Papers
Approximate message passing for amplitude based optimization
Junjie Ma, Ji Xu, Arian Maleki
Approximate Nearest Neighbors in Limited Space
Piotr Indyk, Tal Wagner
Approximate Ranking from Pairwise Comparisons
Reinhard Heckel, Max Simchowitz, Kannan Ramchandran et al.
Approximating Fair Queueing on Reconfigurable Switches
Naveen Kr. Sharma, Ming Liu, Kishore Atreya et al.
Approximating Real-Time Recurrent Learning with Random Kronecker Factors
Asier Mujika, Florian Meier, Angelika Steger
Approximating the maximum weighted decomposable graph problem with applications to probabilistic graphical models
Aritz Pérez, Christian Blum, Jose A. Lozano
Approximating Word Ranking and Negative Sampling for Word Embedding
Guibing Guo, Shichang Ouyang, Fajie Yuan et al.
Approximation Algorithms for Cascading Prediction Models
Matthew Streeter
Approximation algorithms for stochastic clustering
David Harris, Shi Li, Aravind Srinivasan et al.
Approximation Guarantees for Adaptive Sampling
Eric Balkanski, Yaron Singer
Approximation Guarantees of Stochastic Greedy Algorithms for Subset Selection
Chao Qian, Yang Yu, Ke Tang
A Practical Algorithm for Distributed Clustering and Outlier Detection
Jiecao Chen, Erfan Sadeqi Azer, Qin Zhang
A Practical Incremental Learning Framework For Sparse Entity Extraction
Hussein Al-Olimat, Steven Gustafson, Jason Mackay et al.
A Preliminary Study on Tonal Coarticulation in Continuous Speech
Lixia Hao, Wei Zhang, Yanlu Xie et al.
APRIL: Interactively Learning to Summarise by Combining Active Preference Learning and Reinforcement Learning
Yang Gao, Christian M. Meyer, Iryna Gurevych
A Primal-Dual Analysis of Global Optimality in Nonconvex Low-Rank Matrix Recovery
Xiao Zhang, Lingxiao Wang, Yaodong Yu et al.
A Priori SNR Estimation Based on a Recurrent Neural Network for Robust Speech Enhancement
Yangyang Xia, Richard Stern
A Prior-Less Method for Multi-Face Tracking in Unconstrained Videos
Chung-Ching Lin, Ying Hung
A Probabilistic Annotation Model for Crowdsourcing Coreference
Silviu Paun, Jon Chamberlain, Udo Kruschwitz et al.
A probabilistic framework for multi-view feature learning with many-to-many associations via neural networks
Akifumi Okuno, Tetsuya Hada, Hidetoshi Shimodaira
A Probabilistic Model for Joint Learning of Word Embeddings from Texts and Images
Melissa Ailem, Bowen Zhang, Aurelien Bellet et al.
A probabilistic population code based on neural samples
Sabyasachi Shivkumar, Richard Lange, Ankani Chattoraj et al.