Topic 713: Miscellaneous Deep Learning 427


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Stanford Tensorflow Tutorials Huyen Nguyen 2017
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Deep Sequence Modeling Ava Soleimany 2019
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Boltzmann Machines Roger Grosse 2018
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Attacking Networks with Adversarial Examples Kian Katanforoosh 2018
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Guest Lecture: Behnam Neyshabur (IAS/NYU): Generalization in Deep Learning Behnam Neyshabur 2019
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Case Studies Kian Katanforoosh 2018
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Deep Learning for Computer Vision Ava Soleimany 2019
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Introduction: The Curse of Dimensionality in ML Joan Bruna 2019
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Deep Generative Models Alexander Amini 2019
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Geometric Stability in Euclidean Domains: The Scattering Transform and beyond Joan Bruna 2019
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Deep Reinforcement Learning Alexander Amini 2019
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The Scattering Transform and beyond Joan Bruna 2019
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Deep Learning: Limitations and new frontiers Ava Soleimany 2019
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Non-Euclidean Geometric Stability Joan Bruna 2019
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A Biologically Plausible Learning Algorithm for Neural Networks Dimitry Krotov 2019
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Autoregressive models Pieter Abbeel 2019
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Image Domain Transfer Jan Kautz 2019
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Lossless compression Pieter Abbeel 2019
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Final Project Presentations Various 2019
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Sparse coding - dictionary learning algorithm Hugo Larochelle 2017
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Likelihood models II Pieter Abbeel 2019
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Natural language processing - multitask learning Hugo Larochelle 2017
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Likelihood models III Pieter Abbeel 2019
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Sparse coding - ZCA preprocessing Hugo Larochelle 2017
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Case Studies Kian Katanforoosh 2018
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Unpaired Image-to-Image Translation using Cycle-Consistent Adversarial Networks Jun-Yan Zhu, Taesung Park, Phillip Isola, Alexei A. Efros 2017
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Deep Learning Models Rasbt 2019
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Siamese and triplet learning with online pair/triplet mining adambielski 2018
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Taxi-v1 T Erez, Y Tassa, E Todorov 2017
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Tensor network machine learning Miles Emstoudenmire 2016
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A Large-Scale Video Benchmark for Human Activity Understanding Fabian Caba Heilbron, Victor Escorcia, Bernard Ghanem, Juan Carlos Niebles 2015
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Open AI Gym Open AI 2017
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NLP-Models huseinzol05 2018
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Neural Doodle Alex J. Champandard, Jared Feng 2017
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bulbea Achilles Rasquinha 2017
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Deep learning made easy Zygmunt Zaj?c 2013
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SQLNet Xiaojun Xu, Chang Liu, Dawn Song 2017
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code for the paper "Improved Techniques for Training GANs" Tim Salimans, Ian Goodfellow, Wojciech Zaremba 2017
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TRAIN FASTER-RCNN ON A NEW DATASET Ross Girshick, Xin Lei Pan 2015
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Pix2code Author Unknown 2017
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CNTK Venelin Valkov 2015
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Wasserstein GAN Martin Arjovsky, Soumith Chintala, Léon Bottou 2017
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ZhuSuan Author Unknown 2017
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Code, exercises and solutions for popular Reinforcement Learning algorithms Denny Britz 2016
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pyltr Jerry Ma, Luca Soldaini, Simon Stiebellehner, Songwei Ge, Cheng Rui 2017
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Relationship Modeling Networks (RMN) Mohit Iyyer 2016
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Pyevolve Christian S. Perone 2009
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Base pretrained models and datasets in pytorch (MNIST, SVHN, CIFAR10, CIFAR100, STL10, AlexNet, VGG16, VGG19, ResNet, Inception, SqueezeNet) Aaron Chen 2017
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neural-style Justin Johnson, Shubhanshu Mishra 2014
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Simple and Efficient Learning with Dynamic Neural Networks Graham Neubig 2017
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Gensim integration with scikit-learn and Keras Chinmaya Pancholi 2017
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RankPy Tomas Tunys 2016
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Neuralconvo: Chatting with a Deep learning brain Julien Chaumond, Clément Delangue 2018
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GluonNLP: Your Choice of Deep Learning for NLP dmlc 2019
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neuro-lab Nuel Ezenwere 2017
