Paper Citation Record · LEDGER

A guide to convolution arithmetic for deep learning

As of 14 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 25 inbound Pith citation observations for arXiv:1603.07285.

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

pith.paper-citation-record.v1
1603.07285 v2

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measured 25 of 25 inbound itemization

Pith citing papers itemized under the disclosed page cap.

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Source: pith, observed 2026-07-04T12:39:49.217701Z

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Pith citing papers

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Jointly Aligning and Predicting Continuous Emotion Annotations cites this paper.

Jointly Aligning and Predicting Continuous Emotion Annotations A guide to convolution arithmetic for deep learning

Reference 53

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Understanding Deep Learning Techniques for Image Segmentation cites this paper.

Understanding Deep Learning Techniques for Image Segmentation A guide to convolution arithmetic for deep learning

Reference 53

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Deep Learning for Time Series Forecasting: The Electric Load Case cites this paper.

Deep Learning for Time Series Forecasting: The Electric Load Case A guide to convolution arithmetic for deep learning

Reference 88

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A deep-learning-based surrogate model for data assimilation in dynamic subsurface flow problems cites this paper.

A deep-learning-based surrogate model for data assimilation in dynamic subsurface flow problems A guide to convolution arithmetic for deep learning

Reference 27

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Coarse-to-fine Optimization for Speech Enhancement cites this paper.

Coarse-to-fine Optimization for Speech Enhancement A guide to convolution arithmetic for deep learning

Reference 32

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Feedbackward Decoding for Semantic Segmentation cites this paper.

Feedbackward Decoding for Semantic Segmentation A guide to convolution arithmetic for deep learning

Reference 10

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Learning Sensor Placement from Demonstration for UAV networks cites this paper.

Learning Sensor Placement from Demonstration for UAV networks A guide to convolution arithmetic for deep learning

Reference 6

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Deep learning networks for selection of persistent scatterer pixels in multi-temporal SAR interferometric processing cites this paper.

Deep learning networks for selection of persistent scatterer pixels in multi-temporal SAR interferometric processing A guide to convolution arithmetic for deep learning

Reference 27

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Compression of Higher Order Ambisonics with Multichannel RVQGAN cites this paper.

Compression of Higher Order Ambisonics with Multichannel RVQGAN A guide to convolution arithmetic for deep learning

Reference 29

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A generalised novel loss function for computational fluid dynamics cites this paper.

A generalised novel loss function for computational fluid dynamics A guide to convolution arithmetic for deep learning

Reference 34

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Biologically-inspired Semi-supervised Semantic Segmentation for Biomedical Imaging cites this paper.

Biologically-inspired Semi-supervised Semantic Segmentation for Biomedical Imaging A guide to convolution arithmetic for deep learning

Reference 66

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Relative Layer-Wise Relevance Propagation: a more Robust Neural Networks eXplaination cites this paper.

Relative Layer-Wise Relevance Propagation: a more Robust Neural Networks eXplaination A guide to convolution arithmetic for deep learning

Reference 7

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Mapping Galaxy Images Across Ultraviolet, Visible and Infrared Bands Using Generative Deep Learning cites this paper.

Mapping Galaxy Images Across Ultraviolet, Visible and Infrared Bands Using Generative Deep Learning A guide to convolution arithmetic for deep learning

Reference 5

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A data-driven two-microphone method for in-situ sound absorption measurements cites this paper.

A data-driven two-microphone method for in-situ sound absorption measurements A guide to convolution arithmetic for deep learning

Reference 49

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Bidirectional Variational Autoencoders cites this paper.

Bidirectional Variational Autoencoders A guide to convolution arithmetic for deep learning

Reference 13

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Physics-based Generative Models for Geometrically Consistent and Interpretable Wireless Channel Synthesis cites this paper.

Physics-based Generative Models for Geometrically Consistent and Interpretable Wireless Channel Synthesis A guide to convolution arithmetic for deep learning

Reference 2022

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The model is the message: Lightweight convolutional autoencoders applied to noisy imaging data for planetary science and astrobiology cites this paper.

The model is the message: Lightweight convolutional autoencoders applied to noisy imaging data for planetary science and astrobiology A guide to convolution arithmetic for deep learning

Reference 19

Resolution: unresolved; no resolver link, observed 2026-08-06T17:14:29.410012Z

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Measuring and Predicting Where and When Pathologists Focus their Visual Attention while Grading Whole Slide Images of Cancer cites this paper.

Measuring and Predicting Where and When Pathologists Focus their Visual Attention while Grading Whole Slide Images of Cancer A guide to convolution arithmetic for deep learning

Reference 9

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LTBs-KAN: Linear-Time B-splines Kolmogorov-Arnold Networks cites this paper.

LTBs-KAN: Linear-Time B-splines Kolmogorov-Arnold Networks A guide to convolution arithmetic for deep learning

Reference 35

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Determining star formation histories and age-metallicity relations with convolutional neural networks cites this paper.

Determining star formation histories and age-metallicity relations with convolutional neural networks A guide to convolution arithmetic for deep learning

Reference 21

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Unlocking Realism and Interpretability in Wireless Channel Synthesis: A Physics-Guided Generative Approach cites this paper.

Unlocking Realism and Interpretability in Wireless Channel Synthesis: A Physics-Guided Generative Approach A guide to convolution arithmetic for deep learning

Reference 54

Resolution: verified exact; local_arxiv, observed 2026-07-04T12:39:49.218825Z

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CodeJeNN: A simple C++ neural network generator for physics applications cites this paper.

CodeJeNN: A simple C++ neural network generator for physics applications A guide to convolution arithmetic for deep learning

Reference 23

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CodeJeNN: A simple C++ neural network generator for physics applications cites this paper.

CodeJeNN: A simple C++ neural network generator for physics applications A guide to convolution arithmetic for deep learning

Reference 23

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Aftab: A Comprehensive Benchmark of CNN Encoders and Advanced Value Functions in Parallelized Q-Networks cites this paper.

Aftab: A Comprehensive Benchmark of CNN Encoders and Advanced Value Functions in Parallelized Q-Networks A guide to convolution arithmetic for deep learning

Reference 14

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Aftab: A Comprehensive Benchmark of CNN Encoders and Advanced Value Functions in Parallelized Q-Networks cites this paper.

Aftab: A Comprehensive Benchmark of CNN Encoders and Advanced Value Functions in Parallelized Q-Networks A guide to convolution arithmetic for deep learning

Reference 14

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