WTF Deep Learning!!!
Table Of Content
Github
git clone https://github.com/wtf-deeplearning/wtf-deeplearning.github.io.git
Paper
Survey Review
Theory Future
Optimization Regularization
NetworkModels
- Deep residual learning for image recognition (2016), K. He et al. (Microsoft) [pdf]
source : http://arxiv.org/pdf/1512.03385 - Going deeper with convolutions (2015), C. Szegedy et al. (Google) [pdf]
source : http://www.cv-foundation.org/openaccess/content_cvpr_2015/papers/Szegedy_Going_Deeper_With_2015_CVPR_paper.pdf - Fast R-CNN (2015), R. Girshick [pdf]
source : http://www.cv-foundation.org/openaccess/content_iccv_2015/papers/Girshick_Fast_R-CNN_ICCV_2015_paper.pdf - Very deep convolutional networks for large-scale image recognition (2014), K. Simonyan and A. Zisserman [pdf]
source : http://arxiv.org/pdf/1409.1556 - Fully convolutional networks for semantic segmentation (2015), J. Long et al. [pdf]
source : http://www.cv-foundation.org/openaccess/content_cvpr_2015/papers/Long_Fully_Convolutional_Networks_2015_CVPR_paper.pdf - OverFeat: Integrated recognition, localization and detection using convolutional networks (2014), P. Sermanet et al. (LeCun) [pdf]
source : http://arxiv.org/pdf/1312.6229 - Visualizing and understanding convolutional networks (2014), M. Zeiler and R. Fergus [pdf]
source : http://arxiv.org/pdf/1311.2901 - Maxout networks (2013), I. Goodfellow et al. (Bengio) [pdf]
source : http://arxiv.org/pdf/1302.4389v4 - ImageNet classification with deep convolutional neural networks (2012), A. Krizhevsky et al. (Hinton) [pdf]
source : http://papers.nips.cc/paper/4824-imagenet-classification-with-deep-convolutional-neural-networks.pdf - Large scale distributed deep networks (2012), J. Dean et al. [pdf]
source : http://papers.nips.cc/paper/4687-large-scale-distributed-deep-networks.pdf - Deep sparse rectifier neural networks (2011), X. Glorot et al. (Bengio) [pdf]
source : http://machinelearning.wustl.edu/mlpapers/paper_files/AISTATS2011_GlorotBB11.pdf
Image
- Imagenet large scale visual recognition challenge (2015), O. Russakovsky et al. [pdf]
source : http://arxiv.org/pdf/1409.0575 - Faster R-CNN: Towards Real-Time Object Detection with Region Proposal Networks (2015), S. Ren et al. [pdf]
source : http://papers.nips.cc/paper/5638-faster-r-cnn-towards-real-time-object-detection-with-region-proposal-networks.pdf - DRAW: A recurrent neural network for image generation (2015), K. Gregor et al. [pdf]
source : http://arxiv.org/pdf/1502.04623 - Rich feature hierarchies for accurate object detection and semantic segmentation (2014), R. Girshick et al. [pdf]
source : http://www.cv-foundation.org/openaccess/content_cvpr_2014/papers/Girshick_Rich_Feature_Hierarchies_2014_CVPR_paper.pdf - Learning and transferring mid-Level image representations using convolutional neural networks (2014), M. Oquab et al. [pdf]
source : http://www.cv-foundation.org/openaccess/content_cvpr_2014/papers/Oquab_Learning_and_Transferring_2014_CVPR_paper.pdf - DeepFace: Closing the Gap to Human-Level Performance in Face Verification (2014), Y. Taigman et al. (Facebook) [pdf]
source : http://www.cv-foundation.org/openaccess/content_cvpr_2014/papers/Taigman_DeepFace_Closing_the_2014_CVPR_paper.pdf - Decaf: A deep convolutional activation feature for generic visual recognition (2013), J. Donahue et al. [pdf]
