Papers
Toward Controlled Generation of Text
Zhiting Hu, Zichao Yang, Xiaodan Liang et al.
Toward Efficient and Accurate Covariance Matrix Estimation on Compressed Data
Xixian Chen, Michael R. Lyu, Irwin King
Towards K-means-friendly Spaces: Simultaneous Deep Learning and Clustering
Bo Yang, Xiao Fu, Nicholas D. Sidiropoulos et al.
Tunable Efficient Unitary Neural Networks (EUNN) and their application to RNNs
Li Jing, Yichen Shen, Tena Dubcek et al.
Uncertainty Assessment and False Discovery Rate Control in High-Dimensional Granger Causal Inference
Aditya Chaudhry, Pan Xu, Quanquan Gu
Uncorrelation and Evenness: a New Diversity-Promoting Regularizer
Pengtao Xie, Aarti Singh, Eric P. Xing
Uncovering Causality from Multivariate Hawkes Integrated Cumulants
Massil Achab, Emmanuel Bacry, Stéphane Gaı̈ffas et al.
Understanding Black-box Predictions via Influence Functions
Pang Wei Koh, Percy Liang
Understanding Synthetic Gradients and Decoupled Neural Interfaces
Wojciech Marian Czarnecki, Grzegorz Świrszcz, Max Jaderberg et al.
Understanding the Representation and Computation of Multilayer Perceptrons: A Case Study in Speech Recognition
Tasha Nagamine, Nima Mesgarani
Uniform Convergence Rates for Kernel Density Estimation
Heinrich Jiang
Uniform Deviation Bounds for k-Means Clustering
Olivier Bachem, Mario Lucic, S. Hamed Hassani et al.
Unimodal Probability Distributions for Deep Ordinal Classification
Christopher Beckham, Christopher Pal
Unsupervised Learning by Predicting Noise
Piotr Bojanowski, Armand Joulin
Variants of RMSProp and Adagrad with Logarithmic Regret Bounds
Mahesh Chandra Mukkamala, Matthias Hein
Variational Boosting: Iteratively Refining Posterior Approximations
Andrew C. Miller, Nicholas J. Foti, Ryan P. Adams
Variational Dropout Sparsifies Deep Neural Networks
Dmitry Molchanov, Arsenii Ashukha, Dmitry Vetrov
Variational Inference for Sparse and Undirected Models
John Ingraham, Debora Marks
Variational Policy for Guiding Point Processes
Yichen Wang, Grady Williams, Evangelos Theodorou et al.
Video Pixel Networks
Nal Kalchbrenner, Aäron Oord, Karen Simonyan et al.
Warped Convolutions: Efficient Invariance to Spatial Transformations
João F. Henriques, Andrea Vedaldi
Wasserstein Generative Adversarial Networks
Martin Arjovsky, Soumith Chintala, Léon Bottou
When can Multi-Site Datasets be Pooled for Regression? Hypothesis Tests, $\ell_2$-consistency and Neuroscience Applications
Hao Henry Zhou, Yilin Zhang, Vamsi K. Ithapu et al.
Why is Posterior Sampling Better than Optimism for Reinforcement Learning?
Ian Osband, Benjamin Van Roy