Papers
660 papers found
MAP Estimation for Graphical Models by Likelihood Maximization
Akshat Kumar, Shlomo Zilberstein
(RF)^2 -- Random Forest Random Field
Nadia Payet, Sinisa Todorovic
Structure Learning for Optimization
Shulin Yang, Ali Rahimi
Learning Eigenvectors for Free
Wouter M. Koolen, Wojciech Kotlowski, Manfred K. Warmuth
Co-Training for Domain Adaptation
Minmin Chen, Kilian Q. Weinberger, John Blitzer
Structured Learning for Cell Tracking
Xinghua Lou, Fred A. Hamprecht
Confidence Sets for Network Structure
David S. Choi, Patrick J. Wolfe, Edoardo M. Airoldi
Pylon Model for Semantic Segmentation
Victor Lempitsky, Andrea Vedaldi, Andrew Zisserman
Ancestor Sampling for Particle Gibbs
Fredrik Lindsten, Thomas Schön, Michael I. Jordan
Majorization for CRFs and Latent Likelihoods
Tony Jebara, Anna Choromanska
Projection Retrieval for Classification
Madalina Fiterau, Artur Dubrawski
Online PCA for Contaminated Data
Jiashi Feng, Huan Xu, Shie Mannor et al.
Mixed Optimization for Smooth Functions
Mehrdad Mahdavi, Lijun Zhang, Rong Jin
Deep Neural Networks for Object Detection
Christian Szegedy, Alexander Toshev, Dumitru Erhan
A statistical model for tensor PCA
Emile Richard, Andrea Montanari
The Pareto Regret Frontier for Bandits
Tor Lattimore
Optimal Rates for Random Fourier Features
Bharath Sriperumbudur, Zoltan Szabo
Decomposition Bounds for Marginal MAP
Wei Ping, Qiang Liu, Alex Ihler
Learning Bound for Parameter Transfer Learning
Wataru Kumagai
Improved Techniques for Training GANs
Tim Salimans, Ian Goodfellow, Wojciech Zaremba et al.
A Multi-Batch L-BFGS Method for Machine Learning
Albert S Berahas, Jorge Nocedal, Martin Takac
Review Networks for Caption Generation
Zhilin Yang, Ye Yuan, Yuexin Wu et al.
Sampling for Bayesian Program Learning
Kevin Ellis, Armando Solar-Lezama, Josh Tenenbaum
Fast and Provably Good Seedings for k-Means
Olivier Bachem, Mario Lucic, Hamed Hassani et al.
The Forget-me-not Process
Kieran Milan, Joel Veness, James Kirkpatrick et al.