Latent Translation: Crossing Modalities by Bridging Generative Models, Yingtao Tian, Jesse Engel
- AutoAugment: Learning Augmentation Policies from Data, Ekin D. Cubuk, Barret Zoph, Dandelion Mané, Vijay Vasudevan, Quoc V. Le
- Matrix factorization techniques for recommender systems, Yehuda Koren, Robert Bell, Chris Volinsky
- Understanding Hidden Memories of Recurrent Neural Networks, Yao Ming, et al.
Beyond News Contents: The Role of Social Context for Fake News Detection , Kai Shu, Suhang Wang, Huan Liu [pdf]
- Towards Federated Learning at Scale: System Design, Keith Bonawitz, et al.
- Learning with Privacy at Scale, Differential Privacy Team, Apple [blog]
mixup: Beyond Empirical Risk Minimization, Hongyi Zhang, Moustapha Cisse, Yann N. Dauphin, David Lopez-Paz
- Causal Reasoning from Meta-reinforcement Learning, Ishita Dasgupta, Jane Wang, Silvia Chiappa, Jovana Mitrovic, Pedro Ortega, David Raposo, Edward Hughes, Peter Battaglia, Matthew Botvinick, Zeb Kurth-Nelson
- GAN Dissection: Visualizing and Understanding Generative Adversarial Networks, David Bau, Jun-Yan Zhu, Hendrik Strobelt, Bolei Zhou, Joshua B. Tenenbaum, William T. Freeman, Antonio Torralba
- Visualizing the Loss Landscape of Neural Nets, Hao Li, Zheng Xu, Gavin Taylor, Christoph Studer, Tom Goldstein
- Many Analysts, One Data Set: Making Transparent How Variations in Analytic Choices Affect Results, R. Silberzahn, et al.
- Delayed Impact of Fair Machine Learning, Lydia T. Liu, Sarah Dean, Esther Rolf, Max Simchowitz, Moritz Hardt
Auto ML book chapter 3 on neural architecture search
- Learnability can be undecideable
- An overview of gradient descent optimization algorithms
- Many Analysts, One Datase
- AdaNet: Adaptive Structural Learning of Artificial Neural Networks
Neural Ordinary Differential Equations, Ricky T. Q. Chen, Yulia Rubanova, Jesse Bettencourt, David Duvenaud
- The Matrix Calculus You Need For Deep Learning, Terence Parr, Jeremy Howard
- Mining of Massive Datasets: Chapter 9 - Recommendation Systems, Jure Leskovec, Anand Rajaraman, Jeffrey David Ullman
- Spinning Up in Deep RL, OpenAI
- Knowledge Tracing Machines: Factorization Machines for Knowledge Tracing, Jill-Jênn Vie, Hisashi Kashima
- Visualizing the Loss Landscape of Neural Nets, Hao Li, Zheng Xu, Gavin Taylor, Christoph Studer, Tom Goldstein
- How to Start Training: The Effect of Initialization and Architecture, Boris Hanin, David Rolnick
- Disentangling Correlated Speaker and Noise for Speech Synthesis via Data Augmentation and Adversarial Factorization, Wei-Ning Hsu, Yu Zhang, Ron J. Weiss, Yu-An Chung, Yuxuan Wang, Yonghui Wu, James Glass
How Does Batch Normalization Help Optimization?, Shibani Santurkar, Dimitris Tsipras, Andrew Ilyas, Aleksander Madry
- On the Dimensionality of Word Embedding. Zi Yin, Yuanyuan Shen
- Review Papers: Modeling Capture, Recapture, and Removal Statistics for Estimation of Demographic Parameters for Fish and Wildlife Populations: Past, Present, and Future (paywalled), Kenneth Pollock
- Knowledge Tracing Machines: Factorization Machines for Knowledge Tracing, Jill-Jênn Vie, Hisashi Kashima
- Many Analysts, One Data Set: Making Transparent How Variations in Analytic Choices Affect Results, R. Silberzahn, et al.
Comprehensive benchmarking of Markov chain Monte Carlo methods for dynamical systems, Benjamin Ballnus, Sabine Hug, Kathrin Hatz, Linus Görlitz, Jan Hasenauer and Fabian J. Theis
- Categorical Reparameterization with Gumbel-Softmax, Eric Jang, Shixiang Gu, Ben Poole
- Many Analysts, One Data Set: Making Transparent How Variations in Analytic Choices Affect Results, R. Silberzahn, et al.
