NIPS 2013 reading list

NIPS的论文，光是看标题就觉得高大深，先粗粗地列个reading list，希望以后再慢慢整理。

 

• A simple example of Dirichlet process mixture inconsistency for the number of components Jeffrey W. Miller, Matthew T. Harrison
• Accelerated Mini-Batch Stochastic Dual Coordinate Ascent Shai Shalev-Shwartz, Tong Zhang
• Accelerating Stochastic Gradient Descent using Predictive Variance Reduction Rie Johnson, Tong Zhang 
• Approximate Bayesian Image Interpretation using Generative Probabilistic Graphics Programs Vikash Mansinghka, Tejas D. Kulkarni, Yura N. Perov, Josh Tenenbaum
• Bayesian Estimation of Latently-grouped Parameters in Undirected Graphical Models Jie Liu, David Page
• Bayesian Hierarchical Community Discovery Charles Blundell, Yee Whye Teh
• Bayesian optimization explains human active search Ali Borji, Laurent Itti
• Better Approximation and Faster Algorithm Using the Proximal Average Yao-Liang Yu
• Compressive Feature Learning Hristo S. Paskov, Robert West, John C. Mitchell, Trevor Hastie
• Conditional Random Fields via Univariate Exponential Families Eunho Yang, Pradeep Ravikumar, Genevera I. Allen, Zhandong Liu
• Contrastive Learning Using Spectral Methods James Y. Zou, Daniel Hsu, David C. Parkes, Ryan P. Adams
• Curvature and Optimal Algorithms for Learning and Minimizing Submodular Functions Rishabh K. Iyer, Stefanie Jegelka, Jeff A. Bilmes
• Correlated random features for fast semi-supervised learning Brian McWilliams, David Balduzzi, Joachim Buhmann
• DeViSE: A Deep Visual-Semantic Embedding Model Andrea Frome, Greg S. Corrado, Jon Shlens, Samy Bengio, Jeff Dean, Marc'Aurelio Ranzato, Tomas Mikolov
• Deep Fisher Networks for Large-Scale Image Classification Karen Simonyan, Andrea Vedaldi, Andrew Zisserman

• Efficient Optimization for Sparse Gaussian Process Regression Yanshuai Cao, Marcus A. Brubaker, David Fleet, Aaron Hertzmann

• Extracting regions of interest from biological images with convolutional sparse block coding Marius Pachitariu, Adam M. Packer, Noah Pettit, Henry Dalgleish, Michael Hausser, Maneesh Sahani

• Higher Order Priors for Joint Intrinsic Image, Objects, and Attributes Estimation Vibhav Vineet, Carsten Rother, Philip Torr

• Latent Maximum Margin Clustering Guang-Tong Zhou, Tian Lan, Arash Vahdat, Greg Mori

• Learning Multi-level Sparse Representations Ferran Diego Andilla, Fred A. Hamprecht

• Learning Multiple Models via Regularized Weighting Daniel Vainsencher, Shie Mannor, Huan Xu

• Learning a Deep Compact Image Representation for Visual Tracking Naiyan Wang, Dit-Yan Yeun
• Machine Teaching for Bayesian Learners in the Exponential Family Xiaojin Zhu

• Manifold-based Similarity Adaptation for Label Propagation Masayuki Karasuyama, Hiroshi Mamitsuka

• Mid-level Visual Element Discovery as Discriminative Mode Seeking Carl Doersch, Abhinav Gupta, Alexei A. Efros

• Multiscale Dictionary Learning for Estimating Conditional Distributions Francesca Petralia, Joshua T. Vogelstein, David Dunson
• On Poisson Graphical Models Eunho Yang, Pradeep Ravikumar, Genevera I. Allen, Zhandong Liu

• Similarity Component Analysis Soravit Changpinyo, Kuan Liu, Fei Sha

 

Ali只有一篇论文，好开心。以后得看点高大上的论文咯。