Paper Citation Record · LEDGER

The Indirect Convolution Algorithm

As of 20 August 2026, this Paper Citation Record lists 36 of 36 outbound references and 3 inbound Pith citation observations for arXiv:1907.02129.

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

pith.paper-citation-record.v1
1907.02129 v1

Coverage vector

measured 36 of 36 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-05-25T10:02:28.469463Z

measured 39 of 39 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-20T06:33:59.587034+00:00

measured 3 of 3 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-11T12:44:45.384791Z

measured 0 of 1 external citation measurements

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

Source: pith, observed 2026-08-06T14:55:28.623318Z

Reference resolution

36 of 36 outbound references displayed

verified exact10
verified fuzzy25
unresolved1
parse uncertain0
malformed identifier0
metadata mismatch0

External citation measurements

No source-named external measurement is stored.

Outbound references

Observation 019e28a5-c62a-4912-9fd3-1f49b4da0f3b · outbound

This paper cites Tensorﬂow: A system for large-scale machine learning.

The Indirect Convolution Algorithm Tensorﬂow: A system for large-scale machine learning

Reference 1

Resolution: verified fuzzy; raw_fallback, observed 2026-05-25T10:05:37.707256Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

Observation 7a2c56eb-8194-4ea3-bdec-28dbfa2225f1 · outbound

This paper cites Low-memory GEMM-based convolution algorithms for deep neural networks.

The Indirect Convolution Algorithm Low-memory GEMM-based convolution algorithms for deep neural networks

Reference 2

Resolution: verified exact; local_arxiv, observed 2026-05-25T10:05:36.950130Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

Observation 259de2c0-2068-4f4d-94f3-0aa08b6cf8eb · outbound

This paper cites High per- formance convolutional neural networks for document pro- cessing.

The Indirect Convolution Algorithm High per- formance convolutional neural networks for document pro- cessing

Reference 3

Resolution: verified fuzzy; raw_fallback, observed 2026-05-25T10:05:37.714980Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

Observation 567e16c1-98ba-4db0-a51e-af398aaa49c7 · outbound

This paper cites Encoder-decoder with atrous separable convolution for semantic image segmentation.

The Indirect Convolution Algorithm Encoder-decoder with atrous separable convolution for semantic image segmentation

Reference 4

Resolution: verified fuzzy; raw_fallback, observed 2026-05-25T10:05:37.719145Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

Observation d218b9ff-056b-4b9b-ae9f-e118d7821ad2 · outbound

This paper cites {TVM}: An automated end-to-end optimizing compiler for deep learning.

The Indirect Convolution Algorithm {TVM}: An automated end-to-end optimizing compiler for deep learning

Reference 5

Resolution: verified fuzzy; raw_fallback, observed 2026-05-25T10:05:37.722880Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

Observation 43334afb-5117-4838-84c2-ea2050ace2e2 · outbound

This paper cites cuDNN: Efficient Primitives for Deep Learning.

The Indirect Convolution Algorithm cuDNN: Efficient Primitives for Deep Learning

Reference 6

Resolution: verified exact; local_arxiv, observed 2026-05-25T10:05:36.955808Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

Observation 720dfd14-b7be-4906-9c6a-542ba1120cae · outbound

This paper cites Mec: memory-efﬁcient con- volution for deep neural network.

The Indirect Convolution Algorithm Mec: memory-efﬁcient con- volution for deep neural network

Reference 7

Resolution: verified fuzzy; raw_fallback, observed 2026-05-25T10:05:37.696087Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

Observation 4cb4399a-56dd-494a-b3f7-79b7a1759456 · outbound

This paper cites Xception: Deep learning with depthwise separable convolutions.

The Indirect Convolution Algorithm Xception: Deep learning with depthwise separable convolutions

Reference 8

Resolution: verified fuzzy; raw_fallback, observed 2026-05-25T10:05:37.711266Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

Observation 242d9a4e-daa8-4878-8fdf-e74f1e57f091 · outbound

This paper cites Language modeling with gated convolutional net- works.

The Indirect Convolution Algorithm Language modeling with gated convolutional net- works

Reference 9

Resolution: verified fuzzy; raw_fallback, observed 2026-05-25T10:05:37.730144Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

Observation 633b5489-78c9-4c06-8288-0ff5d2d014e1 · outbound

This paper cites QNNPACK: open source library for optimized mobile deep learn- ing.

The Indirect Convolution Algorithm QNNPACK: open source library for optimized mobile deep learn- ing

Reference 10

Resolution: verified fuzzy; raw_fallback, observed 2026-05-25T10:05:37.726591Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

Observation 7fe8d467-35e8-433c-9aff-eb9225ff424a · outbound

This paper cites Anatomy of high-performance deep learning con- volutions on simd architectures.

The Indirect Convolution Algorithm Anatomy of high-performance deep learning con- volutions on simd architectures

Reference 11

Resolution: verified fuzzy; raw_fallback, observed 2026-05-25T10:05:37.699680Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

Observation 29871af8-36f0-452d-bd70-68ca1cd5f4af · outbound

This paper cites Anatomy of high- performance matrix multiplication.

