《人工智能、机器学习与大数据》课程教学资源（参考文献）Latent Wishart processes for relational kernel learning（讲稿）

《人工智能、机器学习与大数据》课程教学资源（参考文献）Buffered Asynchronous SGD for Byzantine Learning

1 Introduction 2 Unsupervised Hashing 3 Supervised Hashing 4 Ranking-based Hashing 5 Multimodal Hashing 6 Deep Hashing 7 Quantization 8 Conclusion 9 Reference

1 Introduction Problem Definition Existing Methods 2 Scalable Graph Hashing with Feature Transformation Motivation Model and Learning Experiment 3 Conclusion 4 Reference

1 Introduction Problem Definition Existing Methods 2 Isotropic Hashing Model Learning Experiment 3 Multiple-Bit Quantization Double-Bit Quantization Manhattan Quantization 4 Conclusion 5 Reference

1 Introduction Problem Definition Existing Methods Motivation and Contribution 2 Isotropic Hashing Model Learning Experimental Results 3 Multiple-Bit Quantization Double-Bit Quantization Manhattan Quantization 4 Conclusion 5 Reference

 Motivation – Why do we need big data integration? – How has “small” data integration been done? – Challenges in big data integration  Schema alignment  Record linkage  Data fusion  Emerging topics

§ 避免绝对值小的数作除数 § 避免两个相近的数据相减 § 要防止大数“吃掉”小数 § 尽量减少计算工作量 § 选用数值稳定性好的算法

一、循环码译码的原理 二、循环码的译码电路------梅吉特译码法（只纠一个错） 三、扩展汉明码的译码 四、缩短循环码的译码 五、捕错译码——原理 六、大数逻辑译码

【人工智能】面向调线调坡的点云大数据分析及深度模型研究