• LIME – Local Interpretable Model-Agnostic Explanations: http://homes.cs.washington.edu/~marcotcr/blog/lime/
• MetaMind acquired by Salesforce: https://www.metamind.io/salesforce-acquisition
• Frontiers in Graphical Models Research?: https://www.reddit.com/r/MachineLearning/comments/4dbeeg/frontiers_in_graphical_models_research/
• ML and AI Podcasts to Recommend?: https://www.reddit.com/r/MachineLearning/comments/4dbfw6/ml_ai_podcasts_to_reccommend/
• Deep3D: Automatic 2D-to-3D Video Conversion with CNNs: http://dmlc.ml/mxnet/2016/04/04/deep3d-automatic-2d-to-3d-conversion-with-CNN.html
• Panama Papers Dataset 2016: https://github.com/amaboura/panama-papers-dataset-2016
• Pastalog: Simple, Realtime Visualization of NN Training Performance: https://github.com/rewonc/pastalog
• Deep Learning and the Future of AI: https://mediastream.cern.ch/MediaArchive/Video/Public2/weblecture-player/index.html?year=2016&lecture=510372
• NVidia Releases Tesla P100 with Pascal Architecture: http://www.nvidia.com/object/tesla-p100.html (NVidia P100 Architecture Deep Dive: http://www.pcgamer.com/nvidia-pascal-p100-architecture-deep-dive/
• State-of-the-arts in NLP and Their Discussion: https://www.reddit.com/r/MachineLearning/comments/4dkrw1/some_stateofthearts_in_natural_language/
• Towards Bayesian Deep Learning: A Survey: http://arxiv.org/abs/1604.01662
• Evolutionary Computation – Part 1: http://www.alanzucconi.com/2016/04/06/evolutionary-coputation-1/
• Introduction to Debugging NN: http://russellsstewart.com/notes/0.html
• TensorFlow Scan Examples: https://nbviewer.jupyter.org/github/rdipietro/tensorflow-notebooks/blob/master/tensorflow_scan_examples/tensorflow_scan_examples.ipynb
• Learning in Brains and Machines (2): The Dogma of Sparsity: http://blog.shakirm.com/2016/04/learning-in-brains-and-machines-2/
• Is It Better to Have More Training Data Which Are Close to the Decision Boundary, or More Data Which Are “Typical” of Their Class?: https://www.reddit.com/r/MachineLearning/comments/4dwa6q/is_it_better_to_have_more_training_data_which_are/
• Deep Networks with Stochastic Depth Keras Implementation: https://github.com/dblN/stochastic_depth_keras
• Deep Learning for Beginners: http://randomekek.github.io/deep/deeplearning.html
• Resources for GPU Programming: https://www.reddit.com/r/MachineLearning/comments/4dxgd5/resources_for_gpu_programming/
• RNN Online Demos: https://www.reddit.com/r/MachineLearning/comments/4e0aya/rnn_online_demos/
• Someone Shared His Solution for Andrew Ng’s ML Course: https://www.reddit.com/r/MachineLearning/comments/4dyf62/my_python_solutions_to_andrew_ngs_coursera_ml/
• ArgMax Differentiable?: https://www.reddit.com/r/MachineLearning/comments/4e2get/argmax_differentiable/
• How Do You Organize/Manage Your pdf/paper Collection?: https://www.reddit.com/r/MachineLearning/comments/4e0i4h/how_do_you_organize_manage_your_pdfpaper/
• Foveation-based Mechanisms Alleviate Adversarial Examples: http://arxiv.org/abs/1511.06292
• What Are the Primary Differences in Research That a Professor Does at a University vs. What an Industry Researcher Would Do?: https://www.reddit.com/r/MachineLearning/comments/4e2mlr/what_are_the_primary_differences_in_research_that/
• The Ultimate List of Machine Learning and Data Mining Books: http://www.aioptify.com/topmldmbooks.php
• Free eBook on Machine Learning for Recommender Systems: http://subhrajitroy.com/machine-learning-for-recommender-systems-ebook/

 