The Indirect Convolution Algorithm Anatomy of high- performance matrix multiplication

Reference 12

Resolution: verified fuzzy; raw_fallback, observed 2026-05-25T10:05:37.703624Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

Observation f60e1c61-34f8-4814-a255-8715e182ef63 · outbound

This paper cites Mask r-cnn.

The Indirect Convolution Algorithm Mask r-cnn

Reference 13

Resolution: verified fuzzy; raw_fallback, observed 2026-05-25T10:05:37.747432Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

Observation 4d0f66a6-121a-47fd-beed-f8400bb31573 · outbound

This paper cites Deep residual learning for image recognition.

The Indirect Convolution Algorithm Deep residual learning for image recognition

Reference 14

Resolution: verified fuzzy; raw_fallback, observed 2026-05-25T10:05:37.773948Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

Observation 268b315c-1a19-40ef-9d04-d7d9cbd8cb82 · outbound

This paper cites LIBXSMM: accelerating small matrix multi- plications by runtime code generation.

The Indirect Convolution Algorithm LIBXSMM: accelerating small matrix multi- plications by runtime code generation

Reference 15

Resolution: verified fuzzy; raw_fallback, observed 2026-05-25T10:05:37.783769Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

Observation 6d922052-234b-480a-bef8-238ec591c1ea · outbound

This paper cites Squeeze-and-excitation net- works.

The Indirect Convolution Algorithm Squeeze-and-excitation net- works

Reference 16

Resolution: verified fuzzy; raw_fallback, observed 2026-05-25T10:05:37.740424Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

Observation cb5e581f-25b6-46ff-8b38-3e3671f24e9c · outbound

This paper cites SqueezeNet: AlexNet-level accuracy with 50x fewer parameters and <0.5MB model size.

The Indirect Convolution Algorithm SqueezeNet: AlexNet-level accuracy with 50x fewer parameters and <0.5MB model size

Reference 17

Resolution: verified exact; local_arxiv, observed 2026-05-25T10:05:36.944385Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

Observation 33f087cd-08af-420f-b7a4-816baac765fd · outbound

This paper cites Caffe: Convolutional architecture for fast feature embedding.

The Indirect Convolution Algorithm Caffe: Convolutional architecture for fast feature embedding

Reference 18

Resolution: verified fuzzy; raw_fallback, observed 2026-05-25T10:05:37.790768Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

Observation b90131df-3977-4050-922b-c20b62a45c70 · outbound

This paper cites Pooling Pyramid Network for Object Detection.

The Indirect Convolution Algorithm Pooling Pyramid Network for Object Detection

Reference 19

Resolution: verified exact; local_arxiv, observed 2026-05-25T10:05:36.932245Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

Observation 56f18808-c08f-4b24-9622-0d104b9213d6 · outbound

This paper cites Panoptic Feature Pyramid Networks.

The Indirect Convolution Algorithm Panoptic Feature Pyramid Networks

Reference 20

Resolution: verified exact; local_arxiv, observed 2026-05-25T10:05:36.926520Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

Observation 173d820d-6b88-4893-b961-6854c5abb1b5 · outbound

This paper cites Fast algorithms for convo- lutional neural networks.

The Indirect Convolution Algorithm Fast algorithms for convo- lutional neural networks

Reference 21

Resolution: verified fuzzy; raw_fallback, observed 2026-05-25T10:05:37.743770Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

Observation e28b38ca-dd9f-43ed-b99a-ca4ef59090e3 · outbound

This paper cites Focal loss for dense object detection.

The Indirect Convolution Algorithm Focal loss for dense object detection

Reference 22

Resolution: verified fuzzy; raw_fallback, observed 2026-05-25T10:05:37.780371Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

Observation 02384c4d-4bca-4170-a578-ee89daa9faa7 · outbound

This paper cites Shufﬂenet v2: Practical guidelines for efﬁcient CNN archi- tecture design.

The Indirect Convolution Algorithm Shufﬂenet v2: Practical guidelines for efﬁcient CNN archi- tecture design

Reference 23

Resolution: verified fuzzy; raw_fallback, observed 2026-05-25T10:05:37.737042Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

Observation b326dfba-7a6f-4223-9f8f-541dcfd5f3f6 · outbound

This paper cites Automatic differentiation in PyTorch.

The Indirect Convolution Algorithm Automatic differentiation in PyTorch

Reference 24

Resolution: verified fuzzy; raw_fallback, observed 2026-05-25T10:05:37.755675Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

Observation f28965b4-5ee8-4ee9-a44b-9d5f3a59c8ac · outbound

This paper cites YOLOv3: An Incremental Improvement.

The Indirect Convolution Algorithm YOLOv3: An Incremental Improvement

Reference 25

Resolution: verified exact; local_arxiv, observed 2026-05-25T10:05:36.900994Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

Observation b0be89a2-3f64-44de-9c13-84ebd69372a0 · outbound

This paper cites Mobilenetv2: Inverted residuals and linear bottlenecks.

