Paper Citation Record · LEDGER

How to warm-start your unfolding network

As of 14 August 2026, this Paper Citation Record lists 41 of 41 outbound references and 0 inbound Pith citation observations for arXiv:2502.01854.

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

pith.paper-citation-record.v1
2502.01854 v2

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Pith citing papers itemized under the disclosed page cap.

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Reference resolution

41 of 41 outbound references displayed

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External citation measurements

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Outbound references

Observation f836bc26-a9bf-4b05-8c1e-867b9c2a24d2 · outbound

This paper cites AMP- Inspired Deep Networks for Sparse Linear Inverse Prob- lems.

How to warm-start your unfolding network AMP- Inspired Deep Networks for Sparse Linear Inverse Prob- lems

Reference 1

Resolution: verified fuzzy; raw_fallback, observed 2026-08-09T14:18:43.359048Z

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Observation 0a4b0e5e-ff44-4c65-8e28-af99665a1320 · outbound

This paper cites ISTA-Net: Interpretable optimization-inspired deep network for image com- pressive sensing.

How to warm-start your unfolding network ISTA-Net: Interpretable optimization-inspired deep network for image com- pressive sensing

Reference 2

Resolution: verified fuzzy; raw_fallback, observed 2026-08-09T14:18:43.346837Z

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Observation 78a85fa6-1099-4f98-8a12-631e7d8a1c39 · outbound

This paper cites ADMM-CSNet: A deep learning ap- proach for image compressive sensing.

How to warm-start your unfolding network ADMM-CSNet: A deep learning ap- proach for image compressive sensing

Reference 3

Resolution: verified fuzzy; raw_fallback, observed 2026-08-09T14:18:43.334281Z

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Observation 93b79a22-06d3-4d4f-b4dc-53788836d428 · outbound

This paper cites Model-based deep learning.

How to warm-start your unfolding network Model-based deep learning

Reference 4

Resolution: verified fuzzy; raw_fallback, observed 2026-08-09T14:18:43.322279Z

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Observation dcc20eea-885d-421a-aeab-0d14e4d9fe40 · outbound

This paper cites Star DGT: a robust Gabor transform for speech denoising.

How to warm-start your unfolding network Star DGT: a robust Gabor transform for speech denoising

Reference 5

Resolution: verified fuzzy; raw_fallback, observed 2026-08-09T14:18:43.309530Z

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Observation aa5f8340-c4cb-4c08-8809-221a94b970ee · outbound

This paper cites ASCONVSR: Fast and lightweight super- resolution network with assembled convolutions.

How to warm-start your unfolding network ASCONVSR: Fast and lightweight super- resolution network with assembled convolutions

Reference 6

Resolution: verified fuzzy; raw_fallback, observed 2026-08-09T14:18:43.165699Z

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Observation d45ecc26-006c-4ed5-8372-2931f91a6a8a · outbound

This paper cites Pansharpening method based on deep nonlocal unfolding.

How to warm-start your unfolding network Pansharpening method based on deep nonlocal unfolding

Reference 7

Resolution: verified fuzzy; raw_fallback, observed 2026-08-09T14:18:43.060275Z

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Observation d1748ba7-5709-498b-8c5e-451360ee4ea6 · outbound

This paper cites A Deep Proximal-Unfolding Method for Monaural Speech Dereverberation.

How to warm-start your unfolding network A Deep Proximal-Unfolding Method for Monaural Speech Dereverberation

Reference 8

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Observation 04334939-b454-41fa-9bd0-1692966da530 · outbound

This paper cites Model-based compressive sens- ing.

How to warm-start your unfolding network Model-based compressive sens- ing

Reference 9

Resolution: verified fuzzy; raw_fallback, observed 2026-08-09T14:18:42.872039Z

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Observation b52dfdfe-bcae-445c-98e6-0b7e8073512b · outbound

This paper cites Spark Deficient Gabor Frame Provides a Novel Analysis Operator for Compressed Sensing.

How to warm-start your unfolding network Spark Deficient Gabor Frame Provides a Novel Analysis Operator for Compressed Sensing

Reference 10

Resolution: verified fuzzy; raw_fallback, observed 2026-08-09T14:18:42.859169Z

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Observation 0e024168-462e-4c55-9f51-6e5410709971 · outbound

This paper cites DECONET: an Unfolding Network for Analysis-based Compressed Sensing with Generalization Error Bounds.

How to warm-start your unfolding network DECONET: an Unfolding Network for Analysis-based Compressed Sensing with Generalization Error Bounds

Reference 11

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Observation 815dca34-6863-4951-8305-cf85ad98baef · outbound

This paper cites Generalization analysis of an unfolding network for analysis-based Compressed Sensing.