- An Empirical Evaluation of Generic Convolutional and Recurrent Networks for Sequence Modeling, Shaojie Bai, J. Zico Kolter, Vladlen Koltun
Visual Reinforcement learning with imagined goals, Ashvin Nair, Vitchyr Pong, Murtaza Dalal, Shikhar Bahl, Steven Lin, Sergey Levine
- Semi supervised classification with graph cnns,Thomas N. Kipf, Max Welling
- Learning dextrous hand manipulation, OpenAI: Marcin Andrychowicz, Bowen Baker, Maciek Chociej, Rafal Jozefowicz, Bob McGrew, Jakub Pachocki, Arthur Petron, Matthias Plappert, Glenn Powell, Alex Ray, Jonas Schneider, Szymon Sidor, Josh Tobin, Peter Welinder, Lilian Weng, Wojciech Zaremba
- Non‐parametric evidence of second‐leg home advantage in European football., Gery Geenens, Thomas Cuddihy
PointNet: Deep Learning on Point Sets for 3D Classification and Segmentation, Charles R. Qi, Hao Su, Kaichun Mo, Leonidas J. Guibas
- Deep learning of aftershock patterns following large earthquakes, Phoebe M. R. DeVries, Fernanda Viégas, Martin Wattenberg & Brendan J. Meade [blog]
- StarSpace: Embed All The Things!, Ledell Wu, Adam Fisch, Sumit Chopra, Keith Adams, Antoine Bordes, Jason Weston
- Anyone Can Become a Troll: Causes of Trolling Behavior in Online Discussions, Justin Cheng, Michael Bernstein, Cristian Danescu-Niculescu-Mizil, and Jure Leskovec
GAN Lab: Understanding Complex Deep Generative Models using Interactive Visual Experimentation, Minsuk Kahng, Nikhil Thorat, Duen Horng Chau, Fernanda Viégas, Martin Wattenberg
- A Conceptual Introduction to Hamiltonian Monte Carlo, Michael Betancourt
- Factors associated with supermarket and convenience store closure: a discrete time spatial survival modelling approach (paywall), Joshua L. Warren, Penny Gordon‐Larsen
Snorkel: Rapid Training Data Creation with Weak Supervision, Alexander Ratner, Stephen H. Bach, Henry Ehrenberg, Jason Fries, Sen Wu, Christopher Ré
- Factors associated with supermarket and convenience store closure: a discrete time spatial survival modelling approach (paywall), Joshua L. Warren, Penny Gordon‐Larsen
- Deep learning via Hessian-free optimization, James Martens
- Neural Combinatorial Optimization with Reinforcement Learning, Irwan Bello, Hieu Pham, Quoc V. Le, Mohammad Norouzi, Samy Bengio
BPR: Bayesian Personalized Ranking from Implicit Feedback, Steffen Rendle, Christoph Freudenthaler, Zeno Gantner, Lars Schmidt-Thieme
- Image-to-Image Translation with Conditional Adversarial Networks, Phillip Isola, Jun-Yan Zhu, Tinghui Zhou, Alexei A. Efros
- Yes, but Did It Work?: Evaluating Variational Inference, Yuling Yao, Aki Vehtari, Daniel Simpson, Andrew Gelman
A Principled Bayesian Workflow, Michael Betancourt
- Deep Complex Networks, Chiheb Trabelsi, et al.
- Image-to-Image Translation with Conditional Adversarial Networks, Phillip Isola, Jun-Yan Zhu, Tinghui Zhou, Alexei A. Efros
- Adversarial Reprogramming of Neural Networks, Gamaleldin F. Elsayed, Ian Goodfellow, Jascha Sohl-Dickstein
- This looks like that: deep learning for interpretable image recognition, Chaofan Chen, Oscar Li, Alina Barnett, Jonathan Su, Cynthia Rudin
- Taking the Human Out of the Loop: A Review of Bayesian Optimization, Bobak Shahriari, Kevin Swersky, Ziyu Wang, Ryan P. Adams, and Nando de Freitas
- Dynamic Routing Between Capsules, Sara Sabour, Nicholas Frosst, Geoffrey E Hinton
Conditional Neural Processes, Marta Garnelo, Dan Rosenbaum, Chris J. Maddison, Tiago Ramalho, David Saxton, Murray Shanahan, Yee Whye Teh, Danilo J. Rezende, S. M. Ali Eslami
- Table-to-text Generation by Structure-aware Seq2seq Learning, Tianyu Liu, Kexiang Wang, Lei Sha, Baobao Chang, Zhifang Sui
- Curriculum Learning, Yoshua Bengio, Jérôme Louradour, Ronan Collobert, Jason Weston
DeepTest: Automated Testing of Deep-Neural-Network-driven Autonomous Cars, Yuchi Tian, Kexin Pei, Suman Jana, Baishakhi Ray
- Continuous control with deep reinforcement learning, Timothy P. Lillicrap, Jonathan J. Hunt, Alexander Pritzel, Nicolas Heess, Tom Erez, Yuval Tassa, David Silver, Daan Wierstra
- DSOS and SDSOS Optimization: More Tractable Alternatives to Sum of Squares and Semidefinite Optimization, Amir Ali Ahmadi, Anirudha Majumdar
- Gradient-based optimization of neural network architectures, Will Grathwohl, Elliot Creager, Seyed Kamyar Seyed Ghasemipour, Richard Zemel
Probabilistic Numerics and Uncertainty in Computations, Philipp Hennig, Michael A Osborne, Mark Girolami
- Solving the Rubik's Cube Without Human Knowledge, Stephen McAleer, Forest Agostinelli, Alexander Shmakov, Pierre Baldi
- CleanNet: Transfer Learning for Scalable Image Classifier Training with Label Noise, Kuang-Huei Lee, Xiaodong He, Lei Zhang, Linjun Yang
- DeepTest: Automated Testing of Deep-Neural-Network-driven Autonomous Cars, Yuchi Tian, Kexin Pei, Suman Jana, Baishakhi Ray
- Neural scene representation and rendering, S. M. Ali Eslami, Danilo Jimenez Rezende, Frederic Besse, et al.
