An introduction to Neural Networks for Physicists | Paper Citation Record

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An introduction to Neural Networks for Physicists i.e., each elementMij is the average of the elements of a2×2 submatrix ofB 4×4

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This paper cites Note that after such an operation, the size of the processed input data is now com- pressed into a smaller matrix.

An introduction to Neural Networks for Physicists Note that after such an operation, the size of the processed input data is now com- pressed into a smaller matrix

Reference 2

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This paper cites ∆l′ = (y[n] l′ −ˆyl′) d dz[n] l′ fl′(z[n] l′ )(19).

An introduction to Neural Networks for Physicists ∆l′ = (y[n] l′ −ˆyl′) d dz[n] l′ fl′(z[n] l′ )(19)

Reference 3

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An introduction to Neural Networks for Physicists (18) ∆l′←∆ l′ ˆM d dw[k] il z[k] l (20) 13

Reference 4

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An introduction to Neural Networks for Physicists 03- Example 1,

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This paper cites Reviews of Modern Physics.91, 045002 (2019).

An introduction to Neural Networks for Physicists Reviews of Modern Physics.91, 045002 (2019)

Reference 8

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This paper cites k-Nearest Neighbour Classifiers: 2nd Edition (with Python examples).

An introduction to Neural Networks for Physicists k-Nearest Neighbour Classifiers: 2nd Edition (with Python examples)

Reference 11

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This paper cites An Introduction to Logistic Regression Analysis and Reporting.

An introduction to Neural Networks for Physicists An Introduction to Logistic Regression Analysis and Reporting

Reference 12

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An introduction to Neural Networks for Physicists Logistic Regression: From Art to Sci- ence

Reference 13

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This paper cites What is a support vector machine?.

An introduction to Neural Networks for Physicists What is a support vector machine?

Reference 14

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This paper cites C., de Carvalho, A.

An introduction to Neural Networks for Physicists C., de Carvalho, A

Reference 15

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This paper cites Decision trees: a recent overview.

An introduction to Neural Networks for Physicists Decision trees: a recent overview

Reference 16

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An introduction to Neural Networks for Physicists What are decision trees? Nat Biotechnol

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An introduction to Neural Networks for Physicists Introduction to Machine Learning for the Sciences

Reference 18

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This paper cites Machine Learning Algorithms: A ref- erence guide to popular algorithms for data science and ma- chine learning.

An introduction to Neural Networks for Physicists Machine Learning Algorithms: A ref- erence guide to popular algorithms for data science and ma- chine learning

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An introduction to Neural Networks for Physicists Machine learning and quantum devices

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An introduction to Neural Networks for Physicists and Courville, A

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An introduction to Neural Networks for Physicists Unresolved cited work

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This paper cites Solving Rubik's Cube with a Robot Hand.

An introduction to Neural Networks for Physicists Solving Rubik's Cube with a Robot Hand

Reference 25

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An introduction to Neural Networks for Physicists Unresolved cited work

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An introduction to Neural Networks for Physicists and Whiteson, D

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This paper cites A Living Review of Machine Learning for Particle Physics.

An introduction to Neural Networks for Physicists A Living Review of Machine Learning for Particle Physics

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An introduction to Neural Networks for Physicists Machine learning phases of matter

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An introduction to Neural Networks for Physicists and Huber, S

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An introduction to Neural Networks for Physicists S., Isayev, O., Roitberg, A

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This paper cites Physics-Informed Machine Learning: A Survey on Problems, Methods and Applications.

An introduction to Neural Networks for Physicists Physics-Informed Machine Learning: A Survey on Problems, Methods and Applications

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An introduction to Neural Networks for Physicists Physics-Informed machine learning,

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An introduction to Neural Networks for Physicists When Physics Meets Machine Learning: A Survey of Physics-Informed Machine Learning

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An introduction to Neural Networks for Physicists Scientific machine learning through physics–informed neural networks: Where we are and what’s next

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An introduction to Neural Networks for Physicists Physics-Guided, Physics-Informed, and Physics-Encoded Neural Networks in Scientific Computing

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An introduction to Neural Networks for Physicists Physics-informed neural networks (PINNs) for fluid mechanics: A review

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An introduction to Neural Networks for Physicists A Review of Physics-Informed Machine Learning in Fluid Mechanics

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An introduction to Neural Networks for Physicists Uncertainty Quantification in Scientific Machine Learning: Methods, Metrics, and Comparisons

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An introduction to Neural Networks for Physicists Physics-Guided Deep Learning for Dynamical Systems: A Survey

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An introduction to Neural Networks for Physicists Neural networks in quantum many-body physics: a hands-on tutorial

