Papers
176,624 papers found
Latent Variable Models for Predicting File Dependencies in Large-Scale Software Development
Diane Hu, Laurens Maaten, Youngmin Cho et al.
Layered image motion with explicit occlusions, temporal consistency, and depth ordering
Deqing Sun, Erik B. Sudderth, Michael J. Black
Layer-wise analysis of deep networks with Gaussian kernels
Grégoire Montavon, Klaus-Robert Müller, Mikio L. Braun
Learnability, Stability and Uniform Convergence
Shai Shalev-Shwartz, Ohad Shamir, Nathan Srebro et al.
Learning Bayesian Network Structure using LP Relaxations
Tommi Jaakkola, David Sontag, Amir Globerson et al.
Learning Bounds for Importance Weighting
Corinna Cortes, Yishay Mansour, Mehryar Mohri
Learning Causal Structure from Overlapping Variable Sets
Sofia Triantafillou, Ioannis Tsamardinos, Ioannis Tollis
Learning concept graphs from text with stick-breaking priors
America Chambers, Padhraic Smyth, Mark Steyvers
Learning Convolutional Feature Hierarchies for Visual Recognition
Koray Kavukcuoglu, Pierre Sermanet, Y-lan Boureau et al.
Learning Efficient Markov Networks
Vibhav Gogate, William Webb, Pedro Domingos
Learning Exponential Families in High-Dimensions: Strong Convexity and Sparsity
Sham Kakade, Ohad Shamir, Karthik Sindharan et al.
Learning from Candidate Labeling Sets
Jie Luo, Francesco Orabona
Learning From Crowds
Vikas C. Raykar, Shipeng Yu, Linda H. Zhao et al.
Learning from Logged Implicit Exploration Data
Alex Strehl, John Langford, Lihong Li et al.
Learning Gradients: Predictive Models that Infer Geometry and Statistical Dependence
Qiang Wu, Justin Guinney, Mauro Maggioni et al.
Learning Instance-Specific Predictive Models
Shyam Visweswaran, Gregory F. Cooper
Learning invariant features using the Transformed Indian Buffet Process
Joseph L. Austerweil, Thomas L. Griffiths
Learning Kernels with Radiuses of Minimum Enclosing Balls
Kun Gai, Guangyun Chen, Chang-shui Zhang
Learning Multiple Tasks using Manifold Regularization
Arvind Agarwal, Samuel Gerber, Hal Daume
Learning Multiple Tasks with a Sparse Matrix-Normal Penalty
Yi Zhang, Jeff G. Schneider
Learning Networks of Stochastic Differential Equations
José Pereira, Morteza Ibrahimi, Andrea Montanari
Learning Nonlinear Dynamic Models from Non-sequenced Data
Tzu–Kuo Huang, Le Song, Jeff Schneider
Learning Non-Stationary Dynamic Bayesian Networks
Joshua W. Robinson, Alexander J. Hartemink
Learning Policy Improvements with Path Integrals
Evangelos Theodorou, Jonas Buchli, Stefan Schaal
Learning Polyhedral Classifiers Using Logistic Function
Naresh Manwani, P. S. Sastry