- Universal Language Model Fine-tuning for Text Classification, Jeremy Howard, Sebastian Ruder
No Free Lunch Theorems for Optimization, D.H. Wolpert, W.G. Macready
- Delete, Retrieve, Generate: A Simple Approach to Sentiment and Style Transfer, Juncen Li, Robin Jia, He He, Percy Liang
- AutoAugment: Learning Augmentation Policies from Data, Ekin D. Cubuk, Barret Zoph, Dandelion Mane, Vijay Vasudevan, Quoc V. Le
Looking to Listen at the Cocktail Party: A Speaker-Independent Audio-Visual Model for Speech Separation, Ariel Ephrat, Inbar Mosseri, Oran Lang, Tali Dekel, Kevin Wilson, Avinatan Hassidim, William T. Freeman, Michael Rubinstein [blog] [video] [webpage]
- Delete, Retrieve, Generate: A Simple Approach to Sentiment and Style Transfer, Juncen Li, Robin Jia, He He, Percy Liang
- Poincaré Embeddings for Learning Hierarchical Representations, Maximillian Nickel, Douwe Kiela
- Attention is All You Need, Ashish Vaswani, Noam Shazeer, Niki Parmar, Jakob Uszkoreit, Llion Jones, Aidan N. Gomez, Łukasz Kaiser, Illia Polosukhin
Bayesians, Frequentists, and Scientists, Bradley Efron
- Attention Is All You Need, Ashish Vaswani, Noam Shazee, Niki Parmar, Jakob Uszkoreit, Llion Jones, Aidan N. Gomez, Łukasz Kaiser, Illia Polosukhin
- Is Most Published Research Really False?, Jeffrey T. Leek and Leah R. Jager
- Cluster failure: Why fMRI inferences for spatial extent have inflated false-positive rates, Anders Eklund, Thomas E. Nichols, and Hans Knutsson
- Equality of Opportunity in Supervised Learning, Moritz Hardt, Eric Price, Nathan Srebro
Stability, Bin Yu
- The Marginal Value of Adaptive Gradient Methods in Machine Learning, Ashia C. Wilson, Rebecca Roelofs, Mitchell Stern, Nati Srebro, Benjamin Recht
- The Surprising Creativity of Digital Evolution: A Collection of Anecdotes from the Evolutionary Computation and Artificial Life Research Communities, Joel Lehman et al.
Topological Data Analysis, Larry Wasserman
- Data2Vis: Automatic Generation of Data Visualizations Using Sequence to Sequence Recurrent Neural Networks, Victor Dibia, Çagatay Demiralp
- Attention is All You Need, Ashish Vaswani, Noam Shazeer, Niki Parmar, Jakob Uszkoreit, Llion Jones, Aidan N. Gomez, Łukasz Kaiser, Illia Polosukhin
Self-Normalizing Neural Networks, Günter Klambauer, Thomas Unterthiner, Andreas Mayr, Sepp Hochreiter
- Improving Generalization Performance by Switching from Adam to SGD, Nitish Shirish Keskar, Richard Socher
- Can you Trust the Trend: Discovering Simpson's Paradoxes in Social Data, Nazanin Alipourfard, Peter G. Fennell, Kristina Lerman
- Word Translation Without Parallel Data, Alexis Conneau, Guillaume Lample, Marc'Aurelio Ranzato, Ludovic Denoyer, Hervé Jégou
Deep Image Prior, Dmitry Ulyanov, Andrea Vedaldi, Victor Lempitsky [blog] [supplementary material] [code]
- The Surprising Creativity of Digital Evolution: A Collection of Anecdotes from the Evolutionary Computation and Artificial Life Research Communities, Joel Lehman, Jeff Clune, Dusan Misevic, Christoph Adami, Julie Beaulieu, Peter J. Bentley, Samuel Bernard, Guillaume Belson, David M. Bryson, Nick Cheney, Antoine Cully, Stephane Donciuex, Fred C. Dyer, Kai Olav Ellefsen, Robert Feldt, Stephan Fischer, Stephanie Forrest, Antoine Frénoy, Christian Gagneé, Leni Le Goff, Laura M. Grabowski, Babak Hodjat, Laurent Keller, Carole Knibbe, Peter Krcah, Richard E. Lenski, Hod Lipson, Robert MacCurdy, Carlos Maestre, Risto Miikkulainen, Sara Mitri, David E. Moriarty, Jean-Baptiste Mouret, Anh Nguyen, Charles Ofria, Marc Parizeau, David Parsons, Robert T. Pennock, William F. Punch, Thomas S. Ray, Marc Schoenauer, Eric Shulte, Karl Sims, Kenneth O. Stanley, François Taddei, Danesh Tarapore, Simon Thibault, Westley Weimer, Richard Watson, et al. (1 additional author not shown)
- Distilling a Neural Network Into a Soft Decision Tree, Nicholas Frosst, Geoffrey Hinton
Distilling the Knowledge in a Neural Network, Geoffrey Hinton, Oriol Vinyals, Jeff Dean
- Unsupervised Word Mapping Using Structural Similarities in Monolingual Embeddings, Hanan Aldarmaki, Mahesh Mohan, Mona Diab
- The philosophy of Bayes factors and the quantification of statistical evidence, Richard D. Morey, Jan-Willem Romeijn, Jeffrey N. Rouder
- The Secret Sharer: Measuring Unintended Neural Network Memorization & Extracting Secrets, Nicholas Carlini, Chang Liu, Jernej Kos, Úlfar Erlingsson, Dawn Song
Missing-data imputation, Andrew Gelman and Jennifer Hill
