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DCN-M: Improved Deep & Cross Network for Feature Cross Learning in Web-scale Learning to Rank Systems.

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DCN V2: Improved Deep & Cross Network and Practical Lessons for Web-scale Learning to Rank Systems., , , , , , and . WWW, page 1785-1797. ACM / IW3C2, (2021)Understanding and Improving Knowledge Distillation., , , , , , and . CoRR, (2020)How Many Pairwise Preferences Do We Need to Rank A Graph Consistently?, , and . CoRR, (2018)Algorithms for Efficiently Learning Low-Rank Neural Networks., , , , and . CoRR, (2022)Learning on graphs using Orthonormal Representation is Statistically Consistent., and . NIPS, page 3635-3643. (2014)DCN-M: Improved Deep & Cross Network for Feature Cross Learning in Web-scale Learning to Rank Systems., , , , , , and . CoRR, (2020)How Many Pairwise Preferences Do We Need to Rank a Graph Consistently?, , and . AAAI, page 4830-4837. AAAI Press, (2019)On the Factory Floor: ML Engineering for Industrial-Scale Ads Recommendation Models., , , , , , , , , and 2 other author(s). ORSUM@RecSys, volume 3303 of CEUR Workshop Proceedings, CEUR-WS.org, (2022)Spectral Norm Regularization of Orthonormal Representations for Graph Transduction., , , , and . NIPS, page 2215-2223. (2015)