Fast Notes: Title: Improving Survey Aggregation with Sparsely Represented Signals Authors: Tianlin Shi, Stanford University Forest ... Title: Evaluating Mobile App Release Authors: Ya Xu*, LinkedIn Corporation Nanyu Chen, LinkedIn Corporation Abstract: We ...
Kdd2016 Paper 945 - Resource Useful Overview
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Resource Useful Overview
Title: Improving the Sensitivity of Online Controlled Experiments: Case Studies at Netflix Authors: Huizhi Xie*, Netflix Juliette ... Title: Deep Crossing: Web-Scale Modeling without Manually Crafted Combinatorial Features Authors: Ying Shan*, Microsoft ...
Information What to Check First
Title: Robust Extreme Multi-label Learning Authors: Chang Xu*, Peking University Dacheng Tao, University of Technology Sydney ... Title: Revisiting Random Binning Feature: Fast Convergence and Strong Parallelizability Authors: Lingfei Wu*, College of William ... Title: Evaluating Mobile App Release Authors: Ya Xu*, LinkedIn Corporation Nanyu Chen, LinkedIn Corporation Abstract: We ...
Information What It Connects To
Title: Evaluating Mobile App Release Authors: Ya Xu*, LinkedIn Corporation Nanyu Chen, LinkedIn Corporation Abstract: We ... Title: CaSMoS: A Framework for Learning Candidate Selection Models over Structured Queries and Documents Authors: Fedor ...
Comparison Points
Title: Ranking Causal Anomalies via Temporal and Dynamical Analysis on Vanishing Correlations Authors: Wei Cheng*, NEC ... Title: Improving Survey Aggregation with Sparsely Represented Signals Authors: Tianlin Shi, Stanford University Forest ... Title: Streaming-LDA: A Copula-based Approach to Modeling Topic Dependencies in Document Streams Authors: Hesam ...
Key points worth scanning
- Title: Robust Extreme Multi-label Learning Authors: Chang Xu*, Peking University Dacheng Tao, University of Technology Sydney ...
- Title: Evaluating Mobile App Release Authors: Ya Xu*, LinkedIn Corporation Nanyu Chen, LinkedIn Corporation Abstract: We ...
- Title: Streaming-LDA: A Copula-based Approach to Modeling Topic Dependencies in Document Streams Authors: Hesam ...
- Title: CaSMoS: A Framework for Learning Candidate Selection Models over Structured Queries and Documents Authors: Fedor ...
- Title: Revisiting Random Binning Feature: Fast Convergence and Strong Parallelizability Authors: Lingfei Wu*, College of William ...
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