- Hybrid computing using a neural network with dynamic external memory, A Graves, G Wayne, M Reynolds, T Harley, I Danihelka
- Spherical CNNS, Taco S. Cohen, Mario Geiger, Jonas Köhler, Max Welling
- You Only Look Once: Unified, Real-Time Object Detection, Joseph Redmon, Santosh Divvala, Ross Girshick, Ali Farhadi
Probabilistic record linkage, Adrian Sayers, Yoav Ben-Shlomo, Ashley W Blom and Fiona Steele
- You Only Look Once: Unified, Real-Time Object Detection, Joseph Redmon, Santosh Divvala, Ross Girshick, Ali Farhadi
- Virtual Adversarial Ladder Networks For Semi-supervised Learning, Saki Shinoda, Daniel E. Worrall, Gabriel J. Brostow
- Equation-free mechanistic ecosystem forecasting using empirical dynamic modeling, Hao Ye, Richard J. Beamish, Sarah M. Glaser, Sue C. H. Grant, Chih-hao Hsieh, Laura J. Richards, Jon T. Schnute and George Sugihara
- Mastering Chess and Shogi by Self-Play with a General Reinforcement Learning Algorithm, David Silver, Thomas Hubert, Julian Schrittwieser, Ioannis Antonoglou, Matthew Lai, Arthur Guez, Marc Lanctot, Laurent Sifre, Dharshan Kumaran, Thore Graepel, Timothy Lillicrap, Karen Simonyan, Demis Hassabis
- Character-level Convolutional Networks for Text Classification, Xiang Zhang, Junbo Zhao, Yann LeCun
PD-Sparse : A Primal and Dual Sparse Approach to Extreme Multiclass and Multilabel Classification, Ian E. H. Yen, Xiangru Huang, Kai Zhong, Pradeep Ravikumar, Inderjit S. Dhillon
- DeepZip: Lossless Compression using Recurrent Networks, Kedar Tatwawadi
- Equation-free mechanistic ecosystem forecasting using empirical dynamic modeling, Hao Ye, Richard J. Beamish, Sarah M. Glaser, Sue C. H. Grant, Chih-hao Hsieh, Laura J. Richards, Jon T. Schnute and George Sugihara
- Mastering Chess and Shogi by Self-Play with a General Reinforcement Learning Algorithm, David Silver, Thomas Hubert, Julian Schrittwieser, Ioannis Antonoglou, Matthew Lai, Arthur Guez, Marc Lanctot, Laurent Sifre, Dharshan Kumaran, Thore Graepel, Timothy Lillicrap, Karen Simonyan, Demis Hassabis
Opening the Black Box of Deep Neural Networks via Information, Ravid Shwartz-Ziv, Naftali Tishby
- Understanding deep learning requires rethinking generalization, Chiyuan Zhang, Samy Bengio, Moritz Hardt, Benjamin Recht, Oriol Vinyals
- Transformation invariant and outlier revealing dimensionality reduction using triplet embedding, Ehsan Amid, Manfred K. Warmuth
- When to conduct probabilistic linkage vs. deterministic linkage? A simulation study, Y Zhu, Y Matsuyama, Y Ohashi, S Setoguchi
- Word Translation Without Parallel Data, Alexis Conneau, Guillaume Lample, Marc'Aurelio Ranzato, Ludovic Denoyer, Hervé Jégou
- Learning Transferable Architectures for Scalable Image Recognition, Barret Zoph, Vijay Vasudevan, Jonathon Shlens, Quoc V. Le
Solving a Higgs optimization problem with quantum annealing for machine learning, Alex Mott, Joshua Job, Jean-Roch Vlimant, Daniel Lidar & Maria Spiropulu
- Bayesian Inference for Assessing Effects of Email Marketing Campaigns, Wu, Jiexing, Kate J. Li, and Jun S. Liu
- Why Neurons Have Thousands of Synapses, a Theory of Sequence Memory in Neocortex, Jeff Hawkins and Subutai Ahmad
- Matrix capsules with EM routing, Geoffrey E Hinton, Sara Sabour, Nicholas Frosst
- End-To-End Memory Networks, Sainbayar Sukhbaatar, Arthur Szlam, Jason Weston, Rob Fergus
Dynamic Routing Between Capsules, Sara Sabour, Nicholas Frosst, Geoffrey E. Hinton
- Word Translation Without Parallel Data, Alexis Conneau, Guillaume Lample, Marc'Aurelio Ranzato, Ludovic Denoyer, Hervé Jégou
- Practical Bayesian Inference for Record Linkage, Brendan S. McVeigh, Jared S. Murray
- Deep Illumination: Approximating Dynamic Global Illumination with Generative Adversarial Network, Manu Mathew Thomas, Angus G. Forbes
- WaveNet: A Generative Model for Raw Audio, Aaron van den Oord, Sander Dieleman, Heiga Zen, Karen Simonyan, Oriol Vinyals, Alex Graves, Nal Kalchbrenner, Andrew Senior, Koray Kavukcuoglu
- Conditional Random Fields as Recurrent Neural Networks, Shuai Zheng, Sadeep Jayasumana, Bernardino Romera-Paredes, Vibhav Vineet, Zhizhong Su3 Dalong Du, Chang Huang, Philip H. S. Torr
Speech Enhancement Using Bayesian Wavenet, Kaizhi Qian, Yang Zhang, Shiyu Chang, Xuesong Yang, Dinei Florencio, Mark Hasegawa-Johnson [code]
- Age Progression/Regression by Conditional Adversarial Autoencoder, Zhifei Zhang, Yang Song, Hairong Qi
- Trust in numbers, David Spiegelhalter
- Training recurrent networks online without backtracking, Yann Ollivier, Corentin Tallec, Guillaume Charpiat
