber
• arxiv: http://arxiv.org/abs/1607.03474
• github(Tensorflow+Torch): https://github.com/julian121266/RecurrentHighwayNetworks/

DeepSoft: A vision for a deep model of software

• arxiv: http://arxiv.org/abs/1608.00092

Recurrent Neural Networks With Limited Numerical Precision

• arxiv: http://arxiv.org/abs/1608.06902

Hierarchical Multiscale Recurrent Neural Networks

• arxiv: http://arxiv.org/abs/1609.01704
• notes: https://github.com/dennybritz/deeplearning-papernotes/blob/master/notes/hm-rnn.md
• notes: https://medium.com/@jimfleming/notes-on-hierarchical-multiscale-recurrent-neural-networks-7362532f3b64#.pag4kund0

LightRNN

LightRNN: Memory and Computation-Efficient Recurrent Neural Networks

• intro: NIPS 2016
• arxiv: https://arxiv.org/abs/1610.09893

Full-Capacity Unitary Recurrent Neural Networks

• intro: NIPS 2016
• arxiv: https://arxiv.org/abs/1611.00035
• github: https://github.com/stwisdom/urnn

DeepCoder: Learning to Write Programs

• arxiv: https://arxiv.org/abs/1611.01989

shuttleNet: A biologically-inspired RNN with loop connection and parameter sharing

• arxiv: https://arxiv.org/abs/1611.05216

Tracking the World State with Recurrent Entity Networks

• intro: Facebook AI Research
• arxiv: https://arxiv.org/abs/1612.03969
• github(Official): https://github.com/facebook/MemNN/tree/master/EntNet-babi

Robust LSTM-Autoencoders for Face De-Occlusion in the Wild

• intro: National University of Singapore & Peking University
• arxiv: https://arxiv.org/abs/1612.08534

Simplified Gating in Long Short-term Memory (LSTM) Recurrent Neural Networks

• arxiv: https://arxiv.org/abs/1701.03441
• github: https://github.com/jingweimo/Modified-LSTM

The Statistical Recurrent Unit

• intro: CMU
• arxiv: https://arxiv.org/abs/1703.00381

Factorization tricks for LSTM networks

• intro: ICLR 2017 Workshop
• arxiv: https://arxiv.org/abs/1703.10722
• github: https://github.com/okuchaiev/f-lm

Bayesian Recurrent Neural Networks

• intro: UC Berkeley
• arxiv: https://arxiv.org/abs/1704.02798
• github: https://github.com/mirceamironenco/BayesianRecurrentNN

Fast-Slow Recurrent Neural Networks

• arxiv: https://arxiv.org/abs/1705.08639
• github: https://github.com/amujika/Fast-Slow-LSTM

Visualizing LSTM decisions

https://arxiv.org/abs/1705.08153

Recurrent Additive Networks

• intro: [University of Washington & Allen Institute for Artificial Intelligence
• arxiv: https://arxiv.org/abs/1705.07393
• paper: http://www.kentonl.com/pub/llz.2017.pdf
• github(PyTorch): https://github.com/bheinzerling/ran

Recent Advances in Recurrent Neural Networks

• intro: University of Toronto & University of Waterloo
• arxiv: https://arxiv.org/abs/1801.01078

Projects

NeuralTalk (Deprecated): a Python+numpy project for learning Multimodal Recurrent Neural Networks that describe images with sentences

• github: https://github.com/karpathy/neuraltalk

NeuralTalk2: Efficient Image Captioning code in Torch, runs on GPU

• github: https://github.com/karpathy/neuraltalk2

char-rnn in Blocks

• github: https://github.com/johnarevalo/blocks-char-rnn

Project: pycaffe-recurrent

• code: https://github.com/kuprel/pycaffe-recurrent/

Using neural networks for password cracking

• blog: https://0day.work/using-neural-networks-for-password-cracking/
• github: https://github.com/gehaxelt/RNN-Passwords

torch-rnn: Efficient, reusable RNNs and LSTMs for torch

• github: https://github.com/jcjohnson/torch-rnn

Deploying a model trained with GPU in Torch into JavaScript, for everyone to use

• blog: http://testuggine.ninja/blog/torch-conversion
• demo: http://testuggine.ninja/DRUMPF-9000/
• github: https://github.com/Darktex/char-rnn