• Markov Chains Explained Visually: http://setosa.io/ev/markov-chains/
• Deep Learning for Real-Time Atari Game Play Using Offline Monte-Carlo Tree Search Planning: https://papers.nips.cc/paper/5421-deep-learning-for-real-time-atari-game-play-using-offline-monte-carlo-tree-search-planning.pdf
• Hyperparameter Selection: https://www.reddit.com/r/MachineLearning/comments/4cff3e/hyperparameter_selection/
• Can I Hug That? Classifier Trained To Tell You Wheter Or Not What’s In an Image Is Huggable: http://imgur.com/a/T1QNL
• Quadcopter Navigation in the Forest Using Deep Neural Networks: https://www.youtube.com/watch?v=umRdt3zGgpU&feature=share
• What Are You Working On?: https://www.reddit.com/r/MachineLearning/comments/4cf7k3/what_are_you_working_on/
• Table of XX2Vec Algorithms: http://datascienceassn.org/content/table-xx2vec-algorithms
• Attend, Infer, Repeat: Fast Scene Understanding with Generative Models: http://arxiv.org/abs/1603.08575
• IBM’s True North Being Deployed at Livermore National Lab: http://arstechnica.com/science/2016/03/ibms-brain-inspired-chip-finds-a-home-at-livermore-national-lab/
• Recurrent Batch Normalization: http://arxiv.org/abs/1603.09025
• Adaptive Computation Time for Recurrent Neural Networks: http://arxiv.org/abs/1603.08983
• OpenAI Has Started Hiring: https://openai.com/blog/team-plus-plus/
• arXiv-title-fixer: Paper Titles as Tab Titles on Chrome: https://www.reddit.com/r/MachineLearning/comments/4cq9b4/arxivtitlefixer_paper_titles_as_tab_titles_on/
• TFLearn: High Level API for TensorFlow: https://github.com/tflearn/tflearn
• MS Releases Their ML API: https://www.microsoft.com/cognitive-services/en-us/apis
• Music Language Modeling with RNN: http://yoavz.com/music_rnn/
• Recurrent Autoencoder?: https://www.reddit.com/r/MachineLearning/comments/4cvfkh/q_recurrent_autoencoder/
• Deep Networks with Stochastic Depth: http://arxiv.org/abs/1603.09382v1
• As Silicon Valley Fights for Talent, Universities Struggle to Hold on to Their Stars: http://www.economist.com/news/business/21695908-silicon-valley-fights-talent-universities-struggle-hold-their
• Generating Large Images from Latent Vectors: http://blog.otoro.net/2016/04/01/generating-large-images-from-latent-vectors/
• Pentagon Eyes Deep Machine Learning in Fight Against ISIS: http://www.govtech.com/federal/Pentagon-Eyes-Deep-Machine-Learning-in-Fight-Against-ISIS.html
• A Radical Approach to Computation with Real Numbers: http://www.johngustafson.net/presentations/Multicore2016-JLG.pdf
• Interview with Hassabis from MIT Tech Review: https://www.technologyreview.com/s/601139/how-google-plans-to-solve-artificial-intelligence/
• Chess Position Evaluation with CNN in Julia: https://www.reddit.com/r/MachineLearning/comments/4d5s5b/chess_position_evaluation_with_convolutional/
• Stochastic Depth Networks Will Become the New Normal: http://deliprao.com/archives/134
• lda2vec: Tools for Interpreting Natural Language: https://github.com/cemoody/lda2vec

 

