Query conditions: topic=machine-learning, and publish_at in 2018
Daniel Cer, Yinfei Yang, Sheng-yi Kong, Nan Hua, Nicole Limtiaco, Rhomni St. John, Noah Constant, Mario Guajardo-Cespedes, Steve Yuan, Chris Tar, Yun-Hsuan Sung, Brian Strope, Ray Kurzweil · arxiv.org
Bryan McCann, James Bradbury, Caiming Xiong, Richard Socher · arxiv.org
Marjan Ghazvininejad, Chris Brockett, Ming-Wei Chang, Bill Dolan, Jianfeng Gao, Wen-tau Yih, Michel Galley · arxiv.org
Sanjeev Arora, Mikhail Khodak, Nikunj Saunshi, Kiran Vodrahalli · openreview.net
Andreas Rücklé, Steffen Eger, Maxime Peyrard, Iryna Gurevych · arxiv.org
Jinman Zhao, Sidharth Mudgal, Yingyu Liang · arxiv.org
Lajanugen Logeswaran, Honglak Lee · arxiv.org
Jun-Yan Zhu, Philipp Krähenbühl, Eli Shechtman, Alexei A. Efros · arxiv.org
Christian S. Perone, Roberto Silveira, Thomas S. Paula · arxiv.org
Guillaume Lample, Alexis Conneau, Ludovic Denoyer, Marc'Aurelio Ranzato · arxiv.org
Matthew E. Peters, Mark Neumann, Mohit Iyyer, Matt Gardner, Christopher Clark, Kenton Lee, Luke Zettlemoyer · arxiv.org
Prathusha K Sarma, YIngyu Liang, William A Sethares · arxiv.org
This page ranks Machine Learning content by topic match, content-type filter, checkout momentum, and freshness. The ranking is recalculated as new items and engagement signals arrive, so the top results stay practical for current workflows instead of remaining static or purely chronological.
The time suffix in this URL defines the publish-date window used for ranking. Year paths include items in that year, and YYYYMM paths include one calendar month. This makes comparisons cleaner when you want a focused snapshot rather than an all-time aggregate.
No. This ranking is editorial and signal-driven, not sponsored placement. Attendemia evaluates public metadata, source context, and usage momentum to rank candidates. Payment does not buy position, so readers can interpret the list as a curation surface rather than advertising inventory.