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Advances in Data Science Women in Data Science and Mathematics (WiSDM) 2023 /

This volume features recent advances in data science ranging from algebraic geometry used for existence and uniqueness proofs of low rank approximations for tensor data, to category theory used for natural language processing applications, to approximation and optimization frameworks developed for c...

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Bibliografiske detaljer
Institution som forfatter: SpringerLink (Online service)
Andre forfattere: Garcia-Cardona, Cristina (Editor), Lee, Harlin (Editor)
Format: Electronisk eBog
Sprog:engelsk
Udgivet: Cham : Springer Nature Switzerland : Imprint: Springer, 2025.
Udgivelse:1st ed. 2025.
Serier:Association for Women in Mathematics Series, 37
Fag:
Artificial intelligence > Data processing.
Mathematics > Data processing.
Graph theory.
Quantitative research.
Pattern recognition systems.
Natural language processing (Computer science).
Data Science.
Computational Science and Engineering.
Graph Theory.
Data Analysis and Big Data.
Automated Pattern Recognition.
Natural Language Processing (NLP).
Online adgang:https://doi.org/10.1007/978-3-031-87804-6
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Indholdsfortegnelse:
  • Chapter 1: Randomized Iterative Methods for Tensor Regression Under the t-product
  • Chapter 2: Matrix exponentials: Lie-Trotter-Suzuki fractal decomposition, Gauss Runge-Kutta polynomial formulation, and compressible features
  • Chapter 3: An exploration of graph distances, graph curvature, and applications to network analysis
  • Chapter 4: Time-Varying Graph Signal Recovery Using High-Order Smoothness and Adaptive Low-rankness
  • Chapter 5: Graph-Directed Topic Models of Text Documents
  • Chapter 6: Linear independent component analysis in Wasserstein space
  • Chapter 7: Faster Hodgerank Approximation Algorithm for Statistical Ranking and User Recommendation Problems
  • Chapter 8: A Comparison Study of Graph Laplacian Computation
  • Chapter 9: Supervised Dimension Reduction via Local Gradient Elongation
  • Chapter 10: Reducing NLP Model Embeddings for Deployment in Embedded Systems
  • Chapter 11: Automated extraction of roadside slope from aerial LiDAR data in rural North Carolina
  • Chapter 12: A non-parametric optimal design algorithm for population pharmacokinetics
  • Chapter 13: Unrolling Deep Learning End-to-End Method for Phase Retrieval
  • Chapter 14: Performance Analysis of MFCC and wav2vec on Stuttering Data
  • Chapter 15: Active Learning for Reducing Gender Gaps in Undergraduate Computing and Data Science
  • Chapter 16: Quantifying and Documenting Gender-Based Inequalities in the Mathematical Sciences in the United States.

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