Paper Citation Record · LEDGER

MNIST-Gen: A Modular MNIST-Style Dataset Generation Using Hierarchical Semantics, Reinforcement Learning, and Category Theory

As of 10 August 2026, this Paper Citation Record lists 31 of 31 outbound references and 0 inbound Pith citation observations for arXiv:2507.11821.

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

pith.paper-citation-record.v1
2507.11821 v1

Coverage vector

measured 31 of 31 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-06T17:05:52.584018Z

measured 31 of 31 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-09T06:31:02.800959+00:00

measured 0 of 0 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links

measured 0 of 1 external citation measurements

A source-named dated measurement, never combined with another source.

Source: cited_works

Reference resolution

31 of 31 outbound references displayed

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External citation measurements

No source-named external measurement is stored.

Outbound references

Observation 647d8e5c-fc93-4dbb-8968-223f1cac2f85 · outbound

This paper cites Gradient-based learning applied to document recognition,.

MNIST-Gen: A Modular MNIST-Style Dataset Generation Using Hierarchical Semantics, Reinforcement Learning, and Category Theory Gradient-based learning applied to document recognition,

Reference 1

Resolution: unresolved; no resolver link, observed 2026-08-06T17:05:52.510037Z

Source-reported events for the cited work

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Observation 6c6657fd-c339-418c-bf31-8f235f8df38c · outbound

This paper cites Fashion-MNIST: a Novel Image Dataset for Benchmarking Machine Learning Algorithms.

MNIST-Gen: A Modular MNIST-Style Dataset Generation Using Hierarchical Semantics, Reinforcement Learning, and Category Theory Fashion-MNIST: a Novel Image Dataset for Benchmarking Machine Learning Algorithms

Reference 2

Resolution: unresolved; no resolver link, observed 2026-08-06T17:05:52.513338Z

Source-reported events for the cited work

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Observation 56e3f093-4faa-432a-a5b0-7dcb669d4351 · outbound

This paper cites Revisiting unreasonable effectiveness of data in deep learning era,.

MNIST-Gen: A Modular MNIST-Style Dataset Generation Using Hierarchical Semantics, Reinforcement Learning, and Category Theory Revisiting unreasonable effectiveness of data in deep learning era,

Reference 3

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T17:05:52.964141Z

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Observation 8c38be9e-ba7e-4fe1-be06-6ebbb45cd69b · outbound

This paper cites Food-101–mining dis- criminative components with random forests,.

MNIST-Gen: A Modular MNIST-Style Dataset Generation Using Hierarchical Semantics, Reinforcement Learning, and Category Theory Food-101–mining dis- criminative components with random forests,

Reference 4

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T17:05:52.956589Z

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Observation 8a6c15e7-a4ab-43c3-8b34-ff67e60ac44c · outbound

This paper cites Fine-grained plant classification using convolutional neural networks for feature extraction.

MNIST-Gen: A Modular MNIST-Style Dataset Generation Using Hierarchical Semantics, Reinforcement Learning, and Category Theory Fine-grained plant classification using convolutional neural networks for feature extraction

Reference 5

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T17:05:52.948932Z

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Observation 9c396ffe-aa18-456e-9fc9-30ac4daeefcd · outbound

This paper cites MID-L: Matrix-Interpolated Dropout Layer with Layer-wise Neuron Selection.

MNIST-Gen: A Modular MNIST-Style Dataset Generation Using Hierarchical Semantics, Reinforcement Learning, and Category Theory MID-L: Matrix-Interpolated Dropout Layer with Layer-wise Neuron Selection

Reference 6

Resolution: verified exact; local_arxiv, observed 2026-08-06T17:05:52.774100Z

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Observation 33ffc3f5-c4f0-469f-bb7a-631233233a3c · outbound

This paper cites Learning visual features from large weakly supervised data,.

MNIST-Gen: A Modular MNIST-Style Dataset Generation Using Hierarchical Semantics, Reinforcement Learning, and Category Theory Learning visual features from large weakly supervised data,

Reference 7

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T17:05:52.941817Z

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Observation 40cc66c9-b529-4509-9b6f-c76405c7c404 · outbound

This paper cites A semi-supervised fake news detection using sentiment encoding and lstm with self-attention,.

MNIST-Gen: A Modular MNIST-Style Dataset Generation Using Hierarchical Semantics, Reinforcement Learning, and Category Theory A semi-supervised fake news detection using sentiment encoding and lstm with self-attention,

Reference 8

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T17:05:52.934235Z

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Observation 210be689-e255-4a44-96d9-64bdfdf24c9f · outbound

This paper cites A new chebyshev operational matrix formulation of least-squares support vector regression for solving fractional integro-differential equations,.

