Paper Citation Record · LEDGER

Optimal Density Functions for Weighted Convolution in Learning Models

As of 8 August 2026, this Paper Citation Record lists 44 of 44 outbound references and 0 inbound Pith citation observations for arXiv:2505.24527.

A citation records a reference. It does not transfer a finding from one paper to another.

pith.paper-citation-record.v1
2505.24527 v1

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measured 44 of 44 reference resolution

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44 of 44 outbound references displayed

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Outbound references

Observation 6ccf555e-5aba-477c-bead-d9d781273963 · outbound

This paper cites A new convolution neural layer based on weights constraints.

Optimal Density Functions for Weighted Convolution in Learning Models A new convolution neural layer based on weights constraints

Reference 1

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Observation 5eec7d7d-6987-42ab-ac3b-bac0b210187a · outbound

This paper cites CNN -hyperparameter optimization for diabetic maculopathy diagnosis in optical coherence tomography and fundus retinography.

Optimal Density Functions for Weighted Convolution in Learning Models CNN -hyperparameter optimization for diabetic maculopathy diagnosis in optical coherence tomography and fundus retinography

Reference 2

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Observation c07641d8-4de7-4d27-b8ba-e5fb7290d3de · outbound

This paper cites Dynamic convolution: Attention over convolution kernels.

Optimal Density Functions for Weighted Convolution in Learning Models Dynamic convolution: Attention over convolution kernels

Reference 3

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This paper cites Discrete weighted transforms and large-integer arithmetic.

Optimal Density Functions for Weighted Convolution in Learning Models Discrete weighted transforms and large-integer arithmetic

Reference 4

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Observation 4e948af9-43d2-40d4-a8e4-c19dc51ccfe0 · outbound

This paper cites Convolutional kernel networks for graph-structured data.

Optimal Density Functions for Weighted Convolution in Learning Models Convolutional kernel networks for graph-structured data

Reference 5

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This paper cites An analysis of single-layer networks in unsupervised feature learning.

Optimal Density Functions for Weighted Convolution in Learning Models An analysis of single-layer networks in unsupervised feature learning

Reference 6

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Observation 55117580-d2b2-49fd-adbf-c8ea4bbe68af · outbound

This paper cites Real-time denoising of ultrasound images based on deep learning.

Optimal Density Functions for Weighted Convolution in Learning Models Real-time denoising of ultrasound images based on deep learning

Reference 7

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This paper cites Learning-based low-rank denoising.

Optimal Density Functions for Weighted Convolution in Learning Models Learning-based low-rank denoising

Reference 8

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This paper cites Analysis and comparison of high-performance computing solvers for minimisation problems in signal processing.

Optimal Density Functions for Weighted Convolution in Learning Models Analysis and comparison of high-performance computing solvers for minimisation problems in signal processing

Reference 9

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This paper cites Weighted convolutional neural network ensemble.

Optimal Density Functions for Weighted Convolution in Learning Models Weighted convolutional neural network ensemble

Reference 10

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Observation 8b586baf-a9d8-4c27-83f6-611bf55cc8bf · outbound

This paper cites Self-organizing multilayered neural network.

Optimal Density Functions for Weighted Convolution in Learning Models Self-organizing multilayered neural network

Reference 11

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Observation c9903410-753a-4979-9b10-bf5d8c13f86c · outbound

This paper cites A locally-biased form of the DIRECT algorithm.

Optimal Density Functions for Weighted Convolution in Learning Models A locally-biased form of the DIRECT algorithm

Reference 12

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This paper cites Generalizing the convolution operator in convolutional neural networks.

Optimal Density Functions for Weighted Convolution in Learning Models Generalizing the convolution operator in convolutional neural networks

Reference 13

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This paper cites Weighted channel dropout for regularization of deep convolutional neural network.

Optimal Density Functions for Weighted Convolution in Learning Models Weighted channel dropout for regularization of deep convolutional neural network

Reference 14

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This paper cites Delving deep into rectifiers: Surpassing human-level performance on imagenet classification.

Optimal Density Functions for Weighted Convolution in Learning Models Delving deep into rectifiers: Surpassing human-level performance on imagenet classification

Reference 15

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This paper cites Deep residual learning for image recognition.