How to warm-start your unfolding network Generalization analysis of an unfolding network for analysis-based Compressed Sensing

Reference 12

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Observation edfbc758-6a05-4e35-b84e-90c1520a8971 · outbound

This paper cites Artificial neural networks for nonlinear regression and classification.

How to warm-start your unfolding network Artificial neural networks for nonlinear regression and classification

Reference 13

Resolution: verified fuzzy; raw_fallback, observed 2026-08-09T14:18:42.842083Z

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Observation 6d52bc8e-38d3-44b6-9a27-0c40bc6437c8 · outbound

This paper cites Dr2-net: Deep residual reconstruction network for image compressive sensing.

How to warm-start your unfolding network Dr2-net: Deep residual reconstruction network for image compressive sensing

Reference 14

Resolution: verified fuzzy; raw_fallback, observed 2026-08-09T14:18:42.823132Z

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Observation 9e24704f-c4be-4628-aa9d-3dfc3b6a6443 · outbound

This paper cites A residual dense u-net neural network for image denois- ing.

How to warm-start your unfolding network A residual dense u-net neural network for image denois- ing

Reference 15

Resolution: verified fuzzy; raw_fallback, observed 2026-08-09T14:18:42.810488Z

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Observation da362893-07da-424b-805d-0ea9a326277d · outbound

This paper cites Designing interpretable recurrent neural networks for video reconstruction via deep un- folding.

How to warm-start your unfolding network Designing interpretable recurrent neural networks for video reconstruction via deep un- folding

Reference 16

Resolution: verified fuzzy; raw_fallback, observed 2026-08-09T14:18:42.794583Z

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Observation e2277403-4df6-4a66-8648-a13e9c38cdd3 · outbound

This paper cites AMP-Net: Denoising-Based Deep Unfolding for Compressive Image Sensing.

How to warm-start your unfolding network AMP-Net: Denoising-Based Deep Unfolding for Compressive Image Sensing

Reference 17

Resolution: verified fuzzy; raw_fallback, observed 2026-08-09T14:18:42.780608Z

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Observation 80a5547f-3d06-4d5f-926f-c314ba70393b · outbound

This paper cites Dynamic path- controllable deep unfolding network for compressive sensing.

How to warm-start your unfolding network Dynamic path- controllable deep unfolding network for compressive sensing

Reference 18

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Observation 57379422-f609-4df4-a46c-ff2019936ca1 · outbound

This paper cites Gates-Controlled Deep Unfolding Network for Image Compressed Sensing.

How to warm-start your unfolding network Gates-Controlled Deep Unfolding Network for Image Compressed Sensing

Reference 19

Resolution: verified fuzzy; raw_fallback, observed 2026-08-09T14:18:42.753642Z

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Observation 3870675e-45d9-48d7-9ad7-fb6b80c92141 · outbound

This paper cites UFC-Net: Unrolling Fixed-point Continuous Network for Deep Compressive Sensing.

How to warm-start your unfolding network UFC-Net: Unrolling Fixed-point Continuous Network for Deep Compressive Sensing

Reference 20

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Observation 88723057-a5ab-416e-91ed-810304203dff · outbound

This paper cites Deep unfolding with normalizing flow priors for inverse problems.

How to warm-start your unfolding network Deep unfolding with normalizing flow priors for inverse problems

Reference 21

Resolution: verified fuzzy; raw_fallback, observed 2026-08-09T14:18:42.574170Z

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Observation ab6b8840-e560-4f2b-bc7d-94527501b476 · outbound

This paper cites A fixed-point continuation method for l1-regularized minimization with applications to compressed sensing.

How to warm-start your unfolding network A fixed-point continuation method for l1-regularized minimization with applications to compressed sensing

Reference 22

Resolution: verified fuzzy; raw_fallback, observed 2026-08-09T14:18:42.430267Z

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Observation c644a98e-3b00-4c22-8233-594dbd2ca042 · outbound

This paper cites Fixed-point contin- uation for ℓ1-minimization: Methodology and conver- gence.

How to warm-start your unfolding network Fixed-point contin- uation for ℓ1-minimization: Methodology and conver- gence

Reference 23

Resolution: verified fuzzy; raw_fallback, observed 2026-08-09T14:18:42.332484Z

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Observation 326f3dd5-461d-43e8-9c7d-a1630b55b46b · outbound

This paper cites NESTA: A fast and accurate first-order method for sparse recovery.

How to warm-start your unfolding network NESTA: A fast and accurate first-order method for sparse recovery

Reference 24

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Observation 67fc63f0-263f-4738-8857-dacb71c1ff5c · outbound

This paper cites Fast ℓ1-Minimization Algorithms for Robust Face Recognition.

