Typed states for the displayed outbound observations.
Source: paper_references, paper_reference_links, observed 2026-08-07T13:13:00.259466Z
Paper Citation Record · LEDGER
Favorability of Loss Landscape with Weight Decay Requires Both Large Overparametrization and Initialization
As of 19 August 2026, this Paper Citation Record lists 60 of 60 outbound references and 0 inbound Pith citation observations for arXiv:2505.22578.
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 2026-08-18T06:34:40.430872+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
60 of 60 outbound references displayed
- verified exact3
- verified fuzzy35
- unresolved21
- parse uncertain0
- malformed identifier0
- metadata mismatch1
External citation measurements
No source-named external measurement is stored.
Outbound references
Observation cf65120c-5e39-42e5-b796-7692035685d2 · outbound
This paper cites Du, Wei Hu, Zhiyuan Li, and Ruosong Wang.
Favorability of Loss Landscape with Weight Decay Requires Both Large Overparametrization and Initialization Du, Wei Hu, Zhiyuan Li, and Ruosong Wang
Reference 1
- Resolution
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Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.
Observation 83dd3c12-b08d-4f3a-81a2-57cb4c270077 · outbound
This paper cites an unresolved cited work.
Favorability of Loss Landscape with Weight Decay Requires Both Large Overparametrization and Initialization Unresolved cited work
Reference 2
- Resolution
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Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.
Observation 74c7d6ca-ef8d-41ef-a13f-d5ba1e3ad86e · outbound
This paper cites Penalising the biases in norm regularisation enforces sparsity https://papers.neurips.cc/paper_files/paper/2023/hash/b444ad72520a5f5c467343be88e352ed-Abstract-Conference.html.
Favorability of Loss Landscape with Weight Decay Requires Both Large Overparametrization and Initialization Penalising the biases in norm regularisation enforces sparsity https://papers.neurips.cc/paper_files/paper/2023/hash/b444ad72520a5f5c467343be88e352ed-Abstract-Conference.html
Reference 3
- Resolution
- verified fuzzy
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Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.
Observation 9c1ef4dc-9322-4aff-a239-1c5c4fb6f1ca · outbound
This paper cites Early alignment in two-layer networks training is a two-edged sword 10.48550/arxiv.2401.10791.
Favorability of Loss Landscape with Weight Decay Requires Both Large Overparametrization and Initialization Early alignment in two-layer networks training is a two-edged sword 10.48550/arxiv.2401.10791
Reference 4
- Resolution
- verified exact
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Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.
Observation f6a9376d-c56c-4135-ae90-47898d9cfd9c · outbound
This paper cites Simplicity bias and optimization threshold in two-layer ReLU networks https://openreview.net/forum?id=qAarsvflTa.
Favorability of Loss Landscape with Weight Decay Requires Both Large Overparametrization and Initialization Simplicity bias and optimization threshold in two-layer ReLU networks https://openreview.net/forum?id=qAarsvflTa
Reference 5
- Resolution
- verified fuzzy
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Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.
Observation 792d70e4-35af-48d8-be78-090179834cc0 · outbound
This paper cites an unresolved cited work.
Favorability of Loss Landscape with Weight Decay Requires Both Large Overparametrization and Initialization Unresolved cited work
Reference 6
- Resolution
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Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.
Observation 8f0db553-e388-4387-8853-a948e9210d09 · outbound
This paper cites How Uniform Random Weights Induce Non-uniform Bias: Typical Interpolating Neural Networks Generalize with Narrow Teachers https://openreview.net/forum?id=3eHNvPHL9Z.
Favorability of Loss Landscape with Weight Decay Requires Both Large Overparametrization and Initialization How Uniform Random Weights Induce Non-uniform Bias: Typical Interpolating Neural Networks Generalize with Narrow Teachers https://openreview.net/forum?id=3eHNvPHL9Z
Reference 7
- Resolution
- verified fuzzy
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Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.
Observation 9b1854d3-34d3-4136-9f96-688a9f2403c1 · outbound
This paper cites Convergence of gradient descent for deep neural networks 10.48550/arxiv.2203.16462.