• Machine Learning: An In-Depth, Non-Technical Guide Part 5: https://medium.com/innoarchitech-innovation-architecture-technology/machine-learning-an-in-depth-non-technical-guide-part-5-b134da025a3e#.cctsrpd4b
• Does The Number of Layers In An LSTM Network Affect Its Ability To Remember Long Patterns?: https://www.reddit.com/r/MachineLearning/comments/4behuh/does_the_number_of_layers_in_an_lstm_network/
• Deep Advances in Generative Modeling: https://www.youtube.com/watch?v=KeJINHjyzOU
• Structured VAEs: Composing Probabilistic Graphical Models and Variational Autoencoders: http://arxiv.org/abs/1603.06277
• Latent Predictor Networks for Code Generation: http://arxiv.org/abs/1603.06744
• text2image: Generating Images from Captions with Attention: https://github.com/emansim/text2image
• Google Releases Cloud Machine Learning with TensorFlow: http://googleresearch.blogspot.kr/2016/03/machine-learning-in-cloud-with.html?utm_source=feedburner&utm_medium=feed&utm_campaign=Feed:+blogspot/gJZg+(Official+Google+Research+Blog)
• Escaping from Saddle Points: http://www.offconvex.org/2016/03/22/saddlepoints/
• Recurrent Dropout without Memory Loss: http://arxiv.org/abs/1603.05118
• A Guide to Convolution Arithmetic for Deep Learning: http://arxiv.org/abs/1603.07285
• An ALternative Update Rule for Generative Adversarial Networks: http://www.inference.vc/an-alternative-update-rule-for-generative-adversarial-networks/
• NNPACK – Acceleration Package for Neural Networks on Multi-core CPUs: https://github.com/Maratyszcza/NNPACK
• Microsoft’s AI Millennial Chatbot Became A Racist Jerk After Less Than a Day on Twitter: http://qz.com/646825/microsofts-ai-millennial-chatbot-became-a-racist-jerk-after-less-than-a-day-on-twitter/
• Harnessing Deep Neural Networks with Logic Rules: http://arxiv.org/abs/1603.06318
• Adversarial Preference Loss: http://www.inference.vc/adversarial-preference-loss/
• Acceleration of Deep Neural Network Training with Resistive Cross-Point Devices: http://arxiv.org/abs/1603.07341
• Misleading Modelling: Overfitting, Cross-validation, and the Bias-variance Trade-off: http://blog.cambridgecoding.com/2016/03/24/misleading-modelling-overfitting-cross-validation-and-the-bias-variance-trade-off/
• L-BFGS and Neural Nets: https://www.reddit.com/r/MachineLearning/comments/4bys6n/lbfgs_and_neural_nets/
• TayAndYou – Toxic Before Human Contact: http://smerity.com/articles/2016/tayandyou.html
• Deep Spelling – Rethinking Spelling Correction in the 21st Century: https://medium.com/@majortal/deep-spelling-9ffef96a24f6#.tebxaodzi
• Generating Factoid Questions with Recurrent Neural Networks: http://arxiv.org/pdf/1603.06807v1.pdf
• A Dagger by Any Other Name: Scheduled Sampling: http://nlpers.blogspot.kr/2016/03/a-dagger-by-any-other-name-scheduled.html
• Theano 0.8 Released: http://deeplearning.net/software/theano/index.html

 

 

• Faster R-CNN: Towards Real-Time Object Detection with Region Proposal Network
• Learning visual biases from human imagination
• Tensorizing Neural Networks
• Active Learning from Weak and Strong Labelers
• Bidirectional Recurrent Neural Networks as Generative Models
• Unsupervised Learning by Program Synthesis
• Deep Visual Analogy-Making
• Deep Generative Image Models using a Laplacian Pyramid of Adversarial Networks
• Teaching Machines to Read and Comprehend
• Principal Differences Analysis: Interpretable Characterization of Differences between Distributions
• End-to-end Learning of LDA by Mirror-Descent Back Propagation over a Deep Architecture
• Spherical Random Features for Polynomial Kernels
• VISALOGY: Answering Visual Analogy Questions
• Generative Image Modeling Using Spatial LSTMs
• Learning with a Wasserstein Loss
• Natural Neural Networks
• Combinatorial Bandits Revisited
• Mind the Gap: A Generative Approach to Interpretable Feature Selection and Extraction
• Deep Convolutional Inverse Graphics Network
• Grammar as a Foreign Language
• Semi-supervised Sequence Learning

 

 

