Outline ①Introduction Learning to Hash o Isotropic Hashing o Scalable Graph Hashing with Feature Transformation o Supervised Hashing with Latent Factor Models o Column Sampling based Discrete Supervised Hashing o Deep Supervised Hashing with Pairwise Labels o Supervised Multimodal Hashing with SCM Multiple-Bit Quantization Distributed Learning Coupled Group Lasso for Web-Scale CTR Prediction Distributed Power-Law Graph Computing ④Stochastic Learning o Fast Asynchronous Parallel Stochastic Gradient Descent Distributed Stochastic ADMM for Matrix Factorization Conclusion 口卡+得三4元互)Q0 Li (http://cs.nju.edu.cn/lvj) Big Leaming CS.NJU 2/115Outline 1 Introduction 2 Learning to Hash Isotropic Hashing Scalable Graph Hashing with Feature Transformation Supervised Hashing with Latent Factor Models Column Sampling based Discrete Supervised Hashing Deep Supervised Hashing with Pairwise Labels Supervised Multimodal Hashing with SCM Multiple-Bit Quantization 3 Distributed Learning Coupled Group Lasso for Web-Scale CTR Prediction Distributed Power-Law Graph Computing 4 Stochastic Learning Fast Asynchronous Parallel Stochastic Gradient Descent Distributed Stochastic ADMM for Matrix Factorization 5 Conclusion Li (http://cs.nju.edu.cn/lwj) Big Learning CS, NJU 2 / 115