LSTM implementation on Caffe

• github: https://github.com/junhyukoh/caffe-lstm

JNN: Java Neural Network Library

• intro: C2W model, LSTM-based Language Model, LSTM-based Part-Of-Speech-Tagger Model
• github: https://github.com/wlin12/JNN

LSTM-Autoencoder: Seq2Seq LSTM Autoencoder

• github: https://github.com/cheng6076/LSTM-Autoencoder

RNN Language Model Variations

• intro: Standard LSTM, Gated Feedback LSTM, 1D-Grid LSTM
• github: https://github.com/cheng6076/mlm

keras-extra: Extra Layers for Keras to connect CNN with RNN

• github: https://github.com/anayebi/keras-extra

Dynamic Vanilla RNN, GRU, LSTM,2layer Stacked LSTM with Tensorflow Higher Order Ops

• github: https://github.com/KnHuq/Dynamic_RNN_Tensorflow

PRNN: A fast implementation of recurrent neural network layers in CUDA

• intro: Baidu Research
• blog: https://svail.github.io/persistent_rnns/
• github: https://github.com/baidu-research/persistent-rnn

min-char-rnn: Minimal character-level language model with a Vanilla Recurrent Neural Network, in Python/numpy

• github: https://github.com/weixsong/min-char-rnn

rnn: Recurrent Neural Network library for Torch7’s nn

• github: https://github.com/Element-Research/rnn

word-rnn-tensorflow: Multi-layer Recurrent Neural Networks (LSTM, RNN) for word-level language models in Python using TensorFlow

• github: https://github.com/hunkim/word-rnn-tensorflow

tf-char-rnn: Tensorflow implementation of char-rnn

• github: https://github.com/shagunsodhani/tf-char-rnn

translit-rnn: Automatic transliteration with LSTM

• blog: http://yerevann.github.io/2016/09/09/automatic-transliteration-with-lstm/
• github: https://github.com/YerevaNN/translit-rnn

tf_lstm.py: Simple implementation of LSTM in Tensorflow in 50 lines (+ 130 lines of data generation and comments)

• gist: https://gist.github.com/nivwusquorum/b18ce332bde37e156034e5d3f60f8a23

Handwriting generating with RNN

• github: https://github.com/Arn-O/kadenze-deep-creative-apps/blob/master/final-project/glyphs-rnn.ipynb

RecNet - Recurrent Neural Network Framework

• github: https://github.com/joergfranke/recnet

Blogs

Survey on Attention-based Models Applied in NLP

http://yanran.li/peppypapers/2015/10/07/survey-attention-model-1.html

Survey on Advanced Attention-based Models

http://yanran.li/peppypapers/2015/10/07/survey-attention-model-2.html

Online Representation Learning in Recurrent Neural Language Models

http://www.marekrei.com/blog/online-representation-learning-in-recurrent-neural-language-models/

Fun with Recurrent Neural Nets: One More Dive into CNTK and TensorFlow

http://esciencegroup.com/2016/03/04/fun-with-recurrent-neural-nets-one-more-dive-into-cntk-and-tensorflow/

Materials to understand LSTM

https://medium.com/@shiyan/materials-to-understand-lstm-34387d6454c1#.4mt3bzoau

Understanding LSTM and its diagrams

:star::star::star::star::star:

• blog: https://medium.com/@shiyan/understanding-lstm-and-its-diagrams-37e2f46f1714
• slides: https://github.com/shi-yan/FreeWill/blob/master/Docs/Diagrams/lstm_diagram.pptx

Persistent RNNs: 30 times faster RNN layers at small mini-batch sizes

Persistent RNNs: Stashing Recurrent Weights On-Chip

• intro: Greg Diamos, Baidu Silicon Valley AI Lab
• paper: http://jmlr.org/proceedings/papers/v48/diamos16.pdf
• blog: http://svail.github.io/persistent_rnns/
• slides: http://on-demand.gputechconf.com/gtc/2016/presentation/s6673-greg-diamos-persisten-rnns.pdf

