Papers
Scalable Kernel Methods via Doubly Stochastic Gradients
Bo Dai, Bo Xie, Niao He et al.
Scalable Methods for Nonnegative Matrix Factorizations of Near-separable Tall-and-skinny Matrices
Austin R Benson, Jason Lee, Bartek Rajwa et al.
Scalable Non-linear Learning with Adaptive Polynomial Expansions
Alekh Agarwal, Alina Beygelzimer, Daniel J. Hsu et al.
Scale Adaptive Blind Deblurring
Haichao Zhang, Jianchao Yang
Scaling-up Importance Sampling for Markov Logic Networks
Deepak Venugopal, Vibhav G Gogate
Searching for Higgs Boson Decay Modes with Deep Learning
Peter J Sadowski, Daniel Whiteson, Pierre Baldi
Self-Adaptable Templates for Feature Coding
Xavier Boix, Gemma Roig, Salomon Diether et al.
Self-Paced Learning with Diversity
Lu Jiang, Deyu Meng, Shoou-I Yu et al.
Semi-Separable Hamiltonian Monte Carlo for Inference in Bayesian Hierarchical Models
Yichuan Zhang, Charles Sutton
Semi-supervised Learning with Deep Generative Models
Diederik P. Kingma, Shakir Mohamed, Danilo Jimenez Rezende et al.
Sensory Integration and Density Estimation
Joseph G Makin, Philip N. Sabes
Sequence to Sequence Learning with Neural Networks
Ilya Sutskever, Oriol Vinyals, Quoc V Le
Sequential Monte Carlo for Graphical Models
Christian Andersson Naesseth, Fredrik Lindsten, Thomas B Schön
SerialRank: Spectral Ranking using Seriation
Fajwel Fogel, Alexandre d'Aspremont, Milan Vojnovic
Shape and Illumination from Shading using the Generic Viewpoint Assumption
Daniel Zoran, Dilip Krishnan, José Bento et al.
Shaping Social Activity by Incentivizing Users
Mehrdad Farajtabar, Nan Du, Manuel Gomez Rodriguez et al.
Signal Aggregate Constraints in Additive Factorial HMMs, with Application to Energy Disaggregation
Mingjun Zhong, Nigel Goddard, Charles Sutton
Simple MAP Inference via Low-Rank Relaxations
Roy Frostig, Sida Wang, Percy Liang et al.
Smoothed Gradients for Stochastic Variational Inference
Stephan Mandt, David Blei
Sparse Bayesian structure learning with “dependent relevance determination” priors
Anqi Wu, Mijung Park, Oluwasanmi O Koyejo et al.
Sparse Multi-Task Reinforcement Learning
Daniele Calandriello, Alessandro Lazaric, Marcello Restelli
Sparse PCA via Covariance Thresholding
Yash Deshpande, Andrea Montanari
Sparse PCA with Oracle Property
Quanquan Gu, Zhaoran Wang, Han Liu
Sparse Polynomial Learning and Graph Sketching
Murat Kocaoglu, Karthikeyan Shanmugam, Alexandros G Dimakis et al.