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Paper Citation Record · LEDGER
Handbook of Convergence Theorems for (Stochastic) Gradient Methods
As of 9 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 53 inbound Pith citation observations for arXiv:2301.11235.
A citation records a reference. It does not transfer a finding from one paper to another.
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One-hop event checks from named stored sources.
Source: scholarly_work_events, retraction_status_cache, observed 2026-08-08T06:32:00.761636+00:00
Pith citing papers itemized under the disclosed page cap.
Source: paper_references, paper_reference_links, observed 2026-08-07T13:30:24.271122Z
A source-named dated measurement, never combined with another source.
Source: arxiv_reference, observed 2026-08-05T02:28:24.338817Z
Reference resolution
0 of 0 outbound references displayed
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External citation measurements
18
arxiv_reference, observed 2026-08-05T02:28:24.338817Z
Outbound references
No outbound reference observations are available for this paper version.
Pith citing papers
Observation 7d99c6f7-0b53-42b8-a1aa-b86f26760ca8 · inbound
AutoSGD: Automatic Learning Rate Selection for Stochastic Gradient Descent cites this paper.
AutoSGD: Automatic Learning Rate Selection for Stochastic Gradient Descent Handbook of Convergence Theorems for (Stochastic) Gradient Methods
Reference 16
- Resolution
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Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation 698d426a-8526-4e44-ba34-6ff5d0488ef5 · inbound
Scalable Parameter and Memory Efficient Pretraining for LLM: Recent Algorithmic Advances and Benchmarking cites this paper.
Scalable Parameter and Memory Efficient Pretraining for LLM: Recent Algorithmic Advances and Benchmarking Handbook of Convergence Theorems for (Stochastic) Gradient Methods
Reference 8
- Resolution
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Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation 544e4bdd-49e0-4f5d-818c-39c4569ff296 · inbound
On the Convergence Analysis of Muon cites this paper.
On the Convergence Analysis of Muon Handbook of Convergence Theorems for (Stochastic) Gradient Methods
Reference 8
- Resolution
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- arxiv_id, observed 2026-05-19T13:22:19.199856Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.
Observation 328ca2cf-adf4-4ebd-a7fc-9cf9278dd6a1 · inbound
On the Convergence Analysis of Muon cites this paper.
On the Convergence Analysis of Muon Handbook of Convergence Theorems for (Stochastic) Gradient Methods
Reference 2011
- Resolution
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Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation 45e1c47b-d304-4719-9c7a-ae8810cde4e7 · inbound
GORACS: Group-level Optimal Transport-guided Coreset Selection for LLM-based Recommender Systems cites this paper.
GORACS: Group-level Optimal Transport-guided Coreset Selection for LLM-based Recommender Systems Handbook of Convergence Theorems for (Stochastic) Gradient Methods
Reference 14
- Resolution
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Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation 199ce0c3-7b7b-4681-81cf-351d7edea8aa · inbound
Incremental Gradient Descent with Small Epoch Counts is Surprisingly Slow on Ill-Conditioned Problems cites this paper.
Incremental Gradient Descent with Small Epoch Counts is Surprisingly Slow on Ill-Conditioned Problems Handbook of Convergence Theorems for (Stochastic) Gradient Methods
Reference 8
- Resolution
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Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation 72447bda-f0b9-4b19-9b60-85ab3ce312c0 · inbound
Transformative or Conservative? Conservation laws for ResNets and Transformers cites this paper.
Transformative or Conservative? Conservation laws for ResNets and Transformers Handbook of Convergence Theorems for (Stochastic) Gradient Methods
Reference 13
- Resolution
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Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation 617cb15a-46d4-4730-9626-b7d35b9579a1 · inbound
Non-Euclidean dual gradient ascent for entropically regularized linear and semidefinite programming cites this paper.
Non-Euclidean dual gradient ascent for entropically regularized linear and semidefinite programming Handbook of Convergence Theorems for (Stochastic) Gradient Methods
Reference 16
- Resolution
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Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation c7ab788e-7c16-4494-83ce-888e2d083663 · inbound
Memory Savings at What Cost? A Study of Alternatives to Backpropagation cites this paper.
Memory Savings at What Cost? A Study of Alternatives to Backpropagation Handbook of Convergence Theorems for (Stochastic) Gradient Methods
Reference 16
- Resolution
- unresolved
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Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation b2449ce3-0f05-40e2-8874-1415fc310549 · inbound
On the boundedness of the sequence generated by minibatch stochastic gradient descent cites this paper.