- Knowledge Vault: A Web-Scale Approach to Probabilistic Knowledge Fusion, Xin Luna Dong, Evgeniy Gabrilovich, Geremy Heitz, Wilko Horn, Ni Lao, Kevin Murphy, Thomas Strohmann, Shaohua Sun, Wei Zhang
- Perturbations in Epidemiological Models: When zombies attack, we can survive!, Robert F. Allen, Cassandra Jens & Theodore J. Wendt
If correlation doesn’t imply causation, then what does?, Michael Nielsen
- Multi-Scale Context Aggregation by Dilated Convolutions, Fisher Yu, Vladlen Koltun
- Perturbations in Epidemiological Models: When zombies attack, we can survive!, Robert F. Allen, Cassandra Jens & Theodore J. Wendt
Evidence of bias in the Eurovision song contest: modelling the votes using Bayesian hierarchical models, Marta Blangiardo, Gianluca Baio
- Data Programming: Creating Large Training Sets, Quickly, Ratner, De Sa, Wu, Selsam, Ré
- “Why Should I Trust You?” Explaining the Predictions of Any Classifier, Marco Tulio Ribeiro, Sameer Singh, Carlos Guestrin
- Opening the Black Box of Deep Neural Networks via Information, Ravid Shwartz-Ziv, Naftali Tishby
Learning to learn by gradient descent by gradient descent, Marcin Andrychowicz, Misha Denil, Sergio Gómez Colmenarejo, Matthew W. Hoffman, David Pfau, Tom Schaul, Brendan Shillingford, Nando de Freitas
- Evidence of bias in the Eurovision song contest: modelling the votes using Bayesian hierarchical models, Marta Blangiardo, Gianluca Baio
- Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift, Sergey Ioffe, Christian Szegedy
- BadNets: Identifying Vulnerabilities in the Machine Learning Model Supply Chain, Tianyu Gu, Brendan Dolan-Gavitt, Siddharth Garg
- Attentive Recurrent Comparators, Pranav Shyam, Shubham Gupta, Ambedkar Dukkipati
A Brief Survey of Deep Reinforcement Learning, Kai Arulkumaran, Marc Peter Deisenroth, Miles Brundage, Anil Anthony Bharath
- Methodologies for Cross-Domain Data Fusion: An Overview, Yu Zheng [pdf]
- Transitive Invariance for Self-supervised Visual Representation Learning, Xiaolong Wang, Kaiming He, Abhinav Gupta
- An overview of gradient descent optimization algorithms, Sebastian Ruder
- Engineering Efficient and Effective Non-Metric Space Library, Leonid Boytsov and Bilegsaikhan Naidan
Variational Inference: A Review for Statisticians, David M. Blei, Alp Kucukelbir, Jon D. McAuliffe
- Challenges in Data-to-Document Generation, Sam Wiseman, Stuart M. Shieber, Alexander M. Rush
- Transitive Invariance for Self-supervised Visual Representation Learning, Xiaolong Wang, Kaiming He, Abhinav Gupta
- An overview of gradient descent optimization algorithms, Sebastian Ruder
- Engineering Efficient and Effective Non-Metric Space Library, Leonid Boytsov and Bilegsaikhan Naidan
Deal or No Deal? End-to-End Learning for Negotiation Dialogues, Lewis, Yarats, Dauphin, Parikh, Batra [blog] [code]
- Data Programming: Creating Large Training Sets, Quickly, Ratner, De Sa, Wu, Selsam, Ré
- BIRDNEST: Bayesian Inference for Ratings-Fraud Detection, Hooi, Shah, Beutel, Gunnemann, Akoglu, Kumar, Makhija, Faloutsos
- Stealing Machine Learning Models via Prediction APIs, Tramèr, Zhang, Juels, Reiter, Ristenpart [slides] [code] [video]
Tutorial on Variational Autoencoders, Carl Doersch
- Data Programming: Creating Large Training Sets, Quickly, Alexander Ratner, Christopher De Sa, Sen Wu, Daniel Selsam, Christopher Ré
- Climbing the Kaggle Leaderboard by Exploiting the Log-Loss Oracle, Jacob Whitehill
What Uncertainties Do We Need in Bayesian Deep Learning for Computer Vision?, Alex Kendall, Yarin Gal
- Surprise Search: Beyond Objectives and Novelty, Daniele Gravina, Antonios Liapis, Georgios N. Yannakakis
- Two Decades of Recommender Systems at Amazon.com, Brent Smith, Greg Linden
- Data Programming: Creating Large Training Sets, Quickly, Alexander J. Ratner, Christopher M. De Sa, Sen Wu, Daniel Selsam, Christopher Ré
Introduction to Nonnegative Matrix Factorization, Nicolas Gillis
- Collaborative Filtering for Implicit Feedback Datasets, Yifan Hu, Yehuda Koren, Chris Volinsky
- Manifold Relevance Determination, Andreas Damianou, Carl Ek, Michalis Titsias, Neil Lawrence
- DART: Dropouts meet Multiple Additive Regression Trees, K. V. Rashmi, Ran Gilad-Bachrach
- Count-ception: Counting by Fully Convolutional Redundant Counting, Joseph Paul Cohen, Henry Z. Lo, Yoshua Bengio
Pixel Recurrent Neural Networks, Aäron van den Oord, Nal Kalchbrenner, Koray Kavukcuoglu [notes]