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An introduction to Neural Networks for Physicists Artificial neural networks for photonic applications—from algorithms to implementation: tutorial,

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An introduction to Neural Networks for Physicists Froim, S.; Yosef, Erez ; Giryes, Raja ;Bahabad, Alon

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An introduction to Neural Networks for Physicists A Review of Optical Neural Networks

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An introduction to Neural Networks for Physicists Quantum Computing and Ma- chine Learning on an Integrated Photonics Platform

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An introduction to Neural Networks for Physicists Unresolved cited work

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An introduction to Neural Networks for Physicists Adaptive quantum state tomogra- phy with neural networks

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An introduction to Neural Networks for Physicists Three ways to solve par- tial differential equations with neural networks—a review,

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This paper cites Integrating Scientific Knowledge with Machine Learning for Engineering and Environmental Systems.

An introduction to Neural Networks for Physicists Integrating Scientific Knowledge with Machine Learning for Engineering and Environmental Systems

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An introduction to Neural Networks for Physicists Adv.6,eaay2631(2020)

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An introduction to Neural Networks for Physicists Fourier Neural Operator for Parametric Partial Differential Equations

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An introduction to Neural Networks for Physicists Optical machine learning with inco- herent light and a single-pixel detector

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An introduction to Neural Networks for Physicists Arti- ficial intelligence and machine learning in optics: tutorial,

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An introduction to Neural Networks for Physicists NeuralPDE: Automating Physics-Informed Neural Networks (PINNs) with Error Approximations

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An introduction to Neural Networks for Physicists Adv.6,eaay2631(2020).DOI:10.1126/sciadv.aay2631

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An introduction to Neural Networks for Physicists PhyCV: The First Physics-inspired Computer Vision Library

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This paper cites Physics-informed computer vision: A review and perspectives.

An introduction to Neural Networks for Physicists Physics-informed computer vision: A review and perspectives

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This paper cites ChatGPT: A comprehensive review on background, applications, key challenges, bias, ethics, limita- tions and future scope.

An introduction to Neural Networks for Physicists ChatGPT: A comprehensive review on background, applications, key challenges, bias, ethics, limita- tions and future scope

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This paper cites Convolutional neu- ral networks for image classification,.

An introduction to Neural Networks for Physicists Convolutional neu- ral networks for image classification,

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This paper cites The Application and Im- provement of Deep Neural Networks in Environmental Sound Recognition.

An introduction to Neural Networks for Physicists The Application and Im- provement of Deep Neural Networks in Environmental Sound Recognition

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This paper cites Natural Language Processing with Improved Deep Learning Neural Networks.

An introduction to Neural Networks for Physicists Natural Language Processing with Improved Deep Learning Neural Networks

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An introduction to Neural Networks for Physicists Unresolved cited work

Reference 64

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An introduction to Neural Networks for Physicists Scikit-learn: Machine learning in Python

Reference 65

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This paper cites Understanding the dif- ficulty of training deep feedforward neural networks.

An introduction to Neural Networks for Physicists Understanding the dif- ficulty of training deep feedforward neural networks

Reference 66

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This paper cites Review and comparison of commonly used activation functions for deep neural networks.

An introduction to Neural Networks for Physicists Review and comparison of commonly used activation functions for deep neural networks

Reference 67

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An introduction to Neural Networks for Physicists Implicit neural representations with periodic activation functions

Reference 68

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An introduction to Neural Networks for Physicists Unresolved cited work

Reference 69

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An introduction to Neural Networks for Physicists A review on applications of genetic algorithm for artificial neural network

Reference 70

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An introduction to Neural Networks for Physicists Neural network structure optimization by simulated annealing

Reference 71

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An introduction to Neural Networks for Physicists An Overview of Stochastic Gradient De- scent in Machine Learning

Reference 72

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An introduction to Neural Networks for Physicists Survey of optimization algorithms in modern neural net- works

Reference 73

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An introduction to Neural Networks for Physicists Pulse modulation for enhanced control in NMR-based Quantum Devices

Reference 74

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This paper cites Grid Search, Random Search, Genetic Algorithm: A Big Comparison for NAS.

An introduction to Neural Networks for Physicists Grid Search, Random Search, Genetic Algorithm: A Big Comparison for NAS

Reference 75

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An introduction to Neural Networks for Physicists An overview of stochastic gradient-based methods

Reference 76

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An introduction to Neural Networks for Physicists Adam: A Method for Stochastic Optimization

Reference 77

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An introduction to Neural Networks for Physicists Unresolved cited work

Reference 78

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This paper cites Introduction to Neural Network.