On the boundedness of the sequence generated by minibatch stochastic gradient descent Handbook of Convergence Theorems for (Stochastic) Gradient Methods
Reference 5
- Resolution
- unresolved
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Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation 900b19bc-3ea6-4693-8941-ab9d58e7d8f5 · inbound
Optimized methods for composite optimization: a reduction perspective cites this paper.
Optimized methods for composite optimization: a reduction perspective Handbook of Convergence Theorems for (Stochastic) Gradient Methods
Reference 20
- Resolution
- unresolved
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Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation 90e18804-29e9-42d1-b08e-38c0d2c82e50 · inbound
Data Depth as a Risk cites this paper.
Data Depth as a Risk Handbook of Convergence Theorems for (Stochastic) Gradient Methods
Reference 13
- Resolution
- unresolved
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Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation de23c43e-b2ed-4039-ba5d-c8c7ed40feac · inbound
Last-Iterate Complexity of SGD for Convex and Smooth Stochastic Problems cites this paper.
Last-Iterate Complexity of SGD for Convex and Smooth Stochastic Problems Handbook of Convergence Theorems for (Stochastic) Gradient Methods
Reference 7
- Resolution
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Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation 744272d5-fc88-4222-9352-b81651dd1072 · inbound
Stochastic Quantum Hamiltonian Descent cites this paper.
Stochastic Quantum Hamiltonian Descent Handbook of Convergence Theorems for (Stochastic) Gradient Methods
Reference 27
- Resolution
- unresolved
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Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation 49e269f7-cf5e-4578-b7e0-66f94975e58c · inbound
Learning to optimize with guarantees: a complete characterization of linearly convergent algorithms cites this paper.
Learning to optimize with guarantees: a complete characterization of linearly convergent algorithms Handbook of Convergence Theorems for (Stochastic) Gradient Methods
Reference 20
- Resolution
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Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation 0e045065-3d08-4c26-bac7-50393756b7cb · inbound
Constrained free energy minimization for the design of thermal states and stabilizer thermodynamic systems cites this paper.
Constrained free energy minimization for the design of thermal states and stabilizer thermodynamic systems Handbook of Convergence Theorems for (Stochastic) Gradient Methods
Reference 77
- Resolution
- verified exact
- arxiv_id, observed 2026-05-18T23:12:52.857953Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.
Observation b4394677-3d40-4776-9022-79941a13b3bd · inbound
A Sketch-and-Project Analysis of Subsampled Natural Gradient Algorithms cites this paper.
A Sketch-and-Project Analysis of Subsampled Natural Gradient Algorithms Handbook of Convergence Theorems for (Stochastic) Gradient Methods
Reference 18
- Resolution
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Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation a923754c-d293-456b-b6fc-38c0d824e9d0 · inbound
Stochastic versus Deterministic in Stochastic Gradient Descent cites this paper.
Stochastic versus Deterministic in Stochastic Gradient Descent Handbook of Convergence Theorems for (Stochastic) Gradient Methods
Reference 36
- Resolution
- verified exact
- arxiv_id, observed 2026-05-18T20:16:50.172360Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.
Observation ec767131-6bda-4a23-a6bb-eae9d86ff5bf · inbound
Delayed Momentum Aggregation: Communication-efficient Byzantine-robust Federated Learning with Partial Participation cites this paper.
Delayed Momentum Aggregation: Communication-efficient Byzantine-robust Federated Learning with Partial Participation Handbook of Convergence Theorems for (Stochastic) Gradient Methods
Reference 31
- Resolution
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Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation f2710097-b845-44d4-8a0e-cb8e6ffa8dd0 · inbound
Balancing Utility and Privacy: Dynamically Private SGD with Random Projection cites this paper.
Balancing Utility and Privacy: Dynamically Private SGD with Random Projection Handbook of Convergence Theorems for (Stochastic) Gradient Methods
Reference 22
- Resolution
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Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation 55331fa9-9d44-4445-b665-19e2813685fc · inbound
How does the optimizer implicitly bias the model merging loss landscape? cites this paper.
How does the optimizer implicitly bias the model merging loss landscape? Handbook of Convergence Theorems for (Stochastic) Gradient Methods
Reference 3
- Resolution
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Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.