• XNOR-Net: ImageNet Classification Using Binary Convolutional Neural Networks: http://arxiv.org/abs//1603.05279
• Neural Variational Inference for Text Processing: http://gitxiv.com/posts/oRw692PEooNcwh9Qh/neural-variational-inference-for-text-processing
• Theano Tutorial: http://www.marekrei.com/blog/theano-tutorial/
• Face2Face: Real-time Face Capture and Reenactment of RGB Videos (CVPR 2016 Oral): https://www.youtube.com/watch?v=ohmajJTcpNk
• South Korea Trumpets $860M AI Fund after AlphaGo ‘Shock’: http://www.nature.com/news/south-korea-trumpets-860-million-ai-fund-after-alphago-shock-1.19595?WT.mc_id=FBK_NatureNews
• Open Datasets: http://www.datasets.co/
• Deep Spreadsheets with ExcelNet: http://www.deepexcel.net/
• D-Q Learning Flappy Bird: https://github.com/yenchenlin1994/DeepLearningFlappyBird
• AIX Project from MS, Using Minecraft to Build More Intelligent Technology: https://blogs.microsoft.com/next/2016/03/13/project-aix-using-minecraft-build-intelligent-technology/
• Neural Networks Demystified: http://lumiverse.io/series/neural-networks-demystified
• Google Blog Post on What They Learned in Seoul with AlphaGo: https://googleblog.blogspot.kr/2016/03/what-we-learned-in-seoul-with-alphago.html
• Detecting Heart Arrhythmias Using Machine Learning and Apple Watch Data: http://insighthealthdata.com/blog/HealthyBeats/
• Topological Visualisation of a Convolutional Neural Network: http://terencebroad.com/convnetvis/vis.html
• Identity Mapping in Deep Residual Networks: http://arxiv.org/abs/1603.05027
• AlphaGo and the Future of Computer Games: https://www.youtube.com/watch?v=UMm0XaCFTJQ
• The Next Step after Andrew Ng’s Course: https://www.reddit.com/r/MachineLearning/comments/4anq6h/age_old_question_the_next_step_after_andrew_ngs/
• Exploratory Gradient Boosting for Reinforcement Learning in Complex Domains: http://arxiv.org/abs/1603.04119
• The BlackBox Challenge – Win the Game with Unknown Rules: http://blackboxchallenge.com/
• Do You Think TensorFlow Will Eventually Replace Theano and Torch: https://www.reddit.com/r/MachineLearning/comments/4amr7p/askreddit_do_you_think_tensorflow_will_eventually/
• Whiskey Embeddings: http://wrec.herokuapp.com/methodology
• Diagnosing Heart Diseases with Deep Neural Networks: http://irakorshunova.github.io/2016/03/15/heart.html
• Why Enrollment is Surging in Machine learning Classes: https://blogs.nvidia.com/blog/2016/02/24/enrollment-in-machine-learning/
• GNU Launches NN Framework, Gneural Network: https://www.gnu.org/software/gneuralnetwork/
• Installing TensorFlow on Raspberry Pi 3: https://github.com/samjabrahams/tensorflow-on-raspberry-pi
• Advice on Deep Learning with Time Series: https://www.reddit.com/r/MachineLearning/comments/4ad4zl/advice_on_deep_learning_with_time_series/
• Yann LeCun’s Statement on General AI: https://www.facebook.com/yann.lecun/posts/10153426023477143

 

 

• Deep-Q Learning Pong with TensorFlow and PyGame: http://www.danielslater.net/2016/03/deep-q-learning-pong-with-TensorFlow.html
• Must Know Tips/Tricks in Deep Neural Networks: http://lamda.nju.edu.cn/weixs/project/CNNTricks/CNNTricks.html
• Stanford CS231n: https://www.youtube.com/playlist?list=PLwQyV9I_3POsyBPRNUU_ryNfXzgfkiw2p
• Neural Doodle: https://github.com/alexjc/neural-doodle
• SigOpt for ML: Unsupervised Learning with Even Less Supervision Using Bayesian Optimization: http://blog.sigopt.com/post/140871698423/sigopt-for-ml-unsupervised-learning-with-even
• Montreal Deep Learning Summer School: https://sites.google.com/site/deeplearningsummerschool2016/
• The Sadness and Beauty of Watching Google’s AI Play Go: https://sites.google.com/site/deeplearningsummerschool2016/
• Low-Rank Passthrough Neural Networks: https://www.reddit.com/r/MachineLearning/comments/49yjry/160303116_lowrank_passthrough_neural_networks/
• Getting Started with MXNet: https://indico.io/blog/getting-started-with-mxnet/
• Deploy TensorFlow Graphs for Faster Evaluation and Export to TensorFlow-less Environments Running NumPy: https://github.com/riga/tfdeploy
• Value Iteration Networks: http://arxiv.org/pdf/1602.02867v1.pdf
• Collection of Machine Learning Interview Questions: http://analyticscosm.com/machine-learning-interview-questions-for-data-scientist-interview/
• Interview Experience with DeepMind: https://www.reddit.com/r/MachineLearning/comments/49rbnn/has_anyone_here_interviewed_with_deepmind_what/
• Train Your Own Image Classifier with Inception in TensorFlow: http://googleresearch.blogspot.kr/2016/03/train-your-own-image-classifier-with.html
• Comprehensive List of All of the Famous Problems Solved Successfully Using Deep Learning: https://www.reddit.com/r/MachineLearning/comments/49izzc/is_there_a_comprehensive_list_of_all_of_the/
• Deep Learning in a Nutshell: Sequence Learning: https://devblogs.nvidia.com/parallelforall/deep-learning-nutshell-sequence-learning/
• Learning Physical Intuition of Block Towers by Example: http://arxiv.org/abs/1603.01312
• Intuitive Explanation of Gaussian Processes: https://www.reddit.com/r/MachineLearning/comments/49elmx/can_someone_explain_gaussian_processes_intuitively/
• The DeepMind Bubble?: https://www.reddit.com/r/MachineLearning/comments/49bpid/the_deepmind_bubble/
• Normalization Propagation: A Parametric Technique for Removing Internal Covariate Shift in Deep Networks: http://arxiv.org/abs/1603.01431
• Dynamic Memory Networks for Visual and Textual Question Answering: http://arxiv.org/abs/1603.01417

