Papers
Learning the Multilinear Structure of Visual Data
Mengjiao Wang, Yannis Panagakis, Patrick Snape et al.
Learning the Network Structure of Heterogeneous Data via Pairwise Exponential Markov Random Fields
Youngsuk Park, David Hallac, Stephen Boyd et al.
Learning-Theoretic Foundations of Algorithm Configuration for Combinatorial Partitioning Problems
Maria-Florina Balcan, Vaishnavh Nagarajan, Ellen Vitercik et al.
Learning Theory for Conditional Risk Minimization
Alexander Zimin, Christoph Lampert
Learning Theory of Distributed Regression with Bias Corrected Regularization Kernel Network
Zheng-Chu Guo, Lei Shi, Qiang Wu
Learning the Structure of Generative Models without Labeled Data
Stephen H. Bach, Bryan He, Alexander Ratner et al.
Learning the Structure of Variable-Order CRFs: a finite-state perspective
Thomas Lavergne, François Yvon
Learning Time Series Detection Models from Temporally Imprecise Labels
Roy Adams, Ben Marlin
Learning to Aggregate Ordinal Labels by Maximizing Separating Width
Guangyong Chen, Shengyu Zhang, Di Lin et al.
Learning to Align Semantic Segmentation and 2.5D Maps for Geolocalization
Anil Armagan, Martin Hirzer, Peter M. Roth et al.
Learning to Align the Source Code to the Compiled Object Code
Dor Levy, Lior Wolf
Learning to Ask: Neural Question Generation for Reading Comprehension
Xinya Du, Junru Shao, Claire Cardie
Learning to Compose Domain-Specific Transformations for Data Augmentation
Alexander J Ratner, Henry Ehrenberg, Zeshan Hussain et al.
Learning to Create and Reuse Words in Open-Vocabulary Neural Language Modeling
Kazuya Kawakami, Chris Dyer, Phil Blunsom
Learning to Detect Salient Objects With Image-Level Supervision
Lijun Wang, Huchuan Lu, Yifan Wang et al.
Learning to Detect Sepsis with a Multitask Gaussian Process RNN Classifier
Joseph Futoma, Sanjay Hariharan, Katherine Heller
Learning to Diagnose: Assimilating Clinical Narratives using Deep Reinforcement Learning
Yuan Ling, Sadid A. Hasan, Vivek Datla et al.
Learning to Discover Cross-Domain Relations with Generative Adversarial Networks
Taeksoo Kim, Moonsu Cha, Hyunsoo Kim et al.
Learning to Discover Sparse Graphical Models
Eugene Belilovsky, Kyle Kastner, Gael Varoquaux et al.
Learning to Explain Entity Relationships by Pairwise Ranking with Convolutional Neural Networks
Jizhou Huang, Wei Zhang, Shiqi Zhao et al.
Learning to Explain Non-Standard English Words and Phrases
Ke Ni, William Yang Wang
Learning to Extract Semantic Structure From Documents Using Multimodal Fully Convolutional Neural Networks
Xiao Yang, Ersin Yumer, Paul Asente et al.
Learning to Generate Long-term Future via Hierarchical Prediction
Ruben Villegas, Jimei Yang, Yuliang Zou et al.
Learning to Generate Market Comments from Stock Prices
Soichiro Murakami, Akihiko Watanabe, Akira Miyazawa et al.
Learning to generate one-sentence biographies from Wikidata
Andrew Chisholm, Will Radford, Ben Hachey