Observation 5e2448c7-14a7-4b21-9eef-2b997c74a859 · inbound
SketchGuard: Scaling Byzantine-Robust Decentralized Federated Learning via Sketch-Based Screening cites this paper.
SketchGuard: Scaling Byzantine-Robust Decentralized Federated Learning via Sketch-Based Screening Handbook of Convergence Theorems for (Stochastic) Gradient Methods
Reference 16
- Resolution
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- arxiv_id, observed 2026-05-18T08:56:08.513607Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.
Observation bfa6aaef-a7dc-4667-9056-3808783bb403 · inbound
Accelerated optimization of measured relative entropies cites this paper.
Accelerated optimization of measured relative entropies Handbook of Convergence Theorems for (Stochastic) Gradient Methods
Reference 2024
- Resolution
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Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation 13e9b960-fb01-4740-8d43-2600f0aeb5c0 · inbound
Design Criteria for SGD Preconditioners: Local Conditioning, Noise Floors, and Basin Stability cites this paper.
Design Criteria for SGD Preconditioners: Local Conditioning, Noise Floors, and Basin Stability Handbook of Convergence Theorems for (Stochastic) Gradient Methods
Reference 10
- Resolution
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Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation 2b744de1-8550-4f23-a8ed-d8d8d8f522f6 · inbound
On the Convergence Rate of LoRA Gradient Descent cites this paper.
On the Convergence Rate of LoRA Gradient Descent Handbook of Convergence Theorems for (Stochastic) Gradient Methods
Reference 3
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Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.
Observation 66b29a9f-f37e-48db-803f-1a75f369b3f7 · inbound
Introduction to optimization methods for training SciML models cites this paper.
Introduction to optimization methods for training SciML models Handbook of Convergence Theorems for (Stochastic) Gradient Methods
Reference 2021
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Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation f03df642-e748-46f7-b925-342af21cc48a · inbound
Efficient Stochastic Optimisation via Sequential Monte Carlo cites this paper.
Efficient Stochastic Optimisation via Sequential Monte Carlo Handbook of Convergence Theorems for (Stochastic) Gradient Methods
Reference 2003
- Resolution
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Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation 56c3f6fe-b240-47c3-a073-bbb6020c755e · inbound
Step-Size Stability in Stochastic Optimization: A Theoretical Perspective cites this paper.
Step-Size Stability in Stochastic Optimization: A Theoretical Perspective Handbook of Convergence Theorems for (Stochastic) Gradient Methods
Reference 2024
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Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation d8442d59-db12-468b-bef2-788f64450555 · inbound
Convergence Rates for Distribution Matching with Sliced Optimal Transport cites this paper.
Convergence Rates for Distribution Matching with Sliced Optimal Transport Handbook of Convergence Theorems for (Stochastic) Gradient Methods
Reference 10
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Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation 66eab807-3379-40e7-b92c-3c90b645fbc3 · inbound
Stochastic Gradient Variational Inference with Price's Gradient Estimator from Bures-Wasserstein to Parameter Space cites this paper.
Stochastic Gradient Variational Inference with Price's Gradient Estimator from Bures-Wasserstein to Parameter Space Handbook of Convergence Theorems for (Stochastic) Gradient Methods
Reference 4
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Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.
Observation 8f298c13-e139-4fd0-9323-925cda206988 · inbound
HTMuon: Improving Muon via Heavy-Tailed Spectral Correction cites this paper.
HTMuon: Improving Muon via Heavy-Tailed Spectral Correction Handbook of Convergence Theorems for (Stochastic) Gradient Methods
Reference 10
- Resolution
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Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.
Observation 6d3f316c-663f-4dcc-9067-96d519977fa4 · inbound
Mini-Batch Stochastic Krasnosel'ski\u\i-Mann Algorithm for Nonexpansive Fixed Point Problems cites this paper.
Mini-Batch Stochastic Krasnosel'ski\u\i-Mann Algorithm for Nonexpansive Fixed Point Problems Handbook of Convergence Theorems for (Stochastic) Gradient Methods
Reference 12
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Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.
Observation 2d3f6564-9dad-4d31-bd73-fcc9fe6d7d8d · inbound
Stochastic Krasnoselskii-Mann Iterations: Convergence without Uniformly Bounded Variance cites this paper.
Stochastic Krasnoselskii-Mann Iterations: Convergence without Uniformly Bounded Variance Handbook of Convergence Theorems for (Stochastic) Gradient Methods
Reference 15
- Resolution
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Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.
