Papers
173,579 papers found
On the Asymptotic Optimality of Maximum Margin Bayesian Networks
Sebastian Tschiatschek, Franz Pernkopf
On the Complexity and Approximation of Binary Evidence in Lifted Inference
Guy Van den Broeck, Adnan Darwiche
On the Convergence of Maximum Variance Unfolding
Ery Arias-Castro, Bruno Pelletier
On the difficulty of training recurrent neural networks
Razvan Pascanu, Tomas Mikolov, Yoshua Bengio
On the Generalization Ability of Online Learning Algorithms for Pairwise Loss Functions
Purushottam Kar, Bharath Sriperumbudur, Prateek Jain et al.
On the importance of initialization and momentum in deep learning
Ilya Sutskever, James Martens, George Dahl et al.
On the Learnability of Shuffle Ideals
Dana Angluin, James Aspnes, Sarah Eisenstat et al.
On the Linear Convergence of the Proximal Gradient Method for Trace Norm Regularization
Ke Hou, Zirui Zhou, Anthony Man-Cho So et al.
On the Mean Curvature Flow on Graphs with Applications in Image and Manifold Processing
Abdallah El Chakik, Abderrahim Elmoataz, Ahcene Sadi
On the Mutual Nearest Neighbors Estimate in Regression
Arnaud Guyader, Nick Hengartner
On the Relationship Between Binary Classification, Bipartite Ranking, and Binary Class Probability Estimation
Harikrishna Narasimhan, Shivani Agarwal
On the Representational Efficiency of Restricted Boltzmann Machines
James Martens, Arkadev Chattopadhya, Toni Pitassi et al.
On the Sample Complexity of Subspace Learning
Alessandro Rudi, Guillermo D Canas, Lorenzo Rosasco
On the Statistical Consistency of Algorithms for Binary Classification under Class Imbalance
Aditya Menon, Harikrishna Narasimhan, Shivani Agarwal et al.
Open Problem: Adversarial Multiarmed Bandits with Limited Advice
Yevgeny Seldin, Koby Crammer, Peter Bartlett
Open Problem: Lower bounds for Boosting with Hadamard Matrices
Jiazhong Nie, Manfred K. Warmuth, S.V.N. Vishwanathan et al.
Opportunistic Strategies for Generalized No-Regret Problems
Andrey Bernstein, Shie Mannor, Nahum Shimkin
Optical Flow Estimation Using Laplacian Mesh Energy
Wenbin Li, Darren Cosker, Matthew Brown et al.
Optical Flow via Locally Adaptive Fusion of Complementary Data Costs
Tae Hyun Kim, Hee Seok Lee, Kyoung Mu Lee
Optimal Discovery with Probabilistic Expert Advice: Finite Time Analysis and Macroscopic Optimality
Sébastien Bubeck, Damien Ernst, Aurélien Garivier
Optimal Geometric Fitting under the Truncated L2-Norm
Erik Ask, Olof Enqvist, Fredrik Kahl
Optimal integration of visual speed across different spatiotemporal frequency channels
Matjaz Jogan, Alan Stocker
Optimally Fuzzy Temporal Memory
Karthik H. Shankar, Marc W. Howard