source : http://arxiv.org/pdf/1310.1531 - Learning Hierarchical Features for Scene Labeling (2013), C. Farabet et al. (LeCun) [pdf]
source : https://hal-enpc.archives-ouvertes.fr/docs/00/74/20/77/PDF/farabet-pami-13.pdf - Learning hierarchical invariant spatio-temporal features for action recognition with independent subspace analysis (2011), Q. Le et al. [pdf]
source : http://robotics.stanford.edu/~wzou/cvpr_LeZouYeungNg11.pdf - Learning mid-level features for recognition (2010), Y. Boureau (LeCun) [pdf]
source : http://ece.duke.edu/~lcarin/boureau-cvpr-10.pdf
Caption
Video Human Activity
Word Embedding
Machine Translation QnA
Speech Etc
RL Robotics
Unsupervised
Hardware Software
Bayesian
2013:
- Deep gaussian processes|Andreas C. Damianou,Neil D. Lawrence|2013
Source: http://www.jmlr.org/proceedings/papers/v31/damianou13a.pdf
2014:
- Avoiding pathologies in very deep networks|D Duvenaud, O Rippel, R Adams|2014
Source: http://www.jmlr.org/proceedings/papers/v33/duvenaud14.pdf - Nested variational compression in deep Gaussian processes|J Hensman, ND Lawrence|2014
Source: https://arxiv.org/abs/1412.1370
2015:
- On Modern Deep Learning and Variational Inference |Yarin Gal, Zoubin Ghahramani|2015
Source: http://www.approximateinference.org/accepted/GalGhahramani2015.pdf - Rapid Prototyping of Probabilistic Models: Emerging Challenges in Variational Inference |Yarin Gal, |2015
Source: http://www.approximateinference.org/accepted/Gal2015.pdf - Bayesian Convolutional Neural Networks with Bernoulli Approximate Variational Inference |Yarin Gal, Zoubin Ghahramani|2015
Source: http://arxiv.org/abs/1506.02158 - Dropout as a Bayesian Approximation: Representing Model Uncertainty in Deep Learning |Yarin Gal, Zoubin Ghahramani|2015
Source: http://arxiv.org/abs/1506.02142 - Dropout as a Bayesian Approximation: Insights and Applications |Yarin Gal, |2015
Source: https://sites.google.com/site/deeplearning2015/33.pdf?attredirects=0 - Bayesian Convolutional Neural Networks with Bernoulli Approximate Variational Inference |Yarin Gal, Zoubin Ghahramani|2015
Source: http://arxiv.org/abs/1506.02158 - Scalable Variational Gaussian Process Classification|J Hensman, AGG Matthews, Z Ghahramani|2015
Source: http://www.jmlr.org/proceedings/papers/v38/hensman15.pdf
2016:
- Relativistic Monte Carlo | Xiaoyu Lu| 2016
Source: https://arxiv.org/abs/1609.04388 - Risk versus Uncertainty in Deep Learning: Bayes, Bootstrap and the Dangers of Dropout | Ian Osband| 2016
Source: http://bayesiandeeplearning.org/papers/BDL_4.pdf - Semi-supervised deep kernel learning|Neal Jean, Michael Xie, Stefano Ermon|2016
Source: http://bayesiandeeplearning.org/papers/BDL_5.pdf - Categorical Reparameterization with Gumbel-Softmax| Eric Jang, Shixiang Gu,Ben Poole| 2016
Source: http://bayesiandeeplearning.org/papers/BDL_8.pdf
Video: https://www.youtube.com/watch?v=JFgXEbgcT7g - Learning to Optimise: Using Bayesian Deep Learning for Transfer Learning in Optimisation| Jonas Langhabel, Jannik Wolff| 2016
Source: http://bayesiandeeplearning.org/papers/BDL_9.pdf - One-Shot Learning in Discriminative Neural Networks| Jordan Burgess,James Robert Lloyd,Zoubin Ghahramani| 2016