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Breze Justin Bayer, Sebastian Urban, Adrià Puigdomènech 2012
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RocAlphaGo wrongu, Tyler Trine, James Tauber, Louis Marti, Thouis (Ray) Jones 2016
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deepdream Alexander Mordvintsev, Michael Tyka, Christopher Olah 2015
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Boltzmann Machines in TensorFlow with examples Yelysei Bondarenko 2017
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Google Summer of Code 2017 – Week 1 of Integrating Gensim with scikit-learn and Keras Chinmaya Pancholi 2017
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pix2pix Phillip Isola, Jun-Yan Zhu, Tinghui Zhou, Alexei A. Efros 2017
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DeepMind Lab Thomas Köppe, Jithin Odattu 2016
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word2vec Danielfrg 2018
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Implementation of research papers on Deep Learning, NLP, CV in Python using Keras, Tensorflow and Scikit Learn Gaurav Bhatt 2018
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Visual Interaction Networks Jaesik Yoon 2017
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Review Network for Caption Generation Zhilin Yang, Ye Yuan, Yuexin Wu, Ruslan Salakhutdinov, William W. Cohen 2016
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deepmark Soumith Chintala 2016
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neural image analogies Adam Wentz 2016
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Five Hundred Deep Learning Papers Daniele Ciriello 2015
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Structured Inference Networks for Nonlinear State Space Models Rahul G. Krishnan, Uri Shalit, David Sontag 2017
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Restricted Boltzmann Machines in Python Edwin Chen 2011
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Implementations of label propagation like algorithms Yuto Yamaguchi 2016
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DarkForest, the Facebook Go engine Facebook Research 2017
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The Neural Turing Machine Author Unknown 2017
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Unsupervised Representation Learning with Deep Convolutional Generative Adversarial Networks Alec Radford, Luke Metz, Soumith Chintala 2015
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Rubiks Cube Convnet Author Unknown 2017
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VisualDL PaddlePaddle 2018
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Generative Adversarial Networks in Tensorflow - Linux Shell RubensZimbres 2018
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Introducing Cloud Natural Language API, Speech API open beta and our West Coast region expansion Apoorv Saxena 2016
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Persontyle Workshop for Applied Deep Learning Amaia Salvador and Santiago Pascual 2017
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Simple, Strong Deep-Learning Baselines for NLP in several frameworks Dan Pressel 2016
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Sort-of-CLEVR Kim Heecheol 2017
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The Predictron Zhongwen Xu 2016
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DeepWalk Bryan Perozzi, Rami Al-Rfou, Haochen Chen 2014
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#MSR RastRDFStore Package Author Unknown 2017
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Seq2Seq Chatbot Author Unknown 2017
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seq2seq Anna Goldie, Denny Britz 2017
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Easy to Learn and Use Distributed Deep Learning Platform Yu Yang, Gang Liao, Tao Luo, Jacques Qiao 2016
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Implementation of Using Fast Weights to Attend to the Recent Past Goku Mohandas 2017
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Implementation of Skip-Thought Vectors Chris Shallue 2017
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Simple DQN Author Unknown 2017
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Thinc: Practical Machine Learning for NLP in Python Matthew Honnibal 2016
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NSynth: Neural Audio Synthesis Jesse Engel, Cinjon Resnick, Adam Roberts, Sander Dieleman, Karen Simonyan, Mohammad Norouzi, Doug Eck 2017
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Generative Adversarial Networks (GANs): Engine and Applications Anton Karazeev 2017
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10 Days of Deep Learning Vaibhav Srivastav 2017
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Deep Learning: How OpenCV's blobFromImage Works Adrian Rosebrock 2017
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Artistic Stylization and Rendering Aaron Hertzmann 2017
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Making computers explain themselves Larry Hardesty 2016
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GAN Playground - Explore Generative Adversarial Nets In Your Browser Reiichiro Nakano 2017
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Understanding Generative Adversarial Networks Egor Dezhic 2018
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PyTorch implementation of NIPS 2017 paper Dynamic Routing Between Capsules Adam Bielski 2017
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Lessons Learned from Applying Deep Learning for NLP Without Big Data Yonatan Hadar 2018
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Towards data set augmentation with GANs Pedro Ferreira 2017
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Getting Ready for AI based gaming agents - Overview of Open Source Reinforcement Learning Patterns Faizan Shaikh 2016
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Deep Neural Network implemented in pure SQL over BigQuery Harisanka Haridas 2018
