Papers
Deep Gaussian Processes for Regression using Approximate Expectation Propagation
Thang Bui, Daniel Hernandez-Lobato, Jose Hernandez-Lobato et al.
Deep Speech 2 : End-to-End Speech Recognition in English and Mandarin
Dario Amodei, Sundaram Ananthanarayanan, Rishita Anubhai et al.
Deep Structured Energy Based Models for Anomaly Detection
Shuangfei Zhai, Yu Cheng, Weining Lu et al.
Dictionary Learning for Massive Matrix Factorization
Arthur Mensch, Julien Mairal, Bertrand Thirion et al.
Differential Geometric Regularization for Supervised Learning of Classifiers
Qinxun Bai, Steven Rosenberg, Zheng Wu et al.
Differentially Private Chi-Squared Hypothesis Testing: Goodness of Fit and Independence Testing
Marco Gaboardi, Hyun Lim, Ryan Rogers et al.
Differentially Private Policy Evaluation
Borja Balle, Maziar Gomrokchi, Doina Precup
Dirichlet Process Mixture Model for Correcting Technical Variation in Single-Cell Gene Expression Data
Sandhya Prabhakaran, Elham Azizi, Ambrose Carr et al.
Discrete Deep Feature Extraction: A Theory and New Architectures
Thomas Wiatowski, Michael Tschannen, Aleksandar Stanic et al.
Discrete Distribution Estimation under Local Privacy
Peter Kairouz, Keith Bonawitz, Daniel Ramage
Discriminative Embeddings of Latent Variable Models for Structured Data
Hanjun Dai, Bo Dai, Le Song
Distributed Clustering of Linear Bandits in Peer to Peer Networks
Nathan Korda, Balazs Szorenyi, Shuai Li
Diversity-Promoting Bayesian Learning of Latent Variable Models
Pengtao Xie, Jun Zhu, Eric Xing
Domain Adaptation with Conditional Transferable Components
Mingming Gong, Kun Zhang, Tongliang Liu et al.
Doubly Decomposing Nonparametric Tensor Regression
Masaaki Imaizumi, Kohei Hayashi
Doubly Robust Off-policy Value Evaluation for Reinforcement Learning
Nan Jiang, Lihong Li
DR-ABC: Approximate Bayesian Computation with Kernel-Based Distribution Regression
Jovana Mitrovic, Dino Sejdinovic, Yee-Whye Teh
Dropout as a Bayesian Approximation: Representing Model Uncertainty in Deep Learning
Yarin Gal, Zoubin Ghahramani
Dropout distillation
Samuel Rota Bulò, Lorenzo Porzi, Peter Kontschieder
Dueling Network Architectures for Deep Reinforcement Learning
Ziyu Wang, Tom Schaul, Matteo Hessel et al.
Dynamic Capacity Networks
Amjad Almahairi, Nicolas Ballas, Tim Cooijmans et al.
Dynamic Memory Networks for Visual and Textual Question Answering
Caiming Xiong, Stephen Merity, Richard Socher
Early and Reliable Event Detection Using Proximity Space Representation
Maxime Sangnier, Jerome Gauthier, Alain Rakotomamonjy
Efficient Algorithms for Adversarial Contextual Learning
Vasilis Syrgkanis, Akshay Krishnamurthy, Robert Schapire
Efficient Algorithms for Large-scale Generalized Eigenvector Computation and Canonical Correlation Analysis
Rong Ge, Chi Jin, Sham et al.