Papers
What to Expect of Classifiers? Reasoning about Logistic Regression with Missing Features
Pasha Khosravi, Yitao Liang, YooJung Choi et al.
What Would You Expect? Anticipating Egocentric Actions With Rolling-Unrolling LSTMs and Modality Attention
Antonino Furnari, Giovanni Maria Farinella
What You See is What You Get: Visual Pronoun Coreference Resolution in Dialogues
Xintong Yu, Hongming Zhang, Yangqiu Song et al.
What You See is What You Get: Visual Pronoun Coreference Resolution in Dialogues
Xintong Yu, Hongming Zhang, Yangqiu Song et al.
When a Good Translation is Wrong in Context: Context-Aware Machine Translation Improves on Deixis, Ellipsis, and Lexical Cohesion
Elena Voita, Rico Sennrich, Ivan Titov
When and Why is Document-level Context Useful in Neural Machine Translation?
Yunsu Kim, Duc Thanh Tran, Hermann Ney
When can unlabeled data improve the learning rate?
Christina Göpfert, Shai Ben-David, Olivier Bousquet et al.
When Choosing Plausible Alternatives, Clever Hans can be Clever
Pride Kavumba, Naoya Inoue, Benjamin Heinzerling et al.
When Color Constancy Goes Wrong: Correcting Improperly White-Balanced Images
Mahmoud Afifi, Brian Price, Scott Cohen et al.
When Do Envy-Free Allocations Exist?
Pasin Manurangsi, Warut Suksompong
When does label smoothing help?
Rafael Müller, Simon Kornblith, Geoffrey E. Hinton
“When Numbers Matter!”: Detecting Sarcasm in Numerical Portions of Text
Abhijeet Dubey, Lakshya Kumar, Arpan Somani et al.
When Samples Are Strategically Selected
Hanrui Zhang, Yu Cheng, Vincent Conitzer
When Specialization Helps: Using Pooled Contextualized Embeddings to Detect Chemical and Biomedical Entities in Spanish
Manuel Stoeckel, Wahed Hemati, Alexander Mehler
When to Trust Your Model: Model-Based Policy Optimization
Michael Janner, Justin Fu, Marvin Zhang et al.
When to use parametric models in reinforcement learning?
Hado P van Hasselt, Matteo Hessel, John Aslanides
Where Is My Mirror?
Xin Yang, Haiyang Mei, Ke Xu et al.
Where's Wally Now? Deep Generative and Discriminative Embeddings for Novelty Detection
Philippe Burlina, Neil Joshi, I-Jeng Wang
Where to Go Next: A Spatio-Temporal Gated Network for Next POI Recommendation
Pengpeng Zhao, Haifeng Zhu, Yanchi Liu et al.
Whether to Pretrain DNN or not?: An Empirical Analysis for Voice Conversion
Nirmesh J. Shah, Hardik B. Sailor, Hemant A. Patil
Which Algorithmic Choices Matter at Which Batch Sizes? Insights From a Noisy Quadratic Model
Guodong Zhang, Lala Li, Zachary Nado et al.
Which Factorization Machine Modeling Is Better: A Theoretical Answer with Optimal Guarantee
Ming Lin, Shuang Qiu, Jieping Ye et al.