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InfoGAN - Generative Adversarial Networks Part III Zak Jost 2017
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Stacked Autoencoders Mehdi Mirza 2017
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Deep Learning Papers Reading Roadmap Flood Sung 2016
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Experience Google’s machine learning on your own images, voice and text Kaz Sato 2017
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Understanding Evolutionary Algorithms Egor Dezhic 2017
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Modern Deep Learning Techniques Applied to Natural Language Processing Elvis Saravia, Soujanya Poria 2019
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Heart Disease Diagnosis with Deep Learning Check-Hou Yee 2017
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Four deep learning trends from ACL 2017: Part 1 Abigail See 2017
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Getting Started with spaCy for Natural Language Processing Matthew Mayo 2018
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Deep Learning Resources Jeremy D. Jackson 2015
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[ICLR][NVIDIA] Progressive generative adversarial networks (GANs) explained with art forgery?—?Part I. Brendan Whitaker 2018
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TFGAN: A Lightweight Library for Generative Adversarial Networks Joel Shor 2017
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Modern Deep Learning Techniques Applied to Natural Language Processing Elvis Saravia, Soujanya Poria 2019
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Four deep learning trends from ACL 2017: Part 2 Abigail See 2017
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Deep Learning Summer School, Montreal 2016 videos Video Lectures 2016
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Automatic feature engineering using Generative Adversarial Networks Hamaad Shah 2018
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Interpretable Machine Learning through Teaching Smitha Milli, Pieter Abbeel, Igor Mordatch 2018
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Relational inductive biases, deep learning, and graph networks Adrian Colyer 2018
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Vectors in Machine Learning Joydeep Bhattacharjee 2017
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Tel Aviv Deep Learning Bootcamp Shlomo Kashani 2017
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Semi-Supervised Learning and GANs Raghav Mehta 2018
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Machine Learning for Video Games SethBling 2015
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Open-sourcing DeepMind Lab Charlie Beattie, Joel Leibo, Stig Petersen, Shane Legg 2016
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Deep Learning Summer School Invited Speakers with slides CIFAR 2016
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Some New Interesting Deep Learning Datasets for Data Scientists Muktabh Mayank 2017
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Demystifying Generative Adversarial Nets (GANs) Stefan Hosein 2018
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Experiments with SWISH activation function on MNIST dataset Aiyam Sharma 2017
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My Experience with CUDAMat, Deep Belief Networks, and Python Adrian Rosebrock 2014
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Summaries and notes on Deep Learning research papers Denny Britz 2015
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Dynamic Pooling And Unfolding Recursive Autoencoders For Paraphrase Detection Richard Socher, Eric H. Huang, Jeffrey Pennington, Andrew Y. Ng, Christopher D. Manning 2011
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Generative Adversarial Networks?—?Explained Rohith Gandhi 2018
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My thoughts on Skip-Thoughts Sanyam Agarwal 2017
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Horovod: An Open Source Distributed Deep Learning Framework for TensorFlow by Uber Kirti Barshi 2017
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Your deep learning + Python Ubuntu virtual machine Adrian Rosebrock 2017
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Demystifying Generative Adversarial Networks Stefan Hosein 2018
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Exploration, Exploitation and Imperfect Representation in Deep Learning Carlos Perez 2017
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What do 50 million drawings look like? Google Research 2017
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A course in reinforcement learning in the wild justheuristic, Oleg Vasilev 2017
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Yann LeCun - Deep Learning and the Future of AI Yann LeCun 2017
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Another Twitter sentiment analysis with Python?—?Part 10 (Neural Network with Doc2Vec/Word2Vec/GloVe) The Rickest Ricky 2018
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Text to Video Generation Antonia Antonova 2017
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Awesome - Most Cited Deep Learning Papers Terry Taewoong Um 2017
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Is Deep Learning without Programming Possible ? Naveen Mawani 2019
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Deep Learning Demos Caglar Gulcehre 2014
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UCL Course on RL David Silver 2015
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Machine Learning for Humans, Part 5: Reinforcement Learning Vishal Maini 2017
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Deep Learning Papers Reading Roadmap floodsung 2018
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Generative Adversarial Networks Paige Zaremba 2017
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Tensorflow Tutorial: image classifier using convolutional neural network Manan Oza 2017