- Densely Connected Convolutional Networks, Gao Huang, Zhuang Liu, Kilian Q. Weinberger, Laurens van der Maaten
- Large-Scale Evolution of Image Classifiers, Esteban Real, Sherry Moore, Andrew Selle, Saurabh Saxena, Yutaka Leon Suematsu, Jie Tan, Quoc Le, Alex Kurakin
Constraints versus priors, Philip B. Stark
- Modelling illegal drug participation, Sarah Brown, Mark N. Harris, Preety Srivastava, Xiaohui Zhang
- Learning to learn by gradient descent by gradient descent, Marcin Andrychowicz, Misha Denil, Sergio Gómez Colmenarejo, Matthew W. Hoffman, David Pfau, Tom Schaul, Brendan Shillingford, Nando de Freitas
- ML confidential: machine learning on encrypted data, Thore Graepel, Kristin Lauter, and Michael Naehrig
- Using stacking to average Bayesian predictive distributions, Yuling Yao, Aki Vehtari, Daniel Simpson, Andrew Gelman
Semi-supervised knowledge transfer for deep learning from private training data, Nicolas Papernot, Martín Abadi, Úlfar Erlingsson, Ian Goodfellow, Kunal Talwar
- Collaborative filtering for implicit feedback datasets, Yifan Hu, Yehuda Koren, Chris Volinsky [pdf]
- Model-based biclustering of clickstream data [paywalled], Volodymyr Melnykov
- Phase-functioned neural networks for character control, Daniel Holden, Taku Komura, Jun Saito
- Approximate Bayesian inference for latent Gaussian models by using integrated nested Laplace approximations, Håvard Rue, Sara Martino, Nicolas Chopin [pdf]
[paywalled] Statistical modelling of a terrorist network, Murray Aitkin, Duy Vu, Brian Francis [slides]
- Constraints versus priors, Philip B. Stark
- To explain or to predict?, Galit Shmueli
- Collaborative filtering for implicit feedback datasets, Yifan Hu, Yehuda Koren, Chris Volinsky
Deep Probabilistic Programming, Dustin Tran, Matthew D. Hoffman, Rif A. Saurous, Eugene Brevdo, Kevin Murphy, David M. Blei
- Multi-talker Speech Separation and Tracing with Permutation Invariant Training of Deep Recurrent Neural Networks, Morten Kolbæk, Dong Yu, Zheng-Hua Tan, Jesper Jensen
- Constraints versus Priors, Philip B. Stark
- Forecasting at Scale, Sean J. Taylor and Benjamin Letham
- Asymptotically exact inference in differentiable generative models, Matthew Graham, Amos Storkey
- Judgment under Uncertainty: Heuristics and Biases, Amos Tversky, Daniel Kahneman
Genotype–environment interactions in mouse behavior: A way out of the problem, Neri Kafkafi, Yoav Benjamini, Anat Sakov, Greg I. Elmer, and Ilan Golani
- Universal adversarial perturbations, Seyed-Mohsen Moosavi-Dezfooli, Alhussein Fawzi, Omar Fawzi, Pascal Frossard
- Deep Voice: Real-time Neural Text-to-Speech, Sercan O. Arik, Mike Chrzanowski, Adam Coates, Gregory Diamos, Andrew Gibiansky, Yongguo Kang, Xian Li, John Miller, Andrew Ng, Jonathan Raiman, Shubho Sengupta, Mohammad Shoeybi [blog post]
- Ambulance Location for Maximum Survival, Erhan Erkut, Armann Ingolfsson, Güneş Erdoğan
- XNOR-Net: ImageNet Classification Using Binary Convolutional Neural Networks, Mohammad Rastegari, Vicente Ordonez, Joseph Redmon, Ali Farhadi
Modeling Customer Lifetimes with Multiple Causes of Churn, Michael Braun, David A. Schweidel
- Deep Voice: Real-time Neural Text-to-Speech, Sercan O. Arik, Mike Chrzanowski, Adam Coates, Gregory Diamos, Andrew Gibiansky, Yongguo Kang, Xian Li, John Miller, Andrew Ng, Jonathan Raiman, Shubho Sengupta, Mohammad Shoeybi [blog post]
- Asynchronous Methods for Deep Reinforcement Learning, Volodymyr Mnih, Adrià Puigdomènech Badia, Mehdi Mirza, Alex Graves, Timothy P. Lillicrap, Tim Harley, David Silver, Koray Kavukcuoglu
- Is most published research really false?, Jeffrey T Leek, Leah R Jager
- Evolution Strategies as a Scalable Alternative to Reinforcement Learning, Tim Salimans, Jonathan Ho, Xi Chen, Ilya Sutskever
PathNet: Evolution Channels Gradient Descent in Super Neural Networks, Chrisantha Fernando, Dylan Banarse, Charles Blundell, Yori Zwols, David Ha, Andrei A. Rusu, Alexander Pritzel, Daan Wierstra
- DeepCoder: Learning to Write Programs, Matej Balog, Alexander L. Gaunt, Marc Brockschmidt, Sebastian Nowozin, Daniel Tarlow
- Negative training data can be harmful to text classification, Xiao-Li Li, Bing Liu, See-Kiong Ng
- Forecasting at Scale, Sean J. Taylor and Benjamin Letham [blog post]
Understanding deep learning requires rethinking generalization, Chiyuan Zhang, Samy Bengio, Moritz Hardt, Benjamin Recht, Oriol Vinyals
- Safe and Nested Endgame Solving for Imperfect-Information Games, Noam Brown, Tuomas Sandholm