Optimal Density Functions for Weighted Convolution in Learning Models Deep residual learning for image recognition

Reference 16

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This paper cites Backpropagation and stochastic gradient descent method.

Optimal Density Functions for Weighted Convolution in Learning Models Backpropagation and stochastic gradient descent method

Reference 17

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This paper cites Variable weight algorithm for convolutional neural networks and its applications to classification of seizure phases and types.

Optimal Density Functions for Weighted Convolution in Learning Models Variable weight algorithm for convolutional neural networks and its applications to classification of seizure phases and types

Reference 18

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This paper cites A new direction adaptive scheme for image interpolation.

Optimal Density Functions for Weighted Convolution in Learning Models A new direction adaptive scheme for image interpolation

Reference 19

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This paper cites Lipschitzian optimization without the L ipschitz constant.

Optimal Density Functions for Weighted Convolution in Learning Models Lipschitzian optimization without the L ipschitz constant

Reference 20

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Optimal Density Functions for Weighted Convolution in Learning Models Resnet 50

Reference 21

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Optimal Density Functions for Weighted Convolution in Learning Models Deep learning in the automotive industry: Applications and tools

Reference 22

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Optimal Density Functions for Weighted Convolution in Learning Models Fast algorithms for convolutional neural networks

Reference 23

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Optimal Density Functions for Weighted Convolution in Learning Models Weighted adaptive lifting-based wavelet transform for image coding

Reference 24

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Optimal Density Functions for Weighted Convolution in Learning Models Omni-dimensional dynamic convolution

Reference 25

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Optimal Density Functions for Weighted Convolution in Learning Models Convolutional kernel networks

Reference 26

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Optimal Density Functions for Weighted Convolution in Learning Models Deep learning in robotics: a review of recent research

Reference 27

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Optimal Density Functions for Weighted Convolution in Learning Models Deep CNN hyperparameter optimization algorithms for sensor-based human activity recognition

Reference 28

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Optimal Density Functions for Weighted Convolution in Learning Models U-net: Convolutional networks for biomedical image segmentation

Reference 29

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Optimal Density Functions for Weighted Convolution in Learning Models Ricker, ormsby, klander, butterworth - a choice of wavelets

Reference 30

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Optimal Density Functions for Weighted Convolution in Learning Models An overview of gradient descent optimization algorithms

Reference 31

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Optimal Density Functions for Weighted Convolution in Learning Models Deep learning with pytorch: Build, train, and tune neural networks using python tools, 2020

Reference 32

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Optimal Density Functions for Weighted Convolution in Learning Models Avoiding overfitting: A survey on regularization methods for convolutional neural networks

Reference 33

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Optimal Density Functions for Weighted Convolution in Learning Models Very deep convolutional networks for large-scale image recognition

Reference 34

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Optimal Density Functions for Weighted Convolution in Learning Models Unresolved cited work

Reference 35

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Optimal Density Functions for Weighted Convolution in Learning Models Catmull-rom splines

Reference 36

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Optimal Density Functions for Weighted Convolution in Learning Models Deep learning for computer vision: A brief review

Reference 37

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Optimal Density Functions for Weighted Convolution in Learning Models Attention is all you need

Reference 38

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Optimal Density Functions for Weighted Convolution in Learning Models Regularization of neural networks using dropconnect

Reference 39

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Optimal Density Functions for Weighted Convolution in Learning Models Weighted support vector machine for data classification

Reference 40

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Optimal Density Functions for Weighted Convolution in Learning Models Kernel-based fuzzy and possibilistic c-means clustering

Reference 41

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Optimal Density Functions for Weighted Convolution in Learning Models Hyperparameter optimization in cnn for learning-centered emotion recognition for intelligent tutoring systems

Reference 42

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Optimal Density Functions for Weighted Convolution in Learning Models A sufficient condition for convergences of adam and rmsprop

Reference 43

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This paper cites Improvement of generalization ability of deep CNN via implicit regularization in two-stage training process.

Optimal Density Functions for Weighted Convolution in Learning Models Improvement of generalization ability of deep CNN via implicit regularization in two-stage training process

Reference 44

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