Summary of A Provably Accurate Randomized Sampling Algorithm For Logistic Regression, by Agniva Chowdhury et al.
A Provably Accurate Randomized Sampling Algorithm for Logistic Regressionby Agniva Chowdhury, Pradeep RamuhalliFirst submitted to…
A Provably Accurate Randomized Sampling Algorithm for Logistic Regressionby Agniva Chowdhury, Pradeep RamuhalliFirst submitted to…
Achieving Instance-dependent Sample Complexity for Constrained Markov Decision Processby Jiashuo Jiang, Yinyu YeFirst submitted to…
Boosting Graph Pooling with Persistent Homologyby Chaolong Ying, Xinjian Zhao, Tianshu YuFirst submitted to arxiv…
Deep Contrastive Graph Learning with Clustering-Oriented Guidanceby Mulin Chen, Bocheng Wang, Xuelong LiFirst submitted to…
Building Flexible Machine Learning Models for Scientific Computing at Scaleby Tianyu Chen, Haoyi Zhou, Ying…
Spectrum Extraction and Clipping for Implicitly Linear Layersby Ali Ebrahimpour Boroojeny, Matus Telgarsky, Hari SundaramFirst…
A Step-by-step Introduction to the Implementation of Automatic Differentiationby Yu-Hsueh Fang, He-Zhe Lin, Jie-Jyun Liu,…
Feature Selection Based on Orthogonal Constraints and Polygon Areaby Zhenxing Zhang, Jun Ge, Zheng Wei,…
Deep Learning Approaches for Improving Question Answering Systems in Hepatocellular Carcinoma Researchby Shuning Huo, Yafei…
HiGPT: Heterogeneous Graph Language Modelby Jiabin Tang, Yuhao Yang, Wei Wei, Lei Shi, Long Xia,…