Papers
Madly Ambiguous: A Game for Learning about Structural Ambiguity and Why It’s Hard for Computers
Ajda Gokcen, Ethan Hill, Michael White
Maelstrom: Mitigating Datacenter-level Disasters by Draining Interdependent Traffic Safely and Efficiently
Kaushik Veeraraghavan, Justin Meza, Scott Michelson et al.
MAGAN: Aligning Biological Manifolds
Matthew Amodio, Smita Krishnaswamy
Magnitude: A Fast, Efficient Universal Vector Embedding Utility Package
Ajay Patel, Alexander Sands, Chris Callison-Burch et al.
Maintenance of Case Bases: Current Algorithms after Fifty Years
Jose M. Juarez, Susan Craw, J. Ricardo Lopez-Delgado et al.
MAJE Submission to the WMT2018 Shared Task on Parallel Corpus Filtering
Marina Fomicheva, Jesús González-Rubio
Make Evasion Harder: An Intelligent Android Malware Detection System
Shifu Hou, Yanfang Ye, Yangqiu Song et al.
Make the Minority Great Again: First-Order Regret Bound for Contextual Bandits
Zeyuan Allen-Zhu, Sebastien Bubeck, Yuanzhi Li
Making Better Use of the Crowd: How Crowdsourcing Can Advance Machine Learning Research
Jennifer Wortman Vaughan
Making Classifier Chains Resilient to Class Imbalance
Bin Liu, Grigorios Tsoumakas
Making Continuous Time Bayesian Networks More Flexible
Manxia Liu, Fabio Stella, Arjen Hommersom et al.
Making Convolutional Networks Recurrent for Visual Sequence Learning
Xiaodong Yang, Pavlo Molchanov, Jan Kautz
Making “fetch” happen: The influence of social and linguistic context on nonstandard word growth and decline
Ian Stewart, Jacob Eisenstein
Making Tree Ensembles Interpretable: A Bayesian Model Selection Approach
Satoshi Hara, Kohei Hayashi
Mallows Models for Top-k Lists
Flavio Chierichetti, Anirban Dasgupta, Shahrzad Haddadan et al.
Managing Communication Costs under Temporal Uncertainty
Nikhil Bhargava, Christian Muise, Tiago Vaquero et al.
Mandarin-English Code-switching Speech Recognition
Haihua Xu, Van Tung Pham, Zin Tun Kyaw et al.
Manifold Learning in Quotient Spaces
Éloi Mehr, André Lieutier, Fernando Sanchez Bermudez et al.
Manifold Structured Prediction
Alessandro Rudi, Carlo Ciliberto, GianMaria Marconi et al.
Manifold-tiling Localized Receptive Fields are Optimal in Similarity-preserving Neural Networks
Anirvan Sengupta, Cengiz Pehlevan, Mariano Tepper et al.
Many Paths to Equilibrium: GANs Do Not Need to Decrease a Divergence At Every Step
William Fedus*, Mihaela Rosca*, Balaji Lakshminarayanan et al.
MapNet: An Allocentric Spatial Memory for Mapping Environments
João F. Henriques, Andrea Vedaldi
Mapping Images to Scene Graphs with Permutation-Invariant Structured Prediction
Roei Herzig, Moshiko Raboh, Gal Chechik et al.
Mapping Instructions to Actions in 3D Environments with Visual Goal Prediction
Dipendra Misra, Andrew Bennett, Valts Blukis et al.
Mapping Language to Code in Programmatic Context
Srinivasan Iyer, Ioannis Konstas, Alvin Cheung et al.