MNIST-Gen: A Modular MNIST-Style Dataset Generation Using Hierarchical Semantics, Reinforcement Learning, and Category Theory A new chebyshev operational matrix formulation of least-squares support vector regression for solving fractional integro-differential equations,

Reference 9

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T17:05:52.926674Z

Source-reported events for the cited work

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Observation 31349af9-8d7f-485c-8a5e-1b70ac9e67ea · outbound

This paper cites A machine learning framework for efficiently solving fokker–planck equations,.

MNIST-Gen: A Modular MNIST-Style Dataset Generation Using Hierarchical Semantics, Reinforcement Learning, and Category Theory A machine learning framework for efficiently solving fokker–planck equations,

Reference 10

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T17:05:52.918985Z

Source-reported events for the cited work

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Observation 44d82cc2-ed2a-432d-b321-828d8eb2df6c · outbound

This paper cites Emnist: Extending mnist to handwritten letters,.

MNIST-Gen: A Modular MNIST-Style Dataset Generation Using Hierarchical Semantics, Reinforcement Learning, and Category Theory Emnist: Extending mnist to handwritten letters,

Reference 11

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T17:05:52.911325Z

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Observation 50a4ac2d-b8b0-4a94-8306-94908889d053 · outbound

This paper cites Sentiment and social signals in the climate crisis: A survey on analyzing social media responses to extreme weather events,.

MNIST-Gen: A Modular MNIST-Style Dataset Generation Using Hierarchical Semantics, Reinforcement Learning, and Category Theory Sentiment and social signals in the climate crisis: A survey on analyzing social media responses to extreme weather events,

Reference 12

Resolution: verified exact; raw_fallback, observed 2026-08-06T17:05:52.762842Z

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Observation 8a4e5e74-d8f9-4f9d-a20e-847d616b4f34 · outbound

This paper cites Can LLMs Improve Multimodal Fact-Checking by Asking Relevant Questions?.

MNIST-Gen: A Modular MNIST-Style Dataset Generation Using Hierarchical Semantics, Reinforcement Learning, and Category Theory Can LLMs Improve Multimodal Fact-Checking by Asking Relevant Questions?

Reference 13

Resolution: verified exact; local_arxiv, observed 2026-08-06T17:05:52.633566Z

Source-reported events for the cited work

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Observation 47b9b66d-884e-45e7-99b5-2994e611bdb0 · outbound

This paper cites A multimodal physics-informed neural network approach for mean radiant temperature modeling,.

MNIST-Gen: A Modular MNIST-Style Dataset Generation Using Hierarchical Semantics, Reinforcement Learning, and Category Theory A multimodal physics-informed neural network approach for mean radiant temperature modeling,

Reference 14

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T17:05:52.904022Z

Source-reported events for the cited work

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Observation 667d6aef-ee68-4c31-89e6-99ab0a639029 · outbound

This paper cites Webmrt: An online tool to predict summertime mean radiant tempera- ture using machine learning,.

MNIST-Gen: A Modular MNIST-Style Dataset Generation Using Hierarchical Semantics, Reinforcement Learning, and Category Theory Webmrt: An online tool to predict summertime mean radiant tempera- ture using machine learning,

Reference 15

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T17:05:52.896483Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation ad74a0f2-0970-4742-a471-cd61ddd4250c · outbound

This paper cites Urban form and composition of street canyons: A human-centric big data and deep learning approach,.

MNIST-Gen: A Modular MNIST-Style Dataset Generation Using Hierarchical Semantics, Reinforcement Learning, and Category Theory Urban form and composition of street canyons: A human-centric big data and deep learning approach,

Reference 16

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T17:05:52.888633Z

Source-reported events for the cited work

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Observation 77c5f577-fdef-4ecd-8974-936da325e66e · outbound

This paper cites Snorkel: Rapid training data creation with weak supervision,.

MNIST-Gen: A Modular MNIST-Style Dataset Generation Using Hierarchical Semantics, Reinforcement Learning, and Category Theory Snorkel: Rapid training data creation with weak supervision,

Reference 17

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T17:05:52.880401Z

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Observation 467ee9ad-ca45-4f58-851c-a5a4fa413bc3 · outbound

This paper cites Label studio: Open-source data labeling tool,.

MNIST-Gen: A Modular MNIST-Style Dataset Generation Using Hierarchical Semantics, Reinforcement Learning, and Category Theory Label studio: Open-source data labeling tool,

Reference 18

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T17:05:52.872597Z

Source-reported events for the cited work

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Observation bf7b007c-605f-4ca6-8f2a-1af00403c26b · outbound

This paper cites Google automl: cloud vision,.

MNIST-Gen: A Modular MNIST-Style Dataset Generation Using Hierarchical Semantics, Reinforcement Learning, and Category Theory Google automl: cloud vision,

Reference 19

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T17:05:52.865014Z

Source-reported events for the cited work

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Observation f09182c3-7299-4980-ad91-ae7b8b2c9d7b · outbound

This paper cites Nvidia tao toolkit,.

