Optimal Linear Baseline Models for Scientific Machine Learning | Paper Citation Record

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This paper cites Deep Learning.

Optimal Linear Baseline Models for Scientific Machine Learning Deep Learning

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This paper cites Surrogate and reduced-order modeling: a comparison of approaches for large- scale statistical inverse problems.

Optimal Linear Baseline Models for Scientific Machine Learning Surrogate and reduced-order modeling: a comparison of approaches for large- scale statistical inverse problems

Reference 2

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This paper cites Kernel methods for surrogate modeling.

Optimal Linear Baseline Models for Scientific Machine Learning Kernel methods for surrogate modeling

Reference 3

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This paper cites Physics-informed neural networks: a deep learning framework for solving forward and inverse problems involving nonlinear partial differential equations.

Optimal Linear Baseline Models for Scientific Machine Learning Physics-informed neural networks: a deep learning framework for solving forward and inverse problems involving nonlinear partial differential equations

Reference 4

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This paper cites Scientific machine learning through physics–informed neural networks: where we are and what’s next.

Optimal Linear Baseline Models for Scientific Machine Learning Scientific machine learning through physics–informed neural networks: where we are and what’s next

Reference 5

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This paper cites Deep neural network approach to forward-inverse problems.

Optimal Linear Baseline Models for Scientific Machine Learning Deep neural network approach to forward-inverse problems

Reference 6

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This paper cites Exact representation and efficient approximations of linear model predictive control laws via HardTanh type deep neural networks.

Optimal Linear Baseline Models for Scientific Machine Learning Exact representation and efficient approximations of linear model predictive control laws via HardTanh type deep neural networks

Reference 7

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This paper cites Inverse Problem Theory and Methods for Model Parameter Estimation.

Optimal Linear Baseline Models for Scientific Machine Learning Inverse Problem Theory and Methods for Model Parameter Estimation

Reference 8

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This paper cites Discrete Inverse Problems: Insight and Algorithms.

Optimal Linear Baseline Models for Scientific Machine Learning Discrete Inverse Problems: Insight and Algorithms

Reference 9

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This paper cites Sur les probl` emes aux d´ eriv´ ees partielles et leur signification physique.

Optimal Linear Baseline Models for Scientific Machine Learning Sur les probl` emes aux d´ eriv´ ees partielles et leur signification physique

Reference 10

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Optimal Linear Baseline Models for Scientific Machine Learning Unresolved cited work

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This paper cites Solving inverse problems using data-driven models.

Optimal Linear Baseline Models for Scientific Machine Learning Solving inverse problems using data-driven models

Reference 12

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Observation 3688fec6-9955-4adb-a2dd-1bb4a1c32ccd · outbound

This paper cites Modern regularization methods for inverse problems.

Optimal Linear Baseline Models for Scientific Machine Learning Modern regularization methods for inverse problems

Reference 13

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This paper cites Variational regularization in inverse problems and machine learning.

Optimal Linear Baseline Models for Scientific Machine Learning Variational regularization in inverse problems and machine learning

Reference 14

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This paper cites Solution paths of variational regularization methods for inverse problems.

Optimal Linear Baseline Models for Scientific Machine Learning Solution paths of variational regularization methods for inverse problems

Reference 15

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This paper cites Iterative regularization with a general penalty term-theory and application to L1 and TV regularization.

Optimal Linear Baseline Models for Scientific Machine Learning Iterative regularization with a general penalty term-theory and application to L1 and TV regularization

Reference 16

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This paper cites Iterative total variation schemes for nonlinear inverse prob- lems.

Optimal Linear Baseline Models for Scientific Machine Learning Iterative total variation schemes for nonlinear inverse prob- lems

Reference 17

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This paper cites Empirical Bayesian regularization of the inverse acoustic problem.

Optimal Linear Baseline Models for Scientific Machine Learning Empirical Bayesian regularization of the inverse acoustic problem

Reference 18

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Observation 6fab6bf0-95f7-4891-aa9b-5fa4e2ce40fc · outbound

This paper cites Inverse problems: from regularization to Bayesian inference.

Optimal Linear Baseline Models for Scientific Machine Learning Inverse problems: from regularization to Bayesian inference

Reference 19

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Observation 70c4e035-c7be-401f-9fd0-1d1f6bf141cb · outbound

This paper cites Deep learning techniques for inverse problems in imaging.