Observation cdceee8d-d749-490f-b1db-30d0a30feecd · inbound
Stochastic Krasnoselskii-Mann Iterations: Convergence without Uniformly Bounded Variance cites this paper.
Stochastic Krasnoselskii-Mann Iterations: Convergence without Uniformly Bounded Variance Handbook of Convergence Theorems for (Stochastic) Gradient Methods
Reference 15
- Resolution
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Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.
Observation 8a22ac1c-b7b5-47db-9133-b7641b8c53f7 · inbound
Complex Stochastic Gradient Descent and Directional Bias in Reproducing Kernel Hilbert Spaces cites this paper.
Complex Stochastic Gradient Descent and Directional Bias in Reproducing Kernel Hilbert Spaces Handbook of Convergence Theorems for (Stochastic) Gradient Methods
Reference 21
- Resolution
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Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.
Observation 16a41696-132b-484e-ae80-4b9b3bf8ea75 · inbound
Complex Stochastic Gradient Descent and Directional Bias in Reproducing Kernel Hilbert Spaces cites this paper.
Complex Stochastic Gradient Descent and Directional Bias in Reproducing Kernel Hilbert Spaces Handbook of Convergence Theorems for (Stochastic) Gradient Methods
Reference 21
- Resolution
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Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.
Observation ebff0ad4-eece-49ce-aace-0e1c5d2b1533 · inbound
One Coordinate at a Time: Convergence Guarantees for Rotosolve in Variational Quantum Algorithms cites this paper.
One Coordinate at a Time: Convergence Guarantees for Rotosolve in Variational Quantum Algorithms Handbook of Convergence Theorems for (Stochastic) Gradient Methods
Reference 3
- Resolution
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Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.
Observation 7974660c-7ce3-4e31-b3ad-f9f0b9da8f55 · inbound
Distributed Learning with Adversarial Gradient Perturbations cites this paper.
Distributed Learning with Adversarial Gradient Perturbations Handbook of Convergence Theorems for (Stochastic) Gradient Methods
Reference 11
- Resolution
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Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.
Observation ca3ee808-83d6-46a5-a11c-b9b36d489284 · inbound
Optimal Asymptotic Rates for (Stochastic) Gradient Descent under the Local PL-Condition: A Geometric Approach cites this paper.
Optimal Asymptotic Rates for (Stochastic) Gradient Descent under the Local PL-Condition: A Geometric Approach Handbook of Convergence Theorems for (Stochastic) Gradient Methods
Reference 11
- Resolution
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- arxiv_id, observed 2026-07-01T14:35:47.508979Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.
Observation 632a77a5-7b93-4314-8d36-786b9cccb42d · inbound
Accelerated Gradient Descent for Faster Convergence with Minimal Overhead cites this paper.
Accelerated Gradient Descent for Faster Convergence with Minimal Overhead Handbook of Convergence Theorems for (Stochastic) Gradient Methods
Reference 9
- Resolution
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Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.
Observation 3dc27a8b-1550-4f10-9b69-21f2f25cf622 · inbound
COOPO: Cyclic Offline-Online Policy Optimization Algorithm cites this paper.
COOPO: Cyclic Offline-Online Policy Optimization Algorithm Handbook of Convergence Theorems for (Stochastic) Gradient Methods
Reference 45
- Resolution
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Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.
Observation 82d6e872-7d86-45fa-9e76-7572671ffc82 · inbound
Factor Augmented High-Dimensional SGD cites this paper.
Factor Augmented High-Dimensional SGD Handbook of Convergence Theorems for (Stochastic) Gradient Methods
Reference 55
- Resolution
- verified exact
- arxiv_id, observed 2026-05-20T03:33:01.622592Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.
Observation 2bd3db27-9633-4171-a764-620190520e56 · inbound
Randomized conjugate gradient least squares cites this paper.
Randomized conjugate gradient least squares Handbook of Convergence Theorems for (Stochastic) Gradient Methods
Reference 10
- Resolution
- verified exact
- arxiv_id, observed 2026-06-29T23:54:03.385731Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.
Observation 1b0e3681-920a-4b03-9701-11d6239cee02 · inbound
Accelerated Dynamic Importance Weighting with Versatile Divergence-Minimizing Estimators cites this paper.