- Automatic Differentiation Variational Inference, Alp Kucukelbir, Dustin Tran, Rajesh Ranganath, Andrew Gelman, David M. Blei
- Negative training data can be harmful to text classification, Xiao-Li Li, Bing Liu, See-Kiong Ng
Deep clustering: Discriminative embeddings for segmentation and separation, John R. Hershey, Zhuo Chen, Jonathan Le Roux, Shinji Watanabe
- A Conceptual Introduction to Hamiltonian Monte Carlo, Michael Betancourt
- Latent Dirichlet Allocation, David M. Blei, Andrew Y. Ng, Michael I. Jordan
- On using expert opinion in ecological analyses: a frequentist approach, Subhash R. Lele, Kristie L. Allen
- Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift, Sergey Ioffe, Christian Szegedy
- Finding scientific topics, Thomas L. Griffiths, Mark Steyvers
DeepStack: Expert-Level Artificial Intelligence in No-Limit Poker, Matej Moravčík, Martin Schmid, Neil Burch, Viliam Lisý, Dustin Morrill, Nolan Bard, Trevor Davis, Kevin Waugh, Michael Johanson, Michael Bowling
- Unrolled Generative Adversarial Networks, Luke Metz, Ben Poole, David Pfau, Jascha Sohl-Dickstein
- Cluster failure: Why fMRI inferences for spatial extent have inflated false-positive rates, Anders Eklunda, Thomas E. Nicholsd, and Hans Knutssona
- Beyond Object Recognition: Visual Sentiment Analysis with Deep Coupled Adjective and Noun Neural Networks, Jingwen Wang, Jianlong Fu, Yong Xu, Tao Mei
- Privacy-Preserving Data Analysis for the Federal Statistical Agencies, John Abowd, Lorenzo Alvisi, Cynthia Dwork, Sampath Kannan, Ashwin Machanavajjhala, Jerome Reiter
Exploring Online Ad Images Using a Deep Convolutional Neural Network Approach, Michael Fire, Jonathan Schler
- In-season prediction of batting averages: A field test of empirical Bayes and Bayes methodologies, Lawrence D. Brown
- Human-level concept learning through probabilistic program induction, Brenden M. Lake, Ruslan Salakhutdinov, Joshua B. Tenenbaum
- #HashtagWars: Learning a Sense of Humor, Peter Potash, Alexey Romanov, Anna Rumshisky
Building Machines That Learn and Think Like People, Brenden M. Lake, Tomer D. Ullman, Joshua B. Tenenbaum, Samuel J. Gershman
- A density-based algorithm for discovering clusters in large spatial databases with noise, M Ester, HP Kriegel, J Sander, X Xu
- Survival Analysis Part I: Basic concepts and first analyses, TG Clark, MJ Bradburn, SB Love and DG Altman
- Quantifying the evolution of individual scientific impact, R Sinatra, D Wang, P Deville, C Song, A-L Barabási
The Parable of Google Flu: Traps in Big Data Analysis, David Lazer, Ryan Kennedy, Gary King, Alessandro Vespignani
- Hybrid computing using a neural network with dynamic external memory, Alex Graves, Greg Wayne, Malcolm Reynolds, Tim Harley, Ivo Danihelka, Agnieszka Grabska-Barwińska, Sergio Gómez Colmenarejo, Edward Grefenstette, Tiago Ramalho, John Agapiou, Adrià Puigdomènech Badia, Karl Moritz Hermann, Yori Zwols, Georg Ostrovski, Adam Cain, Helen King, Christopher Summerfield, Phil Blunsom, Koray Kavukcuoglu & Demis Hassabis
- Deep Residual Learning for Image Recognition, Kaiming He, Xiangyu Zhang, Shaoqing Ren, Jian Sun
- Playing FPS Games with Deep Reinforcement Learning, Guillaume Lample, Devendra Singh Chaplot
- Google's Multilingual Neural Machine Translation System: Enabling Zero-Shot Translation, Melvin Johnson, Mike Schuster, Quoc V. Le, Maxim Krikun, Yonghui Wu, Zhifeng Chen, Nikhil Thorat, Fernanda Viégas, Martin Wattenberg, Greg Corrado, Macduff Hughes, Jeffrey Dean
Faster R-CNN: Towards Real-Time Object Detection with Region Proposal Networks, Shaoqing Ren, Kaiming He, Ross Girshick, Jian Sun [code]
- Deep Gaussian Processes, Andreas C. Damianou, Neil D. Lawrence
- Stealing Machine Learning Models via Prediction APIs, Florian Tramèr, Fan Zhang, Ari Juels, Michael K. Reiter, Thomas Ristenpart [code]
- Distilling the Knowledge in a Neural Network, Geoffrey Hinton, Oriol Vinyals, Jeff Dean
Multifaceted Feature Visualization: Uncovering the Different Types of Features Learned By Each Neuron in Deep Neural Networks, Anh Nguyen, Jason Yosinski, Jeff Clune [code]
- Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift, Sergey Ioffe, Christian Szegedy
- Stealing Machine Learning Models via Prediction APIs, Florian Tramèr, Fan Zhang, Ari Juels, Michael K. Reiter, Thomas Ristenpart