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Decoupled Neural Interfaces Using Synthetic Gradients Max Jaderberg 2017
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Quick Recipe: Building Word Clouds bogdani 2017
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Interpreting Deep Neural Networks with SVCCA Maithra Raghu 2017
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Anatomize Deep Learning with Information Theory Lilian Weng 2017
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Deep Learning Weekly Jan Bussieck, Malte Baumann 2017
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Top 5 arXiv Deep Learning Papers, Explained Matthew Mayo 2015
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New download API for pretrained NLP models and datasets in Gensim Chaitali Saini 2017
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Applied Deep Learning - Part 3: Autoencoders Arden Dertat 2017
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Neural Information Retrieval: At the End of the Early Years Kezban Dilek Onal, Ye Zhang, Ismail Sengor Altingovde, Md Mustafizur Rahman, Pinar Karagoz, Alex Braylan, Brandon Dang, Heng-Lu Chang, Henna Kim, Quinten McNamara, Aaron Angert, Edward Banner, Vivek K 2017
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When (not) to use Deep Learning for NLP Delip Rao 2017
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Gradient Descent – A Simple Way to Understand Muhammad Rizwan 2018
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Robust Adversarial Examples Anish Athalye 2017
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Adversarial Examples, Explained Thomas Tanay 2018
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Medical Image Analysis with Deep Learning?—?IV Taposh Dutta-Roy 2017
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Neural Networks, Manifolds, and Topology Christopher Olah 2014
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Event2Mind Allen Institute for Artificial Intelligence 2019
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Large-Scale Deep Learning Marc Aurelio Ranzato 2012
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A Beginner’s Tutorial for Restricted Boltzmann Machines Skymind 2017
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Recursive Deep Learning for Modeling Semantic Compositonality Christopher Manning 2013
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Survey of DNN Development Resources Joel Emer, Vivienne Sze, Yu-Hsin Chen, Tien-Ju Yang 2017
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Reinforcement Learning (RL) – Policy Gradients II Goku Mohandas 2016
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Equilid: Socially-Equitable Language Identification David Jurgens 2017
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Multimodal Deep Learning Xavier Giro-i-Nieto 2017
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Introducing TF.Text TensorFlow 2019
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Stanford Deep Learning Tutorial Chris McCormick 2014
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Deep Learning Research Ian Goodfellow and Yoshua Bengio and Aaron Courville 2016
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Learning when to skim and when to read Alexander Rosenberg Johansen, Bryan McCann, James Bradbury, Richard Socher 2017
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index Ian Goodfellow and Yoshua Bengio and Aaron Courville 2016
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Deep Learning without Backpropagation Andrew Trask 2017
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ML teams "Pieter Abbeel, Sergey Karayev, Josh Tobin" 2019
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PixelGAN Autoencoders Alireza Makhzani, Brendan Frey 2017
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Learning Hierarchies of Invariant Features Yann LeCun 2012
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What’s a Generative Adversarial Network? Leading Researcher Explains Jamie Beckett 2017
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WHY DEEP LEARNING WORKS 3: BACKPROP MINIMIZES THE FREE ENERGY ? Charles H. Martin 2017
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Neural Networks, Types, and Functional Programming Christopher Olah 2015
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Dynamic Programming in NLP - Skip Grams Abhijit Mondal 2017
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Deep Learning Based Chatbot Models Richard Csaky 2017
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From natural scene statistics to models of neural coding and representation (part 1) Bruno A. Olshausen 2012
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Deep Autoencoders Skymind 2017
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On the intuition behind deep learning & GANs?—?towards a fundamental understanding Michael Dietz 2017
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Survey of DNN Hardware Joel Emer, Vivienne Sze, Yu-Hsin Chen, Tien-Ju Yang 2017
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Image Recognition Goku Mohandas 2016
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Part 0: Energy-based machine learning Sakyasingha Dasgupta, Takayuki Osogami 2017
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The Unreasonable Ineffectiveness of Deep Learning in NLU Suman Deb Roy 2017
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Generalization and Equilibrium in Generative Adversarial Nets (GANs) Sanjeev Arora 2017
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Deep Learning: a Next Step? Kyunghyun Cho 2017
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Image Compression with Neural Networks Nick Johnston, David Minnen 2016
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Linear Factor Models Ian Goodfellow and Yoshua Bengio and Aaron Courville 2016
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Understanding HintonÕs Capsule Networks. Part II: How Capsules Work Max Pechyonkin 2017
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acknowledgements Ian Goodfellow and Yoshua Bengio and Aaron Courville 2016