Optimal Linear Baseline Models for Scientific Machine Learning Deep learning techniques for inverse problems in imaging

Reference 20

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This paper cites Deep learning methods for inverse problems.

Optimal Linear Baseline Models for Scientific Machine Learning Deep learning methods for inverse problems

Reference 21

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This paper cites Learning regularization parameters of inverse problems via deep neural networks.

Optimal Linear Baseline Models for Scientific Machine Learning Learning regularization parameters of inverse problems via deep neural networks

Reference 22

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This paper cites Deep convolutional neural network for inverse problems in imaging.

Optimal Linear Baseline Models for Scientific Machine Learning Deep convolutional neural network for inverse problems in imaging

Reference 23

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This paper cites Solving Inverse Problems in Medical Imaging with Score-Based Generative Models.

Optimal Linear Baseline Models for Scientific Machine Learning Solving Inverse Problems in Medical Imaging with Score-Based Generative Models

Reference 24

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This paper cites Convolutional neural networks for inverse problems in imaging: a review.

Optimal Linear Baseline Models for Scientific Machine Learning Convolutional neural networks for inverse problems in imaging: a review

Reference 25

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This paper cites Using deep neural networks for inverse problems in imaging: beyond analytical methods.

Optimal Linear Baseline Models for Scientific Machine Learning Using deep neural networks for inverse problems in imaging: beyond analytical methods

Reference 26

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This paper cites Deep magnetic resonance image reconstruction: inverse problems meet neural networks.

Optimal Linear Baseline Models for Scientific Machine Learning Deep magnetic resonance image reconstruction: inverse problems meet neural networks

Reference 27

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This paper cites Interpretation of inaccurate, insufficient and inconsistent data.

Optimal Linear Baseline Models for Scientific Machine Learning Interpretation of inaccurate, insufficient and inconsistent data

Reference 28

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This paper cites An application of the Wiener-Kolmogorov smoothing theory to matrix inversion.

Optimal Linear Baseline Models for Scientific Machine Learning An application of the Wiener-Kolmogorov smoothing theory to matrix inversion

Reference 29

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Optimal Linear Baseline Models for Scientific Machine Learning Unresolved cited work

Reference 30

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This paper cites Extrapolation, Interpolation, and Smoothing of Stationary Time Series: with Engi- neering Applications.

Optimal Linear Baseline Models for Scientific Machine Learning Extrapolation, Interpolation, and Smoothing of Stationary Time Series: with Engi- neering Applications

Reference 31

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This paper cites Reduced-Rank Regression for the Multivariate Linear Model.

Optimal Linear Baseline Models for Scientific Machine Learning Reduced-Rank Regression for the Multivariate Linear Model

Reference 32

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This paper cites The Bayesian approach to inverse problems.

Optimal Linear Baseline Models for Scientific Machine Learning The Bayesian approach to inverse problems

Reference 33

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This paper cites Wiley, 2008.

Optimal Linear Baseline Models for Scientific Machine Learning Wiley, 2008

Reference 34

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Observation 101c45d7-73c1-48bb-a20e-10a8db4fe972 · outbound

This paper cites Inverse problems: a Bayesian perspective.

Optimal Linear Baseline Models for Scientific Machine Learning Inverse problems: a Bayesian perspective

Reference 35

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This paper cites A Paired Autoencoder Framework for Inverse Problems via Bayes Risk Minimization.

Optimal Linear Baseline Models for Scientific Machine Learning A Paired Autoencoder Framework for Inverse Problems via Bayes Risk Minimization

Reference 36

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This paper cites Paired autoencoders for likelihood-free estimation in inverse problems.

Optimal Linear Baseline Models for Scientific Machine Learning Paired autoencoders for likelihood-free estimation in inverse problems

Reference 37

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This paper cites Optimal regularized low rank inverse approximation.

Optimal Linear Baseline Models for Scientific Machine Learning Optimal regularized low rank inverse approximation

Reference 38

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This paper cites Optimal low-rank approximations of Bayesian linear inverse problems.

Optimal Linear Baseline Models for Scientific Machine Learning Optimal low-rank approximations of Bayesian linear inverse problems

Reference 39

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This paper cites Solving Bayesian inverse problems via variational autoencoders.

