Papers
Discovering and Removing Exogenous State Variables and Rewards for Reinforcement Learning
Thomas Dietterich, George Trimponias, Zhitang Chen
Discovering Interpretable Representations for Both Deep Generative and Discriminative Models
Tameem Adel, Zoubin Ghahramani, Adrian Weller
Discrete-Continuous Mixtures in Probabilistic Programming: Generalized Semantics and Inference Algorithms
Yi Wu, Siddharth Srivastava, Nicholas Hay et al.
Disentangled Sequential Autoencoder
Li Yingzhen, Stephan Mandt
Disentangling by Factorising
Hyunjik Kim, Andriy Mnih
Dissecting Adam: The Sign, Magnitude and Variance of Stochastic Gradients
Lukas Balles, Philipp Hennig
Dissipativity Theory for Accelerating Stochastic Variance Reduction: A Unified Analysis of SVRG and Katyusha Using Semidefinite Programs
Bin Hu, Stephen Wright, Laurent Lessard
Distributed Asynchronous Optimization with Unbounded Delays: How Slow Can You Go?
Zhengyuan Zhou, Panayotis Mertikopoulos, Nicholas Bambos et al.
Distributed Clustering via LSH Based Data Partitioning
Aditya Bhaskara, Maheshakya Wijewardena
Distributed Nonparametric Regression under Communication Constraints
Yuancheng Zhu, John Lafferty
Does Distributionally Robust Supervised Learning Give Robust Classifiers?
Weihua Hu, Gang Niu, Issei Sato et al.
Do Outliers Ruin Collaboration?
Mingda Qiao
DRACO: Byzantine-resilient Distributed Training via Redundant Gradients
Lingjiao Chen, Hongyi Wang, Zachary Charles et al.
Dropout Training, Data-dependent Regularization, and Generalization Bounds
Wenlong Mou, Yuchen Zhou, Jun Gao et al.
DVAE++: Discrete Variational Autoencoders with Overlapping Transformations
Arash Vahdat, William Macready, Zhengbing Bian et al.
Dynamical Isometry and a Mean Field Theory of CNNs: How to Train 10,000-Layer Vanilla Convolutional Neural Networks
Lechao Xiao, Yasaman Bahri, Jascha Sohl-Dickstein et al.
Dynamical Isometry and a Mean Field Theory of RNNs: Gating Enables Signal Propagation in Recurrent Neural Networks
Minmin Chen, Jeffrey Pennington, Samuel Schoenholz
Dynamic Evaluation of Neural Sequence Models
Ben Krause, Emmanuel Kahembwe, Iain Murray et al.
Dynamic Regret of Strongly Adaptive Methods
Lijun Zhang, Tianbao Yang, jin et al.
Efficient and Consistent Adversarial Bipartite Matching
Rizal Fathony, Sima Behpour, Xinhua Zhang et al.
Efficient Bias-Span-Constrained Exploration-Exploitation in Reinforcement Learning
Ronan Fruit, Matteo Pirotta, Alessandro Lazaric et al.
Efficient end-to-end learning for quantizable representations
Yeonwoo Jeong, Hyun Oh Song
Efficient First-Order Algorithms for Adaptive Signal Denoising
Dmitrii Ostrovskii, Zaid Harchaoui
Efficient Gradient-Free Variational Inference using Policy Search
Oleg Arenz, Gerhard Neumann, Mingjun Zhong
Efficient Model-Based Deep Reinforcement Learning with Variational State Tabulation
Dane Corneil, Wulfram Gerstner, Johanni Brea