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On the Expressive Efficiency of Overlapping Architectures of Deep Learning Or Sharir, Amnon Shashua 2017
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App Discovery with Google Play, Part 1: Understanding Topics Malay Haldar, Matt MacMahon, Neha Jha, Raj Arasu 2016
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Tutorial on: Deep Learning Part 3 Geoffrey Hinton 2012
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Embed, encode, attend, predict: The new deep learning formula for state-of-the-art NLP models Matthew Honnibal 2016
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NORMALIZATION IN DEEP LEARNING Charles H. Martin 2017
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Visualizing Representations: Deep Learning and Human Beings Christopher Olah 2015
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Dynamic Programming in NLP - Longest Common Subsequence Abhijit Mondal 2017
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Unsupervised Sentiment Neuron Alec Radford, Rafal Jozefowicz, Ilya Sutskever 2017
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Deep learning in the visual cortex: II. Computational mechanisms of rapid recognition and feedforward processing Thomas Serre 2012
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Denoising Autoencoders (dA) LISA Lab 2008
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Tensor Methods for large-scale Machine Learning Anima Anandkumar 2018
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DNN Accelerator Architectures Joel Emer, Vivienne Sze, Yu-Hsin Chen, Tien-Ju Yang 2017
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A Primer in Adversarial Machine Learning – The Next Advance in AI William Vorhies 2016
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Part I: Boltzmann machines & energy-based models Takayuki Osogami 2017
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DeepMind’s Relational Reasoning Networks?—?Demystified Harshvardhan Gupta 2017
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Implicit generative models: dual vs. primal approaches Ilya Tolstikhin 2017
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Deep Learning Research Review Week 2: Reinforcement Learning Adit Deshpande 2016
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Global Optimality in Matrix and Tensor Factorization, Deep Learning & Beyond Ben Haeffele and René Vidal 2016
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Table of Contents Ian Goodfellow and Yoshua Bengio and Aaron Courville 2016
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Having Fun with Deep Convolutional GANs Naoki Shibuya 2017
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Phone recognition on the TIMIT benchmark Geoffrey Hinton 2012
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notation Ian Goodfellow and Yoshua Bengio and Aaron Courville 2016
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A news-analysis NeuralNet learns from a language NeuralNet m.zaradzki 2017
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Marrying Graphical Models & Deep Learning Max Welling 2017
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Teaching Machines to Draw David Ha 2017
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Automatic Image Captioning using Deep Learning (CNN and LSTM) in PyTorch Faizan Shaikh 2018
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Supervised learning without any example Thomas Mensink 2017
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Deep adversarial learning is finally ready ???? and will radically change the game Michael Dietz 2017
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WHY DOES DEEP LEARNING WORK? Charles H. Martin 2015
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Inceptionism: Going Deeper into Neural Networks Christopher Olah, Alexander Mordvintsev, Mike Tyka 2015
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Learning From Unlabelled Data - EM Approach Abhijit Mondal 2017
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Evolution Strategies as a Scalable Alternative to Reinforcement Learning Andrej Karpathy, Tim salimans, Jonathan Ho, Peter Chen, Ilya Sutskever, John Schulman, Greg Brockman & Szymon Sidor 2017
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Deep learning in the visual cortex: III. Beyond feedforward processing: Attentional mechanisms and cortical feedback THomas Serre 2012
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BEGAN: STATE OF THE ART GENERATION OF FACES WITH GENERATIVE ADVERSARIAL NETWORKS Loris Felardos 2017
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Play with Generative Adversarial Networks (GANs) in your browser! Minsuk Kahng, Nikhil Thorat, Polo Chau, Fernanda Viégas, Martin Wattenberg 2018
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Neural coref Author Unknown 2018
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Deep Learning for NLP Best Practices Sebastian Ruder 2017
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Machine Learning is Fun! The world’s easiest introduction to Machine Learning - Part 4 Adam Geitgey 2016
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Advanced Technology Opportunities Joel Emer, Vivienne Sze, Yu-Hsin Chen, Tien-Ju Yang 2017
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Artificial Intelligence is a matter of Language Giuseppe Bonaccorso 2017
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Part III: Boltzmann machines for time-series Takayuki Osogami 2017
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Intrinsic deep learning on manifolds (Spectral CNNs, Geodesic CNNs, Anisotropic CNNs, Mixture Model Networks, Embedding-based techniques) Michael Bronstein 2017
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7—EXOTIC CNN ARCHITECTURES; RNN FROM SCRATCH Jeremy Howard 2016
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Deep learning in the visual cortex Thomas Serre 2012