Optimal Linear Baseline Models for Scientific Machine Learning Solving Bayesian inverse problems via variational autoencoders

Reference 40

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This paper cites Why are big data matrices approximately low rank?.

Optimal Linear Baseline Models for Scientific Machine Learning Why are big data matrices approximately low rank?

Reference 41

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This paper cites Autoencoders and their applications in machine learning: a survey.

Optimal Linear Baseline Models for Scientific Machine Learning Autoencoders and their applications in machine learning: a survey

Reference 42

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This paper cites Medical image denoising using convolutional denoising autoencoders.

Optimal Linear Baseline Models for Scientific Machine Learning Medical image denoising using convolutional denoising autoencoders

Reference 43

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This paper cites Stacked convolutional auto-encoders for hierarchical feature extraction.

Optimal Linear Baseline Models for Scientific Machine Learning Stacked convolutional auto-encoders for hierarchical feature extraction

Reference 44

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Observation 9d1b25ea-f59a-4b33-913c-dcd5a257ec47 · outbound

This paper cites Multilayer feedforward networks are uni- versal approximators.

Optimal Linear Baseline Models for Scientific Machine Learning Multilayer feedforward networks are uni- versal approximators

Reference 45

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This paper cites The mythos of model interpretability: in machine learning, the concept of inter- pretability is both important and slippery.

Optimal Linear Baseline Models for Scientific Machine Learning The mythos of model interpretability: in machine learning, the concept of inter- pretability is both important and slippery

Reference 46

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This paper cites A survey on neural network interpretability.

Optimal Linear Baseline Models for Scientific Machine Learning A survey on neural network interpretability

Reference 47

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Observation b84413b8-00dc-4704-9b84-53fb478075e3 · outbound

This paper cites Neural networks and principal component analysis: learning from examples without local minima.

Optimal Linear Baseline Models for Scientific Machine Learning Neural networks and principal component analysis: learning from examples without local minima

Reference 48

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This paper cites Auto-association by multilayer perceptrons and singular value decompo- sition.

Optimal Linear Baseline Models for Scientific Machine Learning Auto-association by multilayer perceptrons and singular value decompo- sition

Reference 49

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This paper cites From Principal Subspaces to Principal Components with Linear Autoencoders.

Optimal Linear Baseline Models for Scientific Machine Learning From Principal Subspaces to Principal Components with Linear Autoencoders

Reference 50

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This paper cites Regularized linear autoencoders recover the principal components, eventually.

Optimal Linear Baseline Models for Scientific Machine Learning Regularized linear autoencoders recover the principal components, eventually

Reference 51

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This paper cites The approximation of one matrix by another of lower rank.

Optimal Linear Baseline Models for Scientific Machine Learning The approximation of one matrix by another of lower rank

Reference 52

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This paper cites Symmetric gauge functions and unitarily invariant norms.

Optimal Linear Baseline Models for Scientific Machine Learning Symmetric gauge functions and unitarily invariant norms

Reference 53

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Observation f64a5cea-fe61-4e79-8b78-0263a3732e8a · outbound

This paper cites Zur theorie der linearen und nichtlinearen integralgleichungen.

Optimal Linear Baseline Models for Scientific Machine Learning Zur theorie der linearen und nichtlinearen integralgleichungen

Reference 54

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This paper cites Generalized rank-constrained matrix approximations.

Optimal Linear Baseline Models for Scientific Machine Learning Generalized rank-constrained matrix approximations

Reference 55

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This paper cites Finding structure with randomness: probabilistic algo- rithms for constructing approximate matrix decompositions.

Optimal Linear Baseline Models for Scientific Machine Learning Finding structure with randomness: probabilistic algo- rithms for constructing approximate matrix decompositions

Reference 56

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Observation d38d423d-d345-4866-82c9-0a7703657629 · outbound

This paper cites Near-optimal column-based matrix reconstruction.

Optimal Linear Baseline Models for Scientific Machine Learning Near-optimal column-based matrix reconstruction

Reference 57

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Observation 74742c1e-dab3-41ce-ae82-cc9ae4a254b4 · outbound

This paper cites Dimensionality reduction for k-means clustering and low rank approxi- mation.