Source: http://bayesiandeeplearning.org/papers/BDL_10.pdf - Distributed Bayesian Learning with Stochastic Natural-gradient Expectation Propagation| Leonard Hasenclever,
Stefan Webb| 2016
Source: http://bayesiandeeplearning.org/papers/BDL_11.pdf - Knots in random neural networks| Kevin K. Chen| 2016
Source: http://bayesiandeeplearning.org/papers/BDL_2.pdf - Discriminative Bayesian neural networks know what they do not know | Christian Leibig, Siegfried Wahl| 2016
Source: http://bayesiandeeplearning.org/papers/BDL_12.pdf - Variational Inference in Neural Networks using an Approximate Closed-Form Objective|Wolfgang Roth and Franz Pernkopf|2016
Source: http://bayesiandeeplearning.org/papers/BDL_13.pdf - Combining sequential deep learning and variational Bayes for semi-supervised inference| Jos van der Westhuizen, Dr. Joan Lasenby| 2016
Source: http://bayesiandeeplearning.org/papers/BDL_14.pdf - Importance Weighted Autoencoders with Random Neural Network Parameters| Daniel Hernández-Lobato,Thang D. Bui,Yinzhen Li| 2016
Stefan Webb| 2016
Source: http://bayesiandeeplearning.org/papers/BDL_15.pdf - Variational Graph Auto-Encoders| Thomas N. Kipf,Max Welling| 2016
Source: http://bayesiandeeplearning.org/papers/BDL_16.pdf - Dropout-based Automatic Relevance Determination| Dmitry Molchanov, Arseniy Ashuha, Dmitry Vetrov| 2016
Source: http://bayesiandeeplearning.org/papers/BDL_18.pdf - Scalable GP-LSTMs with Semi-Stochastic Gradients| Maruan Al-Shedivat, Andrew Gordon Wilson, Yunus Saatchi, Zhiting Hu and Eric P. Xing| 2016
Source: http://bayesiandeeplearning.org/papers/BDL_19.pdf - Approximate Inference for Deep Latent Gaussian Mixture Models|Eric Nalisnick, Lars Hertel and Padhraic Smyth|2016
Source: http://bayesiandeeplearning.org/papers/BDL_20.pdf - Learning to Draw Samples: With Application to Amortized MLE for Generative Adversarial Training | Dilin Wang, Yihao Feng and Qiang Liu| 2016
Source: http://bayesiandeeplearning.org/papers/BDL_21.pdf
Video: https://www.youtube.com/watch?v=fi-UUQe2Pss - Learning and Policy Search in Stochastic Dynamical Systems with Bayesian Neural Networks| Stefan Depeweg, José Miguel Hernández-Lobato, Finale Doshi-Velez and Steffen Udluft| 2016
Source: https://arxiv.org/abs/1605.07127 - Accelerating Deep Gaussian Processes Inference with Arc-Cosine Kernels | Kurt Cutajar, Edwin V. Bonilla, Pietro Michiardi and Maurizio Filippone| 2016
Source: http://bayesiandeeplearning.org/papers/BDL_24.pdf - Embedding Words as Distributions with a Bayesian Skip-gram Model | Arthur Bražinskas, Serhii Havrylov and Ivan Titov| 2016
Source: http://bayesiandeeplearning.org/papers/BDL_25.pdf - Variational Inference on Deep Exponential Family by using Variational Inferences on Conjugate Models|Mohammad Emtiyaz Khan and Wu Lin|2016
Source: http://bayesiandeeplearning.org/papers/BDL_26.pdf - Neural Variational Inference for Latent Dirichlet Allocation| Akash Srivastava and Charles Sutton| 2016
Source: http://bayesiandeeplearning.org/papers/BDL_27.pdf - Hierarchical Bayesian Neural Networks for Personalized Classification | Ajjen Joshi, Soumya Ghosh, Margrit Betke and Hanspeter Pfister| 2016
Source: http://bayesiandeeplearning.org/papers/BDL_28.pdf - Simple and Scalable Predictive Uncertainty Estimation using Deep Ensembles| Balaji Lakshminarayanan, Alexander Pritzel and Charles Blundell| 2016