- Hybrid computing using a neural network with dynamic external memory, Alex Graves, Greg Wayne, Malcolm Reynolds, Tim Harley, Ivo Danihelka, Agnieszka Grabska-Barwińska, Sergio Gómez Colmenarejo, Edward Grefenstette, Tiago Ramalho, John Agapiou, Adrià Puigdomènech Badia, Karl Moritz Hermann, Yori Zwols, Georg Ostrovski, Adam Cain, Helen King, Christopher Summerfield, Phil Blunsom, Koray Kavukcuoglu & Demis Hassabis
- Deep, Convolutional, and Recurrent Models for Human Activity Recognition using Wearables, Nils Y. Hammerla, Shane Halloran, Thomas Ploetz
Origins of Presidential poll aggregation: A perspective from 2004 to 2012, Samuel S.-H. Wang [code]
- DeepEar: Robust Smartphone Audio Sensing in Unconstrained Acoustic Environments using Deep Learning, Nicholas D. Lane, Petko Georgiev, Lorena Qendro
- Equality of Opportunity in Supervised Learning, Moritz Hardt, Eric Price, Nathan Srebro
- Dynamic Memory Networks for Visual and Textual Question Answering, Caiming Xiong, Stephen Merity, Richard Socher
- Detecting events and key actors in multi-person videos, Vignesh Ramanathan, Jonathan Huang, Sami Abu-El-Haija, Alexander Gorban, Kevin Murphy, Li Fei-Fei
Parameter estimation for text analysis, Gregor Heinrich
- Stan: A probabilistic programming language for Bayesian inference and optimization, Andrew Gelman, Daniel Lee, Jiqiang Guo
- No free lunch theorems for optimization, David H. Wolpert, William G. Macready
- Towards a Mathematical Theory of Cortical Micro-circuits, Dileep George, Jeff Hawkins
- Expectation propagation as a way of life: A framework for Bayesian inference on partitioned data, Aki Vehtari, Andrew Gelman, Tuomas Sivula, Pasi Jylänki, Dustin Tran, Swupnil Sahai, Paul Blomstedt, John P. Cunningham, David Schiminovich, Christian Robert
- The Model Complexity Myth, Jake VanderPlas
- 2016-10-12: Gaussian processes for time-series modelling, S. Roberts, M. Osborne, M. Ebden, S. Reece, N. Gibson, S. Aigrain [pdf]
- 2016-09-28: Boltzmann Machines, Geoffrey Hinton
- 2016-09-14: XGBoost: A Scalable Tree Boosting System, Tianqi Chen, Carlos Guestrin
- 2016-08-31: Visualizing Data using t-SNE, Laurens van der Maaten, Geoffrey Hinton
- 2016-08-17: Big Learning with Bayesian Methods, Jun Zhu, Jianfei Chen, Wenbo Hu
- 2016-08-03: Abandoning Objectives: Evolution through the Search for Novelty Alone, Joel Lehman, Kenneth O. Stanley
- 2016-07-20: POINTWISE: Predicting Points and Valuing Decisions in Real Time with NBA Optical Tracking Data, Dan Cervone, Alexander D’Amour, Luke Bornn, Kirk Goldsberry
- 2016-07-06: Deep Patient: An Unsupervised Representation to Predict the Future of Patients from the Electronic Health Records, Riccardo Miotto, Li Li, Brian A. Kidd & Joel T. Dudley
- 2016-06-22: Learning Phrase Representations using RNN Encoder–Decoder for Statistical Machine Translation, Kyunghyun Cho, Bart van Merrienboer, Caglar Gulcehre, Dzmitry Bahdanau, Fethi Bougares, Holger Schwenk, Yoshua Bengio
- 2016-06-08: Unsupervised Representation Learning with Deep Convolutional Generative Adversarial Networks, Alec Radford, Luke Metz, Soumith Chintala
- 2016-05-25: Intriguing properties of neural networks, Christian Szegedy, Wojciech Zaremba, Ilya Sutskever, Joan Bruna, Dumitru Erhan, Ian Goodfellow, Rob Fergus
- 2016-05-11: “Why Should I Trust You?” Explaining the Predictions of Any Classifier, Marco Tulio Ribeiro, Sameer Singh, Carlos Guestrin
- 2016-04-27: Linear Models: A Useful “Microscope” for Causal Analysis, Judea Pearl
- 2016-04-13: INFERRING CAUSAL IMPACT USING BAYESIAN STRUCTURAL TIME-SERIES MODELS, Kay H. Brodersen, Fabian Gallusser, Jim Koehler, Nicolas Remy and Steven L. Scott
- 2016-03-30: Mastering the game of Go with deep neural networks and tree search, David Silver, Aja Huang1, Chris J. Maddison, Arthur Guez, Laurent Sifre, George van den Driessche,Julian Schrittwieser, Ioannis Antonoglou, Veda Panneershelvam, Marc Lanctot, Sander Dieleman, Dominik Grewe, John Nham, Nal Kalchbrenner, Ilya Sutskever, Timothy Lillicrap, Madeleine Leach, Koray Kavukcuoglu1, Thore Graepel1, Demis Hassabis
- 2016-03-16: A Neural Algorithm of Artistic Style, Leon A. Gatys, Alexander S. Ecker, Matthias Bethge
- 2016-03-02: Probabilistic Methods for Time-Series Analysis, Idris A. Eckley, Paul Fearnhead, Rebecca Killick
- 2016-02-17: Practical recommendations for gradient-based training of deep architectures, Yoshua Bengio
- 2016-01-20: Expectation propagation as a way of life, Andrew Gelman, Aki Vehtari, Pasi Jylänki, Christian Robert, Nicolas Chopink, John P. Cunningham
- 2016-01-06: Aggregation for the probabilistic traveling salesman problem, Ann Melissa Campbell