Optimal Linear Baseline Models for Scientific Machine Learning Dimensionality reduction for k-means clustering and low rank approxi- mation

Reference 58

Resolution: verified exact; arxiv_id_nonexistent, observed 2026-08-05T23:19:31.188214Z

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Observation 9f2ee133-f96a-4d94-b6e5-b71829c0f036 · outbound

This paper cites Low-rank matrix approximation with manifold regularization.

Optimal Linear Baseline Models for Scientific Machine Learning Low-rank matrix approximation with manifold regularization

Reference 59

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Observation faf06b79-b5a4-4509-9a85-28a648b9ab50 · outbound

This paper cites Subspace-orbit randomized decomposi- tion for low-rank matrix approximations.

Optimal Linear Baseline Models for Scientific Machine Learning Subspace-orbit randomized decomposi- tion for low-rank matrix approximations

Reference 60

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Observation ffdbaa11-be0d-42c0-9393-63c16cd2261a · outbound

This paper cites Dimensionality reduction strategy based on auto-encoder.

Optimal Linear Baseline Models for Scientific Machine Learning Dimensionality reduction strategy based on auto-encoder

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This paper cites Rank Reduction Autoencoders.

Optimal Linear Baseline Models for Scientific Machine Learning Rank Reduction Autoencoders

Reference 62

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This paper cites Learning-based low-rank approximations.

Optimal Linear Baseline Models for Scientific Machine Learning Learning-based low-rank approximations

Reference 63

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Optimal Linear Baseline Models for Scientific Machine Learning Sparse Bayesian methods for low-rank matrix estimation

Reference 64

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Observation a1476b53-ce2d-4a14-93af-ce0b915bc6b8 · outbound

This paper cites Learning low-rank latent spaces with simple deterministic autoencoder: theoretical and empirical insights.

Optimal Linear Baseline Models for Scientific Machine Learning Learning low-rank latent spaces with simple deterministic autoencoder: theoretical and empirical insights

Reference 65

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Optimal Linear Baseline Models for Scientific Machine Learning Bayes and empirical Bayes methods for data analysis

Reference 66

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This paper cites Computing optimal low-rank matrix approximations for image processing.

Optimal Linear Baseline Models for Scientific Machine Learning Computing optimal low-rank matrix approximations for image processing

Reference 67

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Optimal Linear Baseline Models for Scientific Machine Learning Optimal regularized inverse matrices for inverse problems

Reference 68

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This paper cites An efficient approach for computing optimal low-rank regu- larized inverse matrices.

Optimal Linear Baseline Models for Scientific Machine Learning An efficient approach for computing optimal low-rank regu- larized inverse matrices

Reference 69

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This paper cites Auto-Encoding Variational Bayes.

Optimal Linear Baseline Models for Scientific Machine Learning Auto-Encoding Variational Bayes

Reference 70

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This paper cites On the reciprocal of the general algebraic matrix.

Optimal Linear Baseline Models for Scientific Machine Learning On the reciprocal of the general algebraic matrix

Reference 71

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Optimal Linear Baseline Models for Scientific Machine Learning A generalized inverse for matrices

Reference 72

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Optimal Linear Baseline Models for Scientific Machine Learning Good Things Come in Pairs: Paired Autoencoders for Inverse Problems

Reference 73

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Observation ba1a2999-3d9c-4c89-97bf-a95b83d07f1c · outbound

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Optimal Linear Baseline Models for Scientific Machine Learning The Elements of Statistical Learning

Reference 74

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Optimal Linear Baseline Models for Scientific Machine Learning Williams and Carl E

Reference 75

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This paper cites MedMNIST v2-a large-scale lightweight benchmark for 2D and 3D biomedical image classification.

Optimal Linear Baseline Models for Scientific Machine Learning MedMNIST v2-a large-scale lightweight benchmark for 2D and 3D biomedical image classification

Reference 76

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This paper cites Annotated high-throughput mi- croscopy image sets for validation.