Favorability of Loss Landscape with Weight Decay Requires Both Large Overparametrization and Initialization Convergence of gradient descent for deep neural networks 10.48550/arxiv.2203.16462
Reference 8
- Resolution
- verified exact
- doi, observed 2026-08-07T13:13:00.843877Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.
Observation 03f525e9-9160-44a3-a045-08d516f2efe6 · outbound
This paper cites Loss Landscapes are All You Need: Neural Network Generalization Can Be Explained Without the Implicit Bias of Gradient Descent https://openreview.net/forum?id=QC10RmRbZy9.
Favorability of Loss Landscape with Weight Decay Requires Both Large Overparametrization and Initialization Loss Landscapes are All You Need: Neural Network Generalization Can Be Explained Without the Implicit Bias of Gradient Descent https://openreview.net/forum?id=QC10RmRbZy9
Reference 9
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-07T13:13:08.001695Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.
Observation 071cdf86-ea16-4c38-a119-33a3a0a780ff · outbound
This paper cites an unresolved cited work.
Favorability of Loss Landscape with Weight Decay Requires Both Large Overparametrization and Initialization Unresolved cited work
Reference 10
- Resolution
- unresolved
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Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.
Observation 38f3a440-21cb-4a86-8869-33e040ebb0ae · outbound
This paper cites an unresolved cited work.
Favorability of Loss Landscape with Weight Decay Requires Both Large Overparametrization and Initialization Unresolved cited work
Reference 11
- Resolution
- unresolved
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Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.
Observation 64cdbab3-38e7-43c0-a369-5bd2ede0cb6c · outbound
This paper cites an unresolved cited work.
Favorability of Loss Landscape with Weight Decay Requires Both Large Overparametrization and Initialization Unresolved cited work
Reference 12
- Resolution
- unresolved
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Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.
Observation e30d8cd1-5d7c-4bdb-91ac-65168652e6dd · outbound
This paper cites an unresolved cited work.
Favorability of Loss Landscape with Weight Decay Requires Both Large Overparametrization and Initialization Unresolved cited work
Reference 13
- Resolution
- unresolved
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Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation b4bd2716-0494-4d02-bfb8-c1da6de70724 · outbound
This paper cites Bach, and Loucas Pillaud - Vivien.
Favorability of Loss Landscape with Weight Decay Requires Both Large Overparametrization and Initialization Bach, and Loucas Pillaud - Vivien
Reference 14
- Resolution
- verified exact
- doi, observed 2026-08-07T13:13:00.630343Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.
Observation e8e52731-abf1-486f-b05d-36faeb95cf96 · outbound
This paper cites an unresolved cited work.
Favorability of Loss Landscape with Weight Decay Requires Both Large Overparametrization and Initialization Unresolved cited work
Reference 15
- Resolution
- unresolved
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Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.
Observation 79819969-13bd-4fc5-b1f9-a5e6b3778476 · outbound
This paper cites Kakade, and Jason D.
Favorability of Loss Landscape with Weight Decay Requires Both Large Overparametrization and Initialization Kakade, and Jason D
Reference 16
- Resolution
- unresolved
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Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation 174a3a10-6587-46e7-8fa9-61538a6fc583 · outbound
This paper cites https://www.jmlr.org/papers/v17/15-408.html CVXPY : A P ython-embedded modeling language for convex optimization.
Favorability of Loss Landscape with Weight Decay Requires Both Large Overparametrization and Initialization https://www.jmlr.org/papers/v17/15-408.html CVXPY : A P ython-embedded modeling language for convex optimization
Reference 17
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-07T13:13:07.256033Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.
Observation 28dc0c1e-59b1-41da-9bd7-e02db3e4f05f · outbound
This paper cites Hamprecht.
Reference 18
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-07T13:13:06.951243Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.
Observation 2e16810a-a1fd-4a45-a54e-25e522cbd06a · outbound
This paper cites Du, Xiyu Zhai, Barnab \' a s P \' o czos, and Aarti Singh.