The Indirect Convolution Algorithm Mobilenetv2: Inverted residuals and linear bottlenecks

Reference 26

Resolution: verified fuzzy; raw_fallback, observed 2026-05-25T10:05:37.787037Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

Observation fcf0e441-32dc-4abc-a32b-ead0168c6c05 · outbound

This paper cites Wavenet: A generative model for raw audio.

The Indirect Convolution Algorithm Wavenet: A generative model for raw audio

Reference 27

Resolution: verified fuzzy; raw_fallback, observed 2026-05-25T10:05:37.794373Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

Observation cda8d6b8-a01c-46ec-9e06-3c78032e9f21 · outbound

This paper cites Blis: A frame- work for rapidly instantiating BLAS functionality.

The Indirect Convolution Algorithm Blis: A frame- work for rapidly instantiating BLAS functionality

Reference 28

Resolution: verified fuzzy; raw_fallback, observed 2026-05-25T10:05:37.733527Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

Observation 04e42665-d871-4bb0-ae70-be5653302f44 · outbound

This paper cites Fast Convolutional Nets With fbfft: A GPU Performance Evaluation.

The Indirect Convolution Algorithm Fast Convolutional Nets With fbfft: A GPU Performance Evaluation

Reference 29

Resolution: verified exact; local_arxiv, observed 2026-05-25T10:05:36.907482Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

Observation 9562219f-aa1e-4b0d-9d8c-f314ffbc0f62 · outbound

This paper cites Rooﬂine: An insightful visual performance model for ﬂoating-point programs and multicore architectures.

The Indirect Convolution Algorithm Rooﬂine: An insightful visual performance model for ﬂoating-point programs and multicore architectures

Reference 30

Resolution: verified fuzzy; raw_fallback, observed 2026-05-25T10:05:37.750949Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

Observation 44eb7141-f690-469c-a80e-7e456bac9807 · outbound

This paper cites FBNet: Hardware-Aware Efficient ConvNet Design via Differentiable Neural Architecture Search.

The Indirect Convolution Algorithm FBNet: Hardware-Aware Efficient ConvNet Design via Differentiable Neural Architecture Search

Reference 31

Resolution: verified exact; local_arxiv, observed 2026-05-25T10:05:36.938204Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

Observation fef84997-e60d-4444-ac4d-2b8a9805f53f · outbound

This paper cites Pay Less Attention with Lightweight and Dynamic Convolutions.

The Indirect Convolution Algorithm Pay Less Attention with Lightweight and Dynamic Convolutions

Reference 32

Resolution: verified exact; local_arxiv, observed 2026-05-25T10:05:36.920371Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

Observation a223c854-413d-4642-a8f3-15aa44276905 · outbound

This paper cites an unresolved cited work.

The Indirect Convolution Algorithm Unresolved cited work

Reference 33

Resolution: unresolved; raw_fallback, observed 2026-05-25T10:05:37.765550Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

Observation 519ad447-dffc-40e2-9489-7adcfb8f2f42 · outbound

This paper cites Aggregated residual transformations for deep neural networks.

The Indirect Convolution Algorithm Aggregated residual transformations for deep neural networks

Reference 34

Resolution: verified fuzzy; raw_fallback, observed 2026-05-25T10:05:37.769726Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

Observation 273dcbbe-33ec-42fb-871e-70105c817fb0 · outbound

This paper cites High Performance Zero-Memory Overhead Direct Convolutions.

The Indirect Convolution Algorithm High Performance Zero-Memory Overhead Direct Convolutions

Reference 35

Resolution: verified exact; local_arxiv, observed 2026-05-25T10:05:36.914218Z

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No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

Observation d74cef89-b098-4758-b23c-0c2882326439 · outbound

This paper cites Learning transferable architectures for scalable image recognition.

The Indirect Convolution Algorithm Learning transferable architectures for scalable image recognition

Reference 36

Resolution: verified fuzzy; raw_fallback, observed 2026-05-25T10:05:37.760086Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

Pith citing papers

Observation 32c92b7f-ae0b-4811-9435-9ba2e3ee45b7 · inbound

Threshold Neuron: A Brain-inspired Artificial Neuron for Efficient On-device Inference cites this paper.

Threshold Neuron: A Brain-inspired Artificial Neuron for Efficient On-device Inference The Indirect Convolution Algorithm

Reference 12

Resolution: unresolved; no resolver link, observed 2026-08-11T12:44:45.384791Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 488de650-a313-423d-a2ad-53c71e553fe3 · inbound

Efficient Column-Wise N:M Pruning on RISC-V CPU cites this paper.

Efficient Column-Wise N:M Pruning on RISC-V CPU The Indirect Convolution Algorithm

Reference 12

Resolution: verified exact; local_arxiv, observed 2026-08-06T14:55:28.626810Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

Observation 38873f0f-7c40-40fa-81a4-9e74dd62c822 · inbound

INT8 Quantization Makes ARM Edge Inference Dispatch-Invariant cites this paper.

INT8 Quantization Makes ARM Edge Inference Dispatch-Invariant The Indirect Convolution Algorithm

Reference 2019

Resolution: unresolved; no resolver link, observed 2026-08-01T00:06:38.994626Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.