An introduction to Neural Networks for Physicists Introduction to Neural Network

Reference 79

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This paper cites Learning internal representations by error propagation,.

An introduction to Neural Networks for Physicists Learning internal representations by error propagation,

Reference 80

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T20:28:25.775894Z

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An introduction to Neural Networks for Physicists Hinton, G

Reference 81

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An introduction to Neural Networks for Physicists Long short-term memory,

Reference 82

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An introduction to Neural Networks for Physicists A survey of transformers

Reference 83

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T20:28:25.604755Z

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An introduction to Neural Networks for Physicists An Introduction to Autoencoders

Reference 84

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An introduction to Neural Networks for Physicists Electronics 8(3), (2019)

Reference 85

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An introduction to Neural Networks for Physicists Lecture Notes: Neural Network Architectures

Reference 86

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An introduction to Neural Networks for Physicists Hopfield Networks is All You Need

Reference 87

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An introduction to Neural Networks for Physicists KAN: Kolmogorov-Arnold Networks

Reference 88

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This paper cites Pytorch: An imperative style, high- performance deep learning library.

An introduction to Neural Networks for Physicists Pytorch: An imperative style, high- performance deep learning library

Reference 89

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T20:28:25.433921Z

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Observation 45ef396a-68a4-45e3-93bc-27a91f70a033 · outbound

This paper cites Tensor- Flow: Large-scale machine learning on heterogeneous sys- tems, Software available from tensorflow.org.

An introduction to Neural Networks for Physicists Tensor- Flow: Large-scale machine learning on heterogeneous sys- tems, Software available from tensorflow.org

Reference 90

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T20:28:25.255034Z

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Observation 2f066b25-c945-4313-a772-aed8d5cd1cd2 · outbound

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An introduction to Neural Networks for Physicists JAX: composable transformations of Python+ NumPy programs

Reference 91

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T20:28:25.243053Z

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This paper cites Automatic differentiation in ma- chine learning: a survey.

An introduction to Neural Networks for Physicists Automatic differentiation in ma- chine learning: a survey

Reference 92

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T20:28:26.584749Z

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Observation 5c247ef6-72cb-48ff-bcf9-8f0f393c7fdd · outbound

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An introduction to Neural Networks for Physicists Artigos no Medium

Reference 93

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Observation 2b09b309-bea1-4d39-9b2e-bfe0a044b4f5 · outbound

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An introduction to Neural Networks for Physicists Im- ageNet Classification with Deep Convolutional Neural Net- works

Reference 94

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An introduction to Neural Networks for Physicists VisualBackProp: efficient visualization of CNNs

Reference 95

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Observation c33d2abc-e898-4afe-9328-80c8e5a6ba68 · outbound

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An introduction to Neural Networks for Physicists Feature Detection with Automatic Scale Selec- tion

Reference 96

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Observation 8b93d764-0010-47ad-ab0c-50690b1c05b6 · outbound

This paper cites and Murphey, Y .L.

An introduction to Neural Networks for Physicists and Murphey, Y .L

Reference 97

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T20:28:24.968434Z

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Observation 7e7a2bb6-c57e-4c95-b03b-bc4708b8b99b · outbound

This paper cites Detecting salient blob-like image structures and their scales with a scale-space primal sketch: A method for focus-of-attention.

An introduction to Neural Networks for Physicists Detecting salient blob-like image structures and their scales with a scale-space primal sketch: A method for focus-of-attention

Reference 98

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T20:28:24.853210Z

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Observation 00817ca8-acb8-4281-b345-020b24113fe1 · outbound

This paper cites Neocognitron: A self-organizing neural net- work model for a mechanism of pattern recognition unaffected by shift in position.

An introduction to Neural Networks for Physicists Neocognitron: A self-organizing neural net- work model for a mechanism of pattern recognition unaffected by shift in position

Reference 99

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T20:28:24.828010Z

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Observation dbd57c62-24b6-472f-9116-c4386074ddc2 · outbound

This paper cites Basodi, C.

An introduction to Neural Networks for Physicists Basodi, C

Reference 100

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This paper cites an unresolved cited work.

An introduction to Neural Networks for Physicists Unresolved cited work

Reference 101

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Observation 368d3eb3-bd45-4fa3-aac4-1bd218f7a6c6 · outbound

This paper cites Addressing the loss-metric mismatch with adaptive loss alignment.

An introduction to Neural Networks for Physicists Addressing the loss-metric mismatch with adaptive loss alignment

Reference 102

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T20:28:24.634030Z

Source-reported events for the cited work

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Outbound references

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