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Generative Adversarial Networks (GANs) in 50 lines of code (PyTorch) Dev Nag 2017
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Mathematics of Deep Learning Raja Giryes, René Vidal 2017
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Autoencoders Ian Goodfellow and Yoshua Bengio and Aaron Courville 2016
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Power and Agency in modern films: Character portrayal analyses using computational tools Maarten Sap, Marcella Cindy, Prasetio Ari Holtzman 2017
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How can we take deep RL into the real world? Sergey Levine 2018
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Deep Learning, Graphical Based Models, Energy Based Models, Structured Prediction Part 2 2012
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Lenny #1: Robots + Reinforcement Learning Lenny Khazan 2016
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Failures of Gradient-Based Deep Learning Shai Shalev-Shwartz, Shaked Shammah, Ohad Shamir 2017
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The GAN Zoo Avinash Hindupur 2017
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Machine Learning is Fun Part 7: Abusing Generative Adversarial Networks to Make 8-bit Pixel Art Adam Geitgey 2017
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Deep Learning for Object Detection and Localization Aaditya Prakash 2017
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WHY DEEP LEARNING WORKS II: THE RENORMALIZATION GROUP Charles H. Martin 2015
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Groups & Group Convolutions Christopher Olah 2014
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Rule-based Matcher Explorer Matthew Honnibal 2019
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Multivew Feature Learning Roland Memisevic 2012
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Five Hundred Deep Learning Papers, Graphviz and Python Daniele Ciriello 2015
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Deep Learning Models for Health Care: Challenges and Solutions Edward Yoonjae Choi, Yan Liu, Sanjay Purushotham, Jimeng Sun 2018
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GANs for Simulation, Representation and Inference Ari Heljakka 2017
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DNN Model and Hardware Co-Design Joel Emer, Vivienne Sze, Yu-Hsin Chen, Tien-Ju Yang 2017
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Machine Learning Foundations and Methods for Precision (Medicine and Healthcare) Suchi Saria, Peter Schulam 2016
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Deep learning on regular 3D data formats (Multiview CNN and 3D CNN) Evangelos Kalogerakis, Jimei Yang 2017
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Information Gain Andrew W. Moore 2001
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Advanced Hierarchical Models Russ Salakhutdinov 2012
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Representation Learning Ian Goodfellow and Yoshua Bengio and Aaron Courville 2016
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Recent Advances and Frontiers in Deep RL Volodymyr Minh 2018
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Feature Learning For Comparing Examples Graham Taylor 2012
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Troubleshooting Deep Learning Networks "Pieter Abbeel, Sergey Karayev, Josh Tobin" 2019
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Rohan #4: The vanishing gradient problem Rohan Kapur 2016
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Deep Learning for Robotics Sergey Levine 2017
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Automating Machine Learning: Advanced WhizzML Workflows Author Unknown 2017
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Marrying Graphical Models & Deep Learning Max Welling 2017
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Learning to Reason with Neural Module Networks Jacob Andreas 2017
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Visual Information Theory Christopher Olah 2015
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Machine Learning to Big Data\\\Scaling Inverted Indexing with Solr Sharmistha Chatterjee 2019
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A wizard's guide to Adversarial Autoencoders: Part 1, Autoencoder? Naresh Nagabushan 2017
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Introduction to MCMC for deep learning Iain Murray 2012
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Five Hundred Deep Learning Papers, Part II (Adding JavaScript) Daniele Ciriello 2015
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Deep Learning Models for Health Care: Challenges and Solutions Yan Liu, Jimeng Sun 2017
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Multimodal Learning Victoria Dean 2017
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A Step-by-Step Guide to Synthesizing Adversarial Examples Anish Athalye 2017
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Structured Probablistic methods for Deep Learning Ian Goodfellow and Yoshua Bengio and Aaron Courville 2016
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Benchmarking Metrics for DNN Hardware Joel Emer, Vivienne Sze, Yu-Hsin Chen, Tien-Ju Yang 2017
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Pruning deep neural networks to make them fast and small Jacob Gildenblat 2017
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A Tutorial on Energy-Based Learning Yann LeCun 2006
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3D Deep Learning Tutorial@CVPR2017 Hao Su 2017
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Looking for the Missing Signal LÉON BOTTOU 2017
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Machine Learning and AI via Brain simulations Andrew Ng 2012
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BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding Jacob Devlin 2018
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Learning Representations of Sequences Graham Taylor 2012
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