Favorability of Loss Landscape with Weight Decay Requires Both Large Overparametrization and Initialization Du, Xiyu Zhai, Barnab \' a s P \' o czos, and Aarti Singh
Reference 19
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-07T13:13:06.708498Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.
Observation 0b01b0f9-19ea-4923-afb6-ec89ca45688d · outbound
This paper cites Convex Geometry and Duality of Over-parameterized Neural Networks http://jmlr.org/papers/v22/20-1447.html.
Favorability of Loss Landscape with Weight Decay Requires Both Large Overparametrization and Initialization Convex Geometry and Duality of Over-parameterized Neural Networks http://jmlr.org/papers/v22/20-1447.html
Reference 20
- Resolution
- verified fuzzy
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Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.
Observation b7873b5f-3ba9-4ee0-8a70-c6cd7daa7e7c · outbound
This paper cites An introduction to probability theory and its applications, volume 2.
Favorability of Loss Landscape with Weight Decay Requires Both Large Overparametrization and Initialization An introduction to probability theory and its applications, volume 2
Reference 21
- Resolution
- verified fuzzy
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Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.
Observation 51ada76a-9c7e-49b9-b168-d8626d6c5b38 · outbound
This paper cites Vetrov, and Andrew Gordon Wilson.
Favorability of Loss Landscape with Weight Decay Requires Both Large Overparametrization and Initialization Vetrov, and Andrew Gordon Wilson
Reference 22
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-07T13:13:06.086766Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.
Observation f8d588e8-3ef4-433d-ae93-2cedfb5b5b9a · outbound
This paper cites https://openreview.net/forum?id=HgOJlxzB16 SGD Finds then Tunes Features in Two-Layer Neural Networks with near-Optimal Sample Complexity: A Case Study in the XOR problem.
Favorability of Loss Landscape with Weight Decay Requires Both Large Overparametrization and Initialization https://openreview.net/forum?id=HgOJlxzB16 SGD Finds then Tunes Features in Two-Layer Neural Networks with near-Optimal Sample Complexity: A Case Study in the XOR problem
Reference 23
- Resolution
- verified fuzzy
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Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.
Observation 28a6c832-38c5-4355-9f20-5bb655161c75 · outbound
This paper cites Truth or backpropaganda? An empirical investigation of deep learning theory https://openreview.net/forum?id=HyxyIgHFvr.
Favorability of Loss Landscape with Weight Decay Requires Both Large Overparametrization and Initialization Truth or backpropaganda? An empirical investigation of deep learning theory https://openreview.net/forum?id=HyxyIgHFvr
Reference 24
- Resolution
- verified fuzzy
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Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.
Observation febe0f18-62a5-4f05-a621-8c1522f71328 · outbound
This paper cites Clarabel: An interior-point solver for conic programs with quadratic objectives.
Favorability of Loss Landscape with Weight Decay Requires Both Large Overparametrization and Initialization Clarabel: An interior-point solver for conic programs with quadratic objectives
Reference 25
- Resolution
- unresolved
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Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation a96253ce-1c46-4c1f-b9cc-8169e7197c6b · outbound
This paper cites Haeffele and Ren \' e Vidal.
Favorability of Loss Landscape with Weight Decay Requires Both Large Overparametrization and Initialization Haeffele and Ren \' e Vidal
Reference 26
- Resolution
- unresolved
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Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation 12dd8075-579f-4650-b68b-bc03a8f58350 · outbound
This paper cites Piecewise linear activations substantially shape the loss surfaces of neural networks https://openreview.net/forum?id=B1x6BTEKwr.
Favorability of Loss Landscape with Weight Decay Requires Both Large Overparametrization and Initialization Piecewise linear activations substantially shape the loss surfaces of neural networks https://openreview.net/forum?id=B1x6BTEKwr
Reference 27
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-07T13:13:05.676439Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.
Observation c6928d55-1f75-46db-bb7c-805e90b5bf41 · outbound
This paper cites Deep Residual Learning for Image Recognition 10.1109/cvpr.2016.90.