Source: http://bayesiandeeplearning.org/papers/BDL_29.pdf - Asynchronous Stochastic Gradient MCMC with Elastic Coupling| Jost Tobias Springenberg, Aaron Klein, Stefan Falkner and Frank Hutter| 2016
Source: http://bayesiandeeplearning.org/papers/BDL_30.pdf - The Concrete Distribution: A Continuous Relaxation of Discrete Random Variables|Chris J. Maddison, Andriy Mnih and Yee Whye Teh| 2016
Source: http://bayesiandeeplearning.org/papers/BDL_31.pdf - Known Unknowns: Uncertainty Quality in Bayesian Neural Networks | Ramon Oliveira, Pedro Tabacof and Eduardo Valle| 2016
Source: http://bayesiandeeplearning.org/papers/BDL_32.pdf - Normalizing Flows on Riemannian Manifolds |Mevlana Gemici, Danilo Rezende and Shakir Mohamed|2016
Source: http://bayesiandeeplearning.org/papers/BDL_33.pdf - Posterior Distribution Analysis for Bayesian Inference in Neural Networks| Pavel Myshkov and Simon Julier| 2016
Source: http://bayesiandeeplearning.org/papers/BDL_34.pdf - Deep Bayesian Active Learning with Image Data| Yarin Gal, Riashat Islam and Zoubin Ghahramani| 2016
Stefan Webb| 2016
Source: http://bayesiandeeplearning.org/papers/BDL_35.pdf - Bottleneck Conditional Density Estimators|Rui Shu, Hung Bui and Mohammad Ghavamzadeh| 2016
Source: http://bayesiandeeplearning.org/papers/BDL_36.pdf - A Tighter Monte Carlo Objective with Renyi alpha-Divergence Measures| Stefan Webb and Yee Whye Teh| 2016
Source: http://bayesiandeeplearning.org/papers/BDL_37.pdf - Bayesian Neural Networks for Predicting Learning Curves| Aaron Klein, Stefan Falkner, Jost Tobias Springenberg and Frank Hutter| 2016
Source: http://bayesiandeeplearning.org/papers/BDL_38.pdf - Nested Compiled Inference for Hierarchical Reinforcement Learning|Tuan Anh Le, Atılım Güneş Baydin and Frank Wood|2016
Source: http://bayesiandeeplearning.org/papers/BDL_41.pdf - Open Problems for Online Bayesian Inference in Neural Networks | Robert Loftin and David Roberts| 2016
Source: http://bayesiandeeplearning.org/papers/BDL_42.pdf - Deep Probabilistic Programming| Dustin Tran, Matt Hoffman, Kevin Murphy, Rif Saurous, Eugene Brevdo, and David Blei| 2016
Source: http://bayesiandeeplearning.org/papers/BDL_43.pdf - Markov Chain Monte Carlo for Deep Latent Gaussian Models |Matthew Hoffman| 2016
Source: http://bayesiandeeplearning.org/papers/BDL_44.pdf - Semi-supervised Active Learning with Deep Probabilistic Generative Models | Amar Shah and Zoubin Ghahramani| 2016
Source: http://bayesiandeeplearning.org/papers/BDL_43.pdf - Thesis: Uncertainty in Deep Learning | Yarin Gal| PhD Thesis, 2016
Source: http://mlg.eng.cam.ac.uk/yarin/thesis/thesis.pdf, Blog: http://mlg.eng.cam.ac.uk/yarin/blog_2248.html
- Deep survival analysis|R. Ranganath, A. Perotte, N. Elhadad, and D. Blei|2016
Source: http://www.cs.columbia.edu/~blei/papers/RanganathPerotteElhadadBlei2016.pdf - Towards Bayesian Deep Learning: A Survey| Hao Wang, Dit-Yan Yeung|2016
Source: https://arxiv.org/pdf/1604.016622017
- Dropout Inference in Bayesian Neural Networks with Alpha-divergences |Yingzhen Li, Yarin Gal|2017
Source: https://arxiv.org/abs/1703.02914 - What Uncertainties Do We Need in Bayesian Deep Learning for Computer Vision? |Alex Kendall, Yarin Gal|2017
Source: https://arxiv.org/abs/1703.04977
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