Typed states for the displayed outbound observations.
Source: paper_references, paper_reference_links, observed 2026-08-09T14:18:41.459635Z
Paper Citation Record · LEDGER
How to warm-start your unfolding network
As of 16 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.
Coverage vector
One-hop event checks from named stored sources.
Source: scholarly_work_events, retraction_status_cache, observed 2026-08-16T06:30:59.297886+00:00
Pith citing papers itemized under the disclosed page cap.
Source: paper_references, paper_reference_links
A source-named dated measurement, never combined with another source.
Source: cited_works
Reference resolution
41 of 41 outbound references displayed
- verified exact3
- verified fuzzy34
- unresolved3
- parse uncertain0
- malformed identifier1
- metadata mismatch0
External citation measurements
No source-named external measurement is stored.
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
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.
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
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.
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
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.
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
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.
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
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.
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
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.
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
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.
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
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-09T14:18:42.895994Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.
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
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.
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
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.
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
- Resolution
- malformed identifier
- arxiv_id_nonexistent, observed 2026-08-09T14:18:41.806139Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.
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
- Resolution
- verified exact
- local_arxiv, observed 2026-08-09T14:18:41.568152Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.
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
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.
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
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.
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
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.
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
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.
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
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.
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
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-09T14:18:42.765274Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.
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
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.
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
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-09T14:18:42.740359Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.
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
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.
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
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.
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
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.
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
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-09T14:18:42.321139Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.
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
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.
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
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.
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
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.
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
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-09T14:18:42.270969Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.
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
- Resolution
- verified exact
- local_arxiv, observed 2026-08-09T14:18:41.550065Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.
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
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-09T14:18:42.259753Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.
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
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-09T14:18:42.245579Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.
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
- Resolution
- verified exact
- local_arxiv, observed 2026-08-09T14:18:41.531130Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.
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
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.
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
- Resolution
- unresolved
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Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation adce2174-639c-4f2e-a356-8c4e258c6085 · outbound
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
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-09T14:18:42.220336Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.
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
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.
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
- Resolution
- unresolved
- no resolver link, observed 2026-08-09T14:18:41.445166Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation 7c72aaab-7229-4072-95b0-384445b6fdbb · outbound
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
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.
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
- Resolution
- unresolved
- no resolver link, observed 2026-08-09T14:18:41.452099Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation 1017872c-b0d8-408f-8a95-7839ccbaff85 · outbound
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
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.
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
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.
Pith citing papers
No inbound Pith citation observations are available.