Favorability of Loss Landscape with Weight Decay Requires Both Large Overparametrization and Initialization Deep Residual Learning for Image Recognition 10.1109/cvpr.2016.90
Reference 28
- Resolution
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Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation 752329bc-34da-4c9c-97ac-02aaa5a0259e · outbound
This paper cites Neural Tangent Kernel: Convergence and Generalization in Neural Networks https://proceedings.neurips.cc/paper/2018/hash/5a4be1fa34e62bb8a6ec6b91d2462f5a-Abstract.html.
Favorability of Loss Landscape with Weight Decay Requires Both Large Overparametrization and Initialization Neural Tangent Kernel: Convergence and Generalization in Neural Networks https://proceedings.neurips.cc/paper/2018/hash/5a4be1fa34e62bb8a6ec6b91d2462f5a-Abstract.html
Reference 29
- Resolution
- verified fuzzy
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Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.
Observation 16283734-4f5a-42f1-bc36-49195400c4fa · outbound
This paper cites an unresolved cited work.
Favorability of Loss Landscape with Weight Decay Requires Both Large Overparametrization and Initialization Unresolved cited work
Reference 30
- Resolution
- unresolved
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Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation f4c597be-e459-448e-9c63-6d5b0870e528 · outbound
This paper cites Mildly Overparameterized ReLU Networks Have a Favorable Loss Landscape https://openreview.net/forum?id=10WARaIwFn.
Favorability of Loss Landscape with Weight Decay Requires Both Large Overparametrization and Initialization Mildly Overparameterized ReLU Networks Have a Favorable Loss Landscape https://openreview.net/forum?id=10WARaIwFn
Reference 31
- Resolution
- verified fuzzy
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Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.
Observation 46685940-4451-4b0e-8239-74ac682d9155 · outbound
This paper cites Deep Learning without Poor Local Minima https://proceedings.neurips.cc/paper/2016/hash/f2fc990265c712c49d51a18a32b39f0c-Abstract.html.
Favorability of Loss Landscape with Weight Decay Requires Both Large Overparametrization and Initialization Deep Learning without Poor Local Minima https://proceedings.neurips.cc/paper/2016/hash/f2fc990265c712c49d51a18a32b39f0c-Abstract.html
Reference 32
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-07T13:13:05.082243Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.
Observation eee97ce2-6b4e-4dad-983b-60b3b5db89fb · outbound
This paper cites Exploring The Loss Landscape Of Regularized Neural Networks Via Convex Duality https://openreview.net/forum?id=4xWQS2z77v.
Favorability of Loss Landscape with Weight Decay Requires Both Large Overparametrization and Initialization Exploring The Loss Landscape Of Regularized Neural Networks Via Convex Duality https://openreview.net/forum?id=4xWQS2z77v
Reference 33
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-07T13:13:04.832441Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.
Observation 2dc68690-56d3-48db-8bf9-d4c0fb558842 · outbound
This paper cites Deep Linear Networks with Arbitrary Loss: All Local Minima Are Global http://proceedings.mlr.press/v80/laurent18a.html.
Favorability of Loss Landscape with Weight Decay Requires Both Large Overparametrization and Initialization Deep Linear Networks with Arbitrary Loss: All Local Minima Are Global http://proceedings.mlr.press/v80/laurent18a.html
Reference 34
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-07T13:13:04.610229Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.
Observation e05247a5-00b8-498d-a4fc-57a01db631b3 · outbound
This paper cites Michaud, and Max Tegmark.
Favorability of Loss Landscape with Weight Decay Requires Both Large Overparametrization and Initialization Michaud, and Max Tegmark
Reference 35
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-07T13:13:04.456563Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.
Observation 79238af5-cf16-4216-898e-969faaaa50b1 · outbound
This paper cites Gradient Descent on Two-layer Nets: Margin Maximization and Simplicity Bias https://proceedings.neurips.cc/paper/2021/hash/6c351da15b5e8a743a21ee96a86e25df-Abstract.html.
