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ORCIDSIAM Journal on Mathematics of Data Science, Volume 1
Volume 1, Number 1, 2019
- Tamara G. Kolda:
Introduction to SIAM Journal on Mathematics of Data Science (SIMODS). 1-7 - Helmut Bölcskei
, Philipp Grohs, Gitta Kutyniok, Philipp Petersen:
Optimal Approximation with Sparsely Connected Deep Neural Networks. 8-45 - Aviad Aberdam, Jeremias Sulam, Michael Elad:
Multi-Layer Sparse Coding: The Holistic Way. 46-77 - Hadrien Montanelli, Qiang Du:
New Error Bounds for Deep ReLU Networks Using Sparse Grids. 78-92 - Nicolás García Trillos:
Variational Limits of k-NN Graph-Based Functionals on Data Clouds. 93-120 - Paul E. Anderson, Timothy P. Chartier, Amy Nicole Langville:
The Rankability of Data. 121-143 - Madeleine Udell, Alex Townsend:
Why Are Big Data Matrices Approximately Low Rank? 144-160 - Ahmed El Alaoui, Aaditya Ramdas, Florent Krzakala, Lenka Zdeborová, Michael I. Jordan:
Decoding from Pooled Data: Sharp Information-Theoretic Bounds. 161-188 - Benjamin Arras, Markus Bachmayr, Albert Cohen:
Sequential Sampling for Optimal Weighted Least Squares Approximations in Hierarchical Spaces. 189-207 - Tingran Gao, Shahar Z. Kovalsky, Ingrid Daubechies:
Gaussian Process Landmarking on Manifolds. 208-236 - Tingran Gao, Shahar Z. Kovalsky, Doug M. Boyer, Ingrid Daubechies:
Gaussian Process Landmarking for Three-Dimensional Geometric Morphometrics. 237-267
Volume 1, Number 2, 2019
- Francesco Tudisco, Desmond J. Higham:
A Nonlinear Spectral Method for Core-Periphery Detection in Networks. 269-292 - Austin R. Benson:
Three Hypergraph Eigenvector Centralities. 293-312 - George C. Linderman, Stefan Steinerberger:
Clustering with t-SNE, Provably. 313-332 - Nate Veldt, David F. Gleich, Anthony Wirth, James Saunderson:
Metric-Constrained Optimization for Graph Clustering Algorithms. 333-355 - Baichuan Yuan, Hao Li, Andrea L. Bertozzi, P. Jeffrey Brantingham, Mason A. Porter:
Multivariate Spatiotemporal Hawkes Processes and Network Reconstruction. 356-382
Volume 1, Number 3, 2019
- Eric C. Chi, Stefan Steinerberger:
Recovering Trees with Convex Clustering. 383-407 - D. Russell Luke, Shoham Sabach, Marc Teboulle:
Optimization on Spheres: Models and Proximal Algorithms with Computational Performance Comparisons. 408-445 - François Malgouyres, Joseph Landsberg:
Multilinear Compressive Sensing and an Application to Convolutional Linear Networks. 446-475 - Wei Zhu, Qiang Qiu, Bao Wang, Jianfeng Lu, Guillermo Sapiro, Ingrid Daubechies:
Stop Memorizing: A Data-Dependent Regularization Framework for Intrinsic Pattern Learning. 476-496 - Amelia Perry, Jonathan Weed, Afonso S. Bandeira, Philippe Rigollet, Amit Singer:
The Sample Complexity of Multireference Alignment. 497-517 - Jeremy E. Cohen, Nicolas Gillis:
Identifiability of Complete Dictionary Learning. 518-536 - Jared Tanner, Andrew Thompson, Simon Vary:
Matrix Rigidity and the Ill-Posedness of Robust PCA and Matrix Completion. 537-554 - Christopher Aicher, Yi-An Ma, Nicholas J. Foti, Emily B. Fox:
Stochastic Gradient MCMC for State Space Models. 555-587 - Tal Shnitzer, Mirela Ben-Chen, Leonidas J. Guibas, Ronen Talmon, Hau-Tieng Wu:
Recovering Hidden Components in Multimodal Data with Composite Diffusion Operators. 588-616 - Jaeho Lee, Maxim Raginsky:
Learning Finite-Dimensional Coding Schemes with Nonlinear Reconstruction Maps. 617-642 - Paul E. Anderson, Timothy P. Chartier, Amy Nicole Langville:
Erratum: The Rankability of Data. 643-646
Volume 1, Number 4, 2019
- Sinan G. Aksoy, Kathleen Nowak, Stephen J. Young:
A Linear-Time Algorithm and Analysis of Graph Relative Hausdorff Distance. 647-666 - A. Roxana Pamfil, Sam D. Howison, Renaud Lambiotte, Mason A. Porter:
Relating Modularity Maximization and Stochastic Block Models in Multilayer Networks. 667-698 - Pierre Baldi, Roman Vershynin:
Polynomial Threshold Functions, Hyperplane Arrangements, and Random Tensors. 699-729 - Nhat Ho, XuanLong Nguyen:
Singularity Structures and Impacts on Parameter Estimation in Finite Mixtures of Distributions. 730-758 - Arda Antikacioglu, Tanvi Bajpai, R. Ravi:
A New System-Wide Diversity Measure for Recommendations with Efficient Algorithms. 759-779 - Jeff Calder:
Consistency of Lipschitz Learning with Infinite Unlabeled Data and Finite Labeled Data. 780-812
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