Optimal Linear Baseline Models for Scientific Machine Learning Annotated high-throughput mi- croscopy image sets for validation

Reference 77

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Optimal Linear Baseline Models for Scientific Machine Learning ChestX-ray8: hospital-scale chest x-ray database and benchmarks on weakly-supervised classification and localization of common thorax diseases

Reference 78

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Optimal Linear Baseline Models for Scientific Machine Learning The Liver Tumor Segmentation Benchmark (LiTS)

Reference 79

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Optimal Linear Baseline Models for Scientific Machine Learning Unresolved cited work

Reference 80

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Optimal Linear Baseline Models for Scientific Machine Learning Adam: A Method for Stochastic Optimization

Reference 81

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Optimal Linear Baseline Models for Scientific Machine Learning Common risk factors in the returns on stocks and bonds

Reference 82

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Optimal Linear Baseline Models for Scientific Machine Learning Capital asset prices: a theory of market equilibrium under conditions of risk

Reference 83

Resolution: verified exact; arxiv_id_nonexistent, observed 2026-08-05T23:19:29.518425Z

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Optimal Linear Baseline Models for Scientific Machine Learning The cross-section of expected stock returns

Reference 84

Resolution: verified exact; arxiv_id_nonexistent, observed 2026-08-05T23:19:29.347481Z

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Observation 0727322f-567f-43ae-a46e-93e1a6639c05 · outbound

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Optimal Linear Baseline Models for Scientific Machine Learning Efficient multiple organ localization in CT image using 3D region proposal network

Reference 85

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Observation 0c1c5ee1-2ce6-4992-a470-143c2763b921 · outbound

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Optimal Linear Baseline Models for Scientific Machine Learning A cluster driven log-volatility factor model: a deepening on the source of the volatility clustering

Reference 86

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Optimal Linear Baseline Models for Scientific Machine Learning The arbitrage theory of capital asset pricing

Reference 87

Resolution: verified exact; doi, observed 2026-08-05T23:19:28.550180Z

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Observation b53df0a7-e382-4353-8ef4-a9f78dc34e92 · outbound

This paper cites yfinance: fownload market data from Yahoo! Finance’s API.

Optimal Linear Baseline Models for Scientific Machine Learning yfinance: fownload market data from Yahoo! Finance’s API

Reference 88

Resolution: unresolved; no resolver link, observed 2026-08-05T23:19:28.323698Z

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Observation bc08b810-3cfd-4213-b03b-5a63f8b5beaf · outbound

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Optimal Linear Baseline Models for Scientific Machine Learning Quant GANs: deep generation of financial time series

Reference 89

Resolution: verified exact; arxiv_id_nonexistent, observed 2026-08-05T23:19:29.160749Z

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Optimal Linear Baseline Models for Scientific Machine Learning Principal component analysis

Reference 90

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Observation 09d263cb-7cb5-43a8-adf8-4d80e1690215 · outbound

This paper cites The Varimax criterion for analytic rotation in factor analysis.

Optimal Linear Baseline Models for Scientific Machine Learning The Varimax criterion for analytic rotation in factor analysis

Reference 91

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Observation 25ae7609-b0c6-41cc-9f86-9b46447b317d · outbound

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Optimal Linear Baseline Models for Scientific Machine Learning A matrix formulation of Kaiser’s Varimax criterion

Reference 92

Resolution: verified exact; doi, observed 2026-08-05T23:19:28.504043Z

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Observation 533f77ac-e8d4-4dc8-8aa1-2841e3c9b829 · outbound

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Optimal Linear Baseline Models for Scientific Machine Learning An overview of analytic rotation in exploratory factor analysis

Reference 93

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Optimal Linear Baseline Models for Scientific Machine Learning Fama and Kenneth R

Reference 94

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Observation 55e854a2-06e9-45f3-ae67-f2bb81d953e2 · outbound

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Optimal Linear Baseline Models for Scientific Machine Learning Campbell, Andrew W

Reference 95

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Observation 1bfdeb18-3e02-4cdf-a401-70db95a48ad1 · outbound

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Optimal Linear Baseline Models for Scientific Machine Learning Brockwell and Richard A

Reference 96

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Optimal Linear Baseline Models for Scientific Machine Learning Generalized autoregressive conditional heteroskedasticity

Reference 97

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Optimal Linear Baseline Models for Scientific Machine Learning Unsupervised alignment of embeddings with Wasserstein Procrustes

Reference 98

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Optimal Linear Baseline Models for Scientific Machine Learning A Mechanism for Producing Aligned Latent Spaces with Autoencoders

Reference 99

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Optimal Linear Baseline Models for Scientific Machine Learning A value for n-person games

Reference 100

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