Favorability of Loss Landscape with Weight Decay Requires Both Large Overparametrization and Initialization Gradient Descent on Two-layer Nets: Margin Maximization and Simplicity Bias https://proceedings.neurips.cc/paper/2021/hash/6c351da15b5e8a743a21ee96a86e25df-Abstract.html
Reference 36
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-07T13:13:04.326855Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.
Observation 7dcba60d-5130-4eb8-a06f-75bed81ef595 · outbound
This paper cites Lee, and Wei Hu.
Favorability of Loss Landscape with Weight Decay Requires Both Large Overparametrization and Initialization Lee, and Wei Hu
Reference 37
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-07T13:13:04.151036Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.
Observation 7f4f4e97-1a83-4c3e-95aa-f8f1cb223d96 · outbound
This paper cites Gradient Descent Quantizes ReLU Network Features.
Favorability of Loss Landscape with Weight Decay Requires Both Large Overparametrization and Initialization Gradient Descent Quantizes ReLU Network Features
Reference 38
- Resolution
- unresolved
- no resolver link, observed 2026-08-07T13:12:58.348208Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation bb55700f-f0c9-4f44-a56d-d5e518060c56 · outbound
This paper cites A mean field view of the landscape of two-layer neural networks 10.1073/pnas.1806579115.
Favorability of Loss Landscape with Weight Decay Requires Both Large Overparametrization and Initialization A mean field view of the landscape of two-layer neural networks 10.1073/pnas.1806579115
Reference 39
- Resolution
- unresolved
- no resolver link, observed 2026-08-07T13:12:58.426905Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation 349c5a1b-fa92-42ed-b34e-603a66389385 · outbound
This paper cites Early Neuron Alignment in Two-layer ReLU Networks with Small Initialization https://openreview.net/forum?id=QibPzdVrRu.
Favorability of Loss Landscape with Weight Decay Requires Both Large Overparametrization and Initialization Early Neuron Alignment in Two-layer ReLU Networks with Small Initialization https://openreview.net/forum?id=QibPzdVrRu
Reference 40
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-07T13:13:03.976474Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.
Observation 279e937b-4b17-45e4-bad8-8d78436d72b9 · outbound
This paper cites Optimal Sets and Solution Paths of ReLU Networks https://proceedings.mlr.press/v202/mishkin23a.html.
Favorability of Loss Landscape with Weight Decay Requires Both Large Overparametrization and Initialization Optimal Sets and Solution Paths of ReLU Networks https://proceedings.mlr.press/v202/mishkin23a.html
Reference 41
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-07T13:13:03.780828Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.
Observation 14ad19bc-d854-492b-9efd-5753fc7dc9e5 · outbound
This paper cites In Search of the Real Inductive Bias: On the Role of Implicit Regularization in Deep Learning.
Favorability of Loss Landscape with Weight Decay Requires Both Large Overparametrization and Initialization In Search of the Real Inductive Bias: On the Role of Implicit Regularization in Deep Learning
Reference 42
- Resolution
- unresolved
- no resolver link, observed 2026-08-07T13:12:58.738470Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation 7b1bb096-e0cc-491c-b36e-c123b365e76b · outbound
This paper cites On Connected Sublevel Sets in Deep Learning http://proceedings.mlr.press/v97/nguyen19a.html.
Favorability of Loss Landscape with Weight Decay Requires Both Large Overparametrization and Initialization On Connected Sublevel Sets in Deep Learning http://proceedings.mlr.press/v97/nguyen19a.html
Reference 43
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-07T13:13:03.632268Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.
Observation 9331022f-4f6d-48ce-b281-a344eee577ab · outbound
This paper cites A Note on Connectivity of Sublevel Sets in Deep Learning.
Favorability of Loss Landscape with Weight Decay Requires Both Large Overparametrization and Initialization A Note on Connectivity of Sublevel Sets in Deep Learning
Reference 44
- Resolution
- unresolved
- no resolver link, observed 2026-08-07T13:12:58.964229Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation 0d225fce-7953-4a55-ae08-b58b9f4cdc38 · outbound
This paper cites When Are Solutions Connected in Deep Networks? https://proceedings.neurips.cc/paper/2021/hash/af5baf594e9197b43c9f26f17b205e5b-Abstract.html In NeurIPS, pages 20956--20969, 2021.