MNIST-Gen: A Modular MNIST-Style Dataset Generation Using Hierarchical Semantics, Reinforcement Learning, and Category Theory Nvidia tao toolkit,

Reference 20

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T17:05:52.857579Z

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Observation 691878f3-f26d-41cf-b787-d9c0e7b2d1b4 · outbound

This paper cites A survey of data collection methods for machine learning,.

MNIST-Gen: A Modular MNIST-Style Dataset Generation Using Hierarchical Semantics, Reinforcement Learning, and Category Theory A survey of data collection methods for machine learning,

Reference 21

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T17:05:52.850038Z

Source-reported events for the cited work

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Observation 8555375e-12c2-4d15-9e98-24c14e00ceb6 · outbound

This paper cites DocParseNet: Advanced Semantic Segmentation and OCR Embeddings for Efficient Scanned Document Annotation.

MNIST-Gen: A Modular MNIST-Style Dataset Generation Using Hierarchical Semantics, Reinforcement Learning, and Category Theory DocParseNet: Advanced Semantic Segmentation and OCR Embeddings for Efficient Scanned Document Annotation

Reference 22

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Observation aeda628b-aefb-4254-be88-cf8cc31a58e0 · outbound

This paper cites Learning transferable visual models from natural language supervision,.

MNIST-Gen: A Modular MNIST-Style Dataset Generation Using Hierarchical Semantics, Reinforcement Learning, and Category Theory Learning transferable visual models from natural language supervision,

Reference 23

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Observation 0d1bfb69-2947-4ca8-87b8-9f14f4b1b9d6 · outbound

This paper cites Fong and D.

MNIST-Gen: A Modular MNIST-Style Dataset Generation Using Hierarchical Semantics, Reinforcement Learning, and Category Theory Fong and D

Reference 24

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T17:05:52.837532Z

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Observation f5e6ed47-89a8-404b-ac26-d9f9f6f9e23a · outbound

This paper cites Level- k reasoning, deep rein- forcement learning, and monte carlo decision process for fast and safe automated lane change and speed management,.

MNIST-Gen: A Modular MNIST-Style Dataset Generation Using Hierarchical Semantics, Reinforcement Learning, and Category Theory Level- k reasoning, deep rein- forcement learning, and monte carlo decision process for fast and safe automated lane change and speed management,

Reference 25

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T17:05:52.829782Z

Source-reported events for the cited work

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Observation 75b2537f-8b9c-42f8-8718-6c3e38258df3 · outbound

This paper cites Learning to reweight ex- amples for robust deep learning,.

MNIST-Gen: A Modular MNIST-Style Dataset Generation Using Hierarchical Semantics, Reinforcement Learning, and Category Theory Learning to reweight ex- amples for robust deep learning,

Reference 26

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T17:05:52.821382Z

Source-reported events for the cited work

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Observation b02e19e2-7850-47d7-a575-7f7f207a4472 · outbound

This paper cites Deliberate practice in data selection for efficient learning,.

MNIST-Gen: A Modular MNIST-Style Dataset Generation Using Hierarchical Semantics, Reinforcement Learning, and Category Theory Deliberate practice in data selection for efficient learning,

Reference 27

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T17:05:52.813619Z

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Observation bc6a687c-2cc1-45ab-aa64-a728a5ebb05a · outbound

This paper cites Adaptive data selection for labeling via reinforcement learning,.

MNIST-Gen: A Modular MNIST-Style Dataset Generation Using Hierarchical Semantics, Reinforcement Learning, and Category Theory Adaptive data selection for labeling via reinforcement learning,

Reference 28

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T17:05:52.805984Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation 5821e71a-7176-48dd-a254-d61900f1125f · outbound

This paper cites U2-net: Going deeper with nested u-structure for salient object detection,.

MNIST-Gen: A Modular MNIST-Style Dataset Generation Using Hierarchical Semantics, Reinforcement Learning, and Category Theory U2-net: Going deeper with nested u-structure for salient object detection,

Reference 29

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T17:05:52.798500Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation c93a205f-2364-433d-82d1-4355f345a6de · outbound

This paper cites Food/non-food image classifi- cation and food categorization using pre-trained googlenet model,.

MNIST-Gen: A Modular MNIST-Style Dataset Generation Using Hierarchical Semantics, Reinforcement Learning, and Category Theory Food/non-food image classifi- cation and food categorization using pre-trained googlenet model,

Reference 30

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T17:05:52.790441Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation eff12eb5-1e63-4c16-bf79-8931d62a4292 · outbound

This paper cites A Multimodal Physics-Informed Neural Network Approach for Mean Radiant Temperature Modeling.

MNIST-Gen: A Modular MNIST-Style Dataset Generation Using Hierarchical Semantics, Reinforcement Learning, and Category Theory A Multimodal Physics-Informed Neural Network Approach for Mean Radiant Temperature Modeling

Reference 2025

Resolution: unresolved; no resolver link, observed 2026-08-06T17:05:52.547006Z

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Pith citing papers

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