 

 

• Faster R-CNN: Towards Real-Time Object Detection with Region Proposal Network
• Learning visual biases from human imagination
• Tensorizing Neural Networks
• Active Learning from Weak and Strong Labelers
• Bidirectional Recurrent Neural Networks as Generative Models
• Unsupervised Learning by Program Synthesis
• Deep Visual Analogy-Making
• Deep Generative Image Models using a Laplacian Pyramid of Adversarial Networks
• Teaching Machines to Read and Comprehend
• Principal Differences Analysis: Interpretable Characterization of Differences between Distributions
• End-to-end Learning of LDA by Mirror-Descent Back Propagation over a Deep Architecture
• Spherical Random Features for Polynomial Kernels
• VISALOGY: Answering Visual Analogy Questions
• Generative Image Modeling Using Spatial LSTMs
• Learning with a Wasserstein Loss
• Natural Neural Networks
• Combinatorial Bandits Revisited
• Mind the Gap: A Generative Approach to Interpretable Feature Selection and Extraction
• Deep Convolutional Inverse Graphics Network
• Grammar as a Foreign Language
• Semi-supervised Sequence Learning

 

 

• Synopsis of Top Go Professional’s Analysis of Google’s Deepmind’s Go AI: https://www.reddit.com/r/MachineLearning/comments/43fl90/synopsis_of_top_go_professionals_analysis_of/
• Public Datasets: https://github.com/caesar0301/awesome-public-datasets
• Hypothesis Testing with Bootstrap and Apache Spark: http://www.kdnuggets.com/2016/01/hypothesis-testing-bootstrap-apache-spark.html
• Game AI Competition: http://theaigames.com/
• List of Deep Learning Courses: https://docs.google.com/spreadsheets/d/1NSbURoynPVnOvSCtmaIX6zV8wl6n3ybacnNGMyb-v-0/edit#gid=0
• Recent Advances in Deep Learning – Oriol Vinyals: https://www.youtube.com/watch?v=UAq961jQjYg
• Play list of Neural Network Tutorials: https://www.youtube.com/playlist?list=PL29C61214F2146796
• Nature’s Video of AlphaGo: https://www.youtube.com/watch?v=g-dKXOlsf98
• Neural Enquirer: Learning to Query Tables with Natural Language: http://arxiv.org/abs/1512.00965
• Bitwise Neural Networks: http://arxiv.org/abs/1601.06071
• 3D Visualization of CNN: http://scs.ryerson.ca/~aharley/vis/conv/
• MS’s CNTK: http://blogs.microsoft.com/next/2016/01/25/microsoft-releases-cntk-its-open-source-deep-learning-toolkit-on-github/
• CNTK Github: https://github.com/Microsoft/CNTK
• Machine Learning: The High-Interest Credit Card of Technical Debt: http://static.googleusercontent.com/media/research.google.com/en//pubs/archive/43146.pdf