Favorability of Loss Landscape with Weight Decay Requires Both Large Overparametrization and Initialization When Are Solutions Connected in Deep Networks? https://proceedings.neurips.cc/paper/2021/hash/af5baf594e9197b43c9f26f17b205e5b-Abstract.html In NeurIPS, pages 20956--20969, 2021
Reference 45
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-07T13:13:03.463658Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.
Observation ded24de2-dbb3-4229-b5a3-351500f6c86a · outbound
This paper cites Banach space representer theorems for neural networks and ridge splines https://dl.acm.org/doi/10.5555/3546258.3546301.
Favorability of Loss Landscape with Weight Decay Requires Both Large Overparametrization and Initialization Banach space representer theorems for neural networks and ridge splines https://dl.acm.org/doi/10.5555/3546258.3546301
Reference 46
- Resolution
- metadata mismatch
- raw_fallback, observed 2026-08-07T13:13:01.452769Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.
Observation 027e40ea-cedb-4cb4-bbe5-9e2f036d8106 · outbound
This paper cites Neural Networks are Convex Regularizers: Exact Polynomial-time Convex Optimization Formulations for Two-layer Networks http://proceedings.mlr.press/v119/pilanci20a.html.
Favorability of Loss Landscape with Weight Decay Requires Both Large Overparametrization and Initialization Neural Networks are Convex Regularizers: Exact Polynomial-time Convex Optimization Formulations for Two-layer Networks http://proceedings.mlr.press/v119/pilanci20a.html
Reference 47
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-07T13:13:03.250869Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.
Observation af995fc2-0e66-43c6-827f-2fdf0ae520e5 · outbound
This paper cites Grokking: Generalization Beyond Overfitting on Small Algorithmic Datasets.
Favorability of Loss Landscape with Weight Decay Requires Both Large Overparametrization and Initialization Grokking: Generalization Beyond Overfitting on Small Algorithmic Datasets
Reference 48
- Resolution
- unresolved
- no resolver link, observed 2026-08-07T13:12:59.316611Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation 147fe6d0-4954-400d-b601-98d8cdd4522c · outbound
This paper cites Trainability and accuracy of artificial neural networks: An interacting particle system approach 10.1002/cpa.22074.
Favorability of Loss Landscape with Weight Decay Requires Both Large Overparametrization and Initialization Trainability and accuracy of artificial neural networks: An interacting particle system approach 10.1002/cpa.22074
Reference 49
- Resolution
- unresolved
- no resolver link, observed 2026-08-07T13:12:59.419770Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation a7b54f09-d21b-4f87-9ddd-80e61a9a7a81 · outbound
This paper cites Spurious Local Minima are Common in Two-Layer ReLU Neural Networks http://proceedings.mlr.press/v80/safran18a.html.
Favorability of Loss Landscape with Weight Decay Requires Both Large Overparametrization and Initialization Spurious Local Minima are Common in Two-Layer ReLU Neural Networks http://proceedings.mlr.press/v80/safran18a.html
Reference 50
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-07T13:13:03.111915Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.
Observation f60e7662-4104-42ba-b431-718fd99987c1 · outbound
This paper cites How do infinite width bounded norm networks look in function space? http://proceedings.mlr.press/v99/savarese19a.html In COLT, pages 2667--2690, 2019.
Favorability of Loss Landscape with Weight Decay Requires Both Large Overparametrization and Initialization How do infinite width bounded norm networks look in function space? http://proceedings.mlr.press/v99/savarese19a.html In COLT, pages 2667--2690, 2019
Reference 51
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-07T13:13:02.939431Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.
Observation 52ee293f-9430-491e-915b-45413cc09286 · outbound
This paper cites Understanding machine learning: From theory to algorithms 10.1017/CBO9781107298019.