All of Recurrent Neural Networks

https://medium.com/@jianqiangma/all-about-recurrent-neural-networks-9e5ae2936f6e#.q4s02elqg

Rolling and Unrolling RNNs

https://shapeofdata.wordpress.com/2016/04/27/rolling-and-unrolling-rnns/

Sequence prediction using recurrent neural networks(LSTM) with TensorFlow: LSTM regression using TensorFlow

• blog: http://mourafiq.com/2016/05/15/predicting-sequences-using-rnn-in-tensorflow.html
• github: https://github.com/mouradmourafiq/tensorflow-lstm-regression

LSTMs

• blog: https://shapeofdata.wordpress.com/2016/06/04/lstms/

Machines and Magic: Teaching Computers to Write Harry Potter

• blog: https://medium.com/@joycex99/machines-and-magic-teaching-computers-to-write-harry-potter-37839954f252#.4fxemal9t
• github: https://github.com/joycex99/hp-word-model

Crash Course in Recurrent Neural Networks for Deep Learning

http://machinelearningmastery.com/crash-course-recurrent-neural-networks-deep-learning/

Understanding Stateful LSTM Recurrent Neural Networks in Python with Keras

http://machinelearningmastery.com/understanding-stateful-lstm-recurrent-neural-networks-python-keras/

Recurrent Neural Networks in Tensorflow

• part I: http://r2rt.com/recurrent-neural-networks-in-tensorflow-i.html
• part II: http://r2rt.com/recurrent-neural-networks-in-tensorflow-ii.html

Written Memories: Understanding, Deriving and Extending the LSTM

http://r2rt.com/written-memories-understanding-deriving-and-extending-the-lstm.html

Attention and Augmented Recurrent Neural Networks

• blog: http://distill.pub/2016/augmented-rnns/
• github: https://github.com/distillpub/post–augmented-rnns

Interpreting and Visualizing Neural Networks for Text Processing

https://civisanalytics.com/blog/data-science/2016/09/22/neural-network-visualization/

A simple design pattern for recurrent deep learning in TensorFlow

• blog: https://medium.com/@devnag/a-simple-design-pattern-for-recurrent-deep-learning-in-tensorflow-37aba4e2fd6b#.homq9zsyr
• github: https://github.com/devnag/tensorflow-bptt

RNN Spelling Correction: To crack a nut with a sledgehammer

• blog: https://medium.com/@yaoyaowd/rnn-spelling-correction-to-crack-a-nut-with-a-sledgehammer-7f5aa442c08c#.mc2ycyfda

Recurrent Neural Network Gradients, and Lessons Learned Therein

• blog: http://willwolf.io/en/2016/10/13/recurrent-neural-network-gradients-and-lessons-learned-therein/

A noob’s guide to implementing RNN-LSTM using Tensorflow

http://monik.in/a-noobs-guide-to-implementing-rnn-lstm-using-tensorflow/

Non-Zero Initial States for Recurrent Neural Networks

• blog: http://r2rt.com/non-zero-initial-states-for-recurrent-neural-networks.html

Interpreting neurons in an LSTM network

http://yerevann.github.io/2017/06/27/interpreting-neurons-in-an-LSTM-network/

Optimizing RNN (Baidu Silicon Valley AI Lab)

Optimizing RNN performance

• blog: http://svail.github.io/rnn_perf/

Optimizing RNNs with Differentiable Graphs

• blog: http://svail.github.io/diff_graphs/
• notes: http://research.baidu.com/svail-tech-notes-optimizing-rnns-differentiable-graphs/

Resources

Awesome Recurrent Neural Networks - A curated list of resources dedicated to RNN

• homepage: http://jiwonkim.org/awesome-rnn/
• github: https://github.com/kjw0612/awesome-rnn

Jürgen Schmidhuber’s page on Recurrent Neural Networks

http://people.idsia.ch/~juergen/rnn.html

Reading and Questions

Are there any Recurrent convolutional neural network network implementations out there ?

• reddit: https://www.reddit.com/r/MachineLearning/comments/4chu3y/are_there_any_recurrent_convolutional_neural/

Papers