Accelerated Dynamic Importance Weighting with Versatile Divergence-Minimizing Estimators Handbook of Convergence Theorems for (Stochastic) Gradient Methods
Reference 52
- Resolution
- verified exact
- arxiv_id, observed 2026-06-29T22:54:01.204163Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.
Observation 6695fc90-989c-437a-baa7-8fb5559334dc · inbound
On subspace-constrained preconditioning for randomized iterative methods cites this paper.
On subspace-constrained preconditioning for randomized iterative methods Handbook of Convergence Theorems for (Stochastic) Gradient Methods
Reference 24
- Resolution
- verified exact
- arxiv_id, observed 2026-06-29T06:23:08.773250Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.
Observation 1a808a56-e34a-4b8b-8ef7-2bbf15fe32fd · inbound
FOAM: Frequency and Operator Error-Based Adaptive Damping Method for Reducing Staleness-Oriented Error for Shampoo cites this paper.
FOAM: Frequency and Operator Error-Based Adaptive Damping Method for Reducing Staleness-Oriented Error for Shampoo Handbook of Convergence Theorems for (Stochastic) Gradient Methods
Reference 10
- Resolution
- verified exact
- arxiv_id, observed 2026-07-01T22:06:16.042460Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.
Observation d1c42a26-9462-4ae7-99c2-56c373c2af72 · inbound
Stochastic Gradient Optimization with Model-Assisted Sampling cites this paper.
Stochastic Gradient Optimization with Model-Assisted Sampling Handbook of Convergence Theorems for (Stochastic) Gradient Methods
Reference 11
- Resolution
- verified exact
- arxiv_id, observed 2026-07-04T13:39:50.339832Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.
Observation dbb51a75-21fa-4ffc-bd86-09dffbcc52d1 · inbound
Sharp $O(1/k)$ convergence rate for the Sinkhorn algorithm via a local analysis cites this paper.
Sharp $O(1/k)$ convergence rate for the Sinkhorn algorithm via a local analysis Handbook of Convergence Theorems for (Stochastic) Gradient Methods
Reference 35
- Resolution
- verified exact
- arxiv_id, observed 2026-06-30T08:44:27.699688Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.
Observation 9bd78cbd-250d-4bf3-9ae1-c4686a3402a6 · inbound
How AI settled the complexity of the oldest SGD algorithm cites this paper.
How AI settled the complexity of the oldest SGD algorithm Handbook of Convergence Theorems for (Stochastic) Gradient Methods
Reference 17
- Resolution
- verified exact
- arxiv_id, observed 2026-06-30T07:24:21.394540Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.
Observation 95dd39ea-cd2d-4dbf-a2e3-6cbaec5d2b4f · inbound
Highly Data Parallelizable Estimation of the Sliced-Wasserstein Distance Using Cumulative Distribution Functions cites this paper.
Highly Data Parallelizable Estimation of the Sliced-Wasserstein Distance Using Cumulative Distribution Functions Handbook of Convergence Theorems for (Stochastic) Gradient Methods
Reference 2
- Resolution
- verified exact
- arxiv_id, observed 2026-06-30T04:14:18.916838Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.
Observation ba7afe21-f279-4a8a-93f5-8f4ed6f3413b · inbound
Random Reshuffling Dominates Stochastic Gradient Descent cites this paper.
Random Reshuffling Dominates Stochastic Gradient Descent Handbook of Convergence Theorems for (Stochastic) Gradient Methods
Reference 9
- Resolution
- verified exact
- arxiv_id, observed 2026-07-01T11:55:42.295919Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.
Observation 9b8f8eed-f8a1-4468-a5e1-5111f3cc020d · inbound
Effective dynamics of the Sinkhorn algorithm in the regime of low entropy regularization cites this paper.
Effective dynamics of the Sinkhorn algorithm in the regime of low entropy regularization Handbook of Convergence Theorems for (Stochastic) Gradient Methods
Reference 35
- Resolution
- verified exact
- arxiv_id, observed 2026-07-02T08:06:47.591600Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.
Observation d8bccd32-aa24-4f55-85b3-77628c9dc6b1 · inbound
Unified convergence analysis for gradient descent optimization methods in the training of deep neural networks cites this paper.
Unified convergence analysis for gradient descent optimization methods in the training of deep neural networks Handbook of Convergence Theorems for (Stochastic) Gradient Methods
Reference 26
- Resolution
- unresolved
- no resolver link, observed 2026-07-11T20:46:05.467029Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.