How to warm-start your unfolding network Fast ℓ1-Minimization Algorithms for Robust Face Recognition

Reference 25

Resolution: verified fuzzy; raw_fallback, observed 2026-08-09T14:18:42.309734Z

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Observation 175756ab-5e20-4e17-aa84-dce23dc437ef · outbound

This paper cites Warm-start strategies in interior-point methods for linear programming.

How to warm-start your unfolding network Warm-start strategies in interior-point methods for linear programming

Reference 26

Resolution: verified fuzzy; raw_fallback, observed 2026-08-09T14:18:42.297953Z

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Observation 45946119-6375-449c-9570-d12cb960bccb · outbound

This paper cites On warm starts for interior methods.

How to warm-start your unfolding network On warm starts for interior methods

Reference 27

Resolution: verified fuzzy; raw_fallback, observed 2026-08-09T14:18:42.285065Z

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Observation b5c9875b-56d5-4e89-a539-fda653b7fa5b · outbound

This paper cites Templates for convex cone problems with applications to sparse signal recovery.

How to warm-start your unfolding network Templates for convex cone problems with applications to sparse signal recovery

Reference 28

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Observation bab3a3ca-2f6e-43cb-ac2f-babb867163d6 · outbound

This paper cites Comprehensive Examination of Unrolled Networks for Solving Linear Inverse Problems.

How to warm-start your unfolding network Comprehensive Examination of Unrolled Networks for Solving Linear Inverse Problems

Reference 29

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Observation 53f41134-0249-4ddb-a631-00af589375af · outbound

This paper cites Denoiser-Regulated Deep Unfolding Compressed Sensing with Learnable Fixed-Point Pro- jections.

How to warm-start your unfolding network Denoiser-Regulated Deep Unfolding Compressed Sensing with Learnable Fixed-Point Pro- jections

Reference 30

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Observation 572e4047-f169-4737-bc15-eb1dc1014b09 · outbound

This paper cites Generalization error bounds for deep unfolding RNNs.

How to warm-start your unfolding network Generalization error bounds for deep unfolding RNNs

Reference 31

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Observation 1e73e999-c2f8-4753-8a0d-caebd8069bbc · outbound

This paper cites On Generalization Bounds for Deep Compound Gaussian Neural Networks.

How to warm-start your unfolding network On Generalization Bounds for Deep Compound Gaussian Neural Networks

Reference 32

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Observation c79d7a55-ddeb-4f42-be3e-cf2ff1433bbf · outbound

This paper cites A comprehensive survey of regression-based loss functions for time series forecasting.

How to warm-start your unfolding network A comprehensive survey of regression-based loss functions for time series forecasting

Reference 33

Resolution: verified fuzzy; raw_fallback, observed 2026-08-09T14:18:42.233624Z

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Observation ba770263-12f2-48cf-93b8-629fa1823332 · outbound

This paper cites Statistical Properties of the log-cosh Loss Function Used in Machine Learning.

How to warm-start your unfolding network Statistical Properties of the log-cosh Loss Function Used in Machine Learning

Reference 34

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This paper cites Visualizing the loss landscape of neural nets.

How to warm-start your unfolding network Visualizing the loss landscape of neural nets

Reference 35

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Observation 1db7d429-a3a7-424c-8680-1b67f2dcc755 · outbound

This paper cites A deep unfolding method for satellite super resolution.

How to warm-start your unfolding network A deep unfolding method for satellite super resolution

Reference 36

Resolution: verified fuzzy; raw_fallback, observed 2026-08-09T14:18:42.208461Z

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Observation 0e6062f9-d2ed-4555-9e91-8f8cfaf0f8b4 · outbound

This paper cites Dying ReLU and Initialization: Theory and Numerical Examples.

How to warm-start your unfolding network Dying ReLU and Initialization: Theory and Numerical Examples

Reference 37

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This paper cites Introduction to pytorch.

How to warm-start your unfolding network Introduction to pytorch

Reference 38

Resolution: verified fuzzy; raw_fallback, observed 2026-08-09T14:18:42.195569Z

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Observation a52315cf-6dd5-4f1d-952a-d8b9edac3cbe · outbound

This paper cites Adam: A Method for Stochastic Optimization.

How to warm-start your unfolding network Adam: A Method for Stochastic Optimization

Reference 39

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This paper cites Early stopping-but when?.

How to warm-start your unfolding network Early stopping-but when?

Reference 40

Resolution: verified fuzzy; raw_fallback, observed 2026-08-09T14:18:42.088939Z

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Observation 8ad9ca1d-4868-4fec-908d-94bfdbce32fd · outbound

This paper cites Loss landscapes and optimization in over-parameterized non-linear systems and neural networks.

How to warm-start your unfolding network Loss landscapes and optimization in over-parameterized non-linear systems and neural networks

Reference 41

Resolution: verified fuzzy; raw_fallback, observed 2026-08-09T14:18:41.919823Z

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No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.

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