Favorability of Loss Landscape with Weight Decay Requires Both Large Overparametrization and Initialization Understanding machine learning: From theory to algorithms 10.1017/CBO9781107298019
Reference 52
- Resolution
- unresolved
- no resolver link, observed 2026-08-07T13:12:59.653892Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation 3a176d66-6b8b-45b8-83d3-617a6339a7b5 · outbound
This paper cites Jamaloddin Golestani.
Favorability of Loss Landscape with Weight Decay Requires Both Large Overparametrization and Initialization Jamaloddin Golestani
Reference 53
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-07T13:13:02.705425Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.
Observation 0855d4a2-ac27-4f9b-bfdc-55c07735c983 · outbound
This paper cites Geometry of the Loss Landscape in Overparameterized Neural Networks: Symmetries and Invariances http://proceedings.mlr.press/v139/simsek21a.html.
Favorability of Loss Landscape with Weight Decay Requires Both Large Overparametrization and Initialization Geometry of the Loss Landscape in Overparameterized Neural Networks: Symmetries and Invariances http://proceedings.mlr.press/v139/simsek21a.html
Reference 54
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-07T13:13:02.467783Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.
Observation 8fc4d611-2594-4d34-ab6c-1d366d687115 · outbound
This paper cites The Global Landscape of Neural Networks: An Overview 10.1109/msp.2020.3004124.
Favorability of Loss Landscape with Weight Decay Requires Both Large Overparametrization and Initialization The Global Landscape of Neural Networks: An Overview 10.1109/msp.2020.3004124
Reference 55
- Resolution
- unresolved
- no resolver link, observed 2026-08-07T13:12:59.866232Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation 42386525-5e0c-41b5-b985-efdf1ddaf1e1 · outbound
This paper cites Bandeira, and Joan Bruna.
Favorability of Loss Landscape with Weight Decay Requires Both Large Overparametrization and Initialization Bandeira, and Joan Bruna
Reference 56
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-07T13:13:02.276608Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.
Observation c461e396-90eb-48de-954c-3a24e8ad053d · outbound
This paper cites The Hidden Convex Optimization Landscape of Regularized Two-Layer ReLU Networks: an Exact Characterization of Optimal Solutions https://openreview.net/forum?id=Z7Lk2cQEG8a.
Favorability of Loss Landscape with Weight Decay Requires Both Large Overparametrization and Initialization The Hidden Convex Optimization Landscape of Regularized Two-Layer ReLU Networks: an Exact Characterization of Optimal Solutions https://openreview.net/forum?id=Z7Lk2cQEG8a
Reference 57
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-07T13:13:02.091759Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.
Observation 064d0a4b-9a0b-4ce9-a5ac-6bb0e04fee23 · outbound
This paper cites On the Convergence of Gradient Descent Training for Two-layer ReLU-networks in the Mean Field Regime.
Favorability of Loss Landscape with Weight Decay Requires Both Large Overparametrization and Initialization On the Convergence of Gradient Descent Training for Two-layer ReLU-networks in the Mean Field Regime
Reference 58
- Resolution
- unresolved
- no resolver link, observed 2026-08-07T13:13:00.129590Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation 047842c8-cae0-4b6f-88e8-63aebf6436ef · outbound
This paper cites Woodworth, Suriya Gunasekar, Jason D.
Favorability of Loss Landscape with Weight Decay Requires Both Large Overparametrization and Initialization Woodworth, Suriya Gunasekar, Jason D
Reference 59
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-07T13:13:01.882611Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.
Observation a1e10686-1355-4d63-8293-7c13b534aa61 · outbound
This paper cites Small nonlinearities in activation functions create bad local minima in neural networks https://openreview.net/forum?id=rke\_YiRct7.
Favorability of Loss Landscape with Weight Decay Requires Both Large Overparametrization and Initialization Small nonlinearities in activation functions create bad local minima in neural networks https://openreview.net/forum?id=rke\_YiRct7
Reference 60
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-07T13:13:01.699375Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.
Pith citing papers
No inbound Pith citation observations are available.