Papers
DeltaGrad: Rapid retraining of machine learning models
Yinjun Wu, Edgar Dobriban, Susan Davidson
Description Based Text Classification with Reinforcement Learning
Duo Chai, Wei Wu, Qinghong Han et al.
DessiLBI: Exploring Structural Sparsity of Deep Networks via Differential Inclusion Paths
Yanwei Fu, Chen Liu, Donghao Li et al.
Detecting Out-of-Distribution Examples with Gram Matrices
Chandramouli Shama Sastry, Sageev Oore
Differentiable Likelihoods for Fast Inversion of ’Likelihood-Free’ Dynamical Systems
Hans Kersting, Nicholas Krämer, Martin Schiegg et al.
Differentiable Product Quantization for End-to-End Embedding Compression
Ting Chen, Lala Li, Yizhou Sun
Differentially Private Set Union
Sivakanth Gopi, Pankaj Gulhane, Janardhan Kulkarni et al.
Differentiating through the Fréchet Mean
Aaron Lou, Isay Katsman, Qingxuan Jiang et al.
DINO: Distributed Newton-Type Optimization Method
Rixon Crane, Fred Roosta
Discount Factor as a Regularizer in Reinforcement Learning
Ron Amit, Ron Meir, Kamil Ciosek
Discriminative Adversarial Search for Abstractive Summarization
Thomas Scialom, Paul-Alexis Dray, Sylvain Lamprier et al.
Discriminative Jackknife: Quantifying Uncertainty in Deep Learning via Higher-Order Influence Functions
Ahmed Alaa, Mihaela Van Der Schaar
Disentangling Trainability and Generalization in Deep Neural Networks
Lechao Xiao, Jeffrey Pennington, Samuel Schoenholz
Dispersed Exponential Family Mixture VAEs for Interpretable Text Generation
Wenxian Shi, Hao Zhou, Ning Miao et al.
Distance Metric Learning with Joint Representation Diversification
Xu Chu, Yang Lin, Yasha Wang et al.
Distinguishing Cause from Effect Using Quantiles: Bivariate Quantile Causal Discovery
Natasa Tagasovska, Valérie Chavez-Demoulin, Thibault Vatter
Distributed Online Optimization over a Heterogeneous Network with Any-Batch Mirror Descent
Nima Eshraghi, Ben Liang
Distributionally Robust Policy Evaluation and Learning in Offline Contextual Bandits
Nian Si, Fan Zhang, Zhengyuan Zhou et al.
Distribution Augmentation for Generative Modeling
Heewoo Jun, Rewon Child, Mark Chen et al.
Divide and Conquer: Leveraging Intermediate Feature Representations for Quantized Training of Neural Networks
Ahmed Taha Elthakeb, Prannoy Pilligundla, Fatemeh Mireshghallah et al.
Divide, Conquer, and Combine: a New Inference Strategy for Probabilistic Programs with Stochastic Support
Yuan Zhou, Hongseok Yang, Yee Whye Teh et al.
Does label smoothing mitigate label noise?
Michal Lukasik, Srinadh Bhojanapalli, Aditya Menon et al.
Does the Markov Decision Process Fit the Data: Testing for the Markov Property in Sequential Decision Making
Chengchun Shi, Runzhe Wan, Rui Song et al.