Paper Citation Record · LEDGER

Neural Architecture Codesign for Fast Physics Applications

As of 11 August 2026, this Paper Citation Record lists 31 of 31 outbound references and 0 inbound Pith citation observations for arXiv:2501.05515.

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

pith.paper-citation-record.v1
2501.05515 v1

Coverage vector

measured 31 of 31 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-10T21:17:54.084352Z

measured 31 of 31 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-11T06:34:44.6726+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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verified fuzzy7
unresolved16
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External citation measurements

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Outbound references

Observation 73c61268-500e-4301-aeeb-f462962b40b0 · outbound

This paper cites Neural Architecture Search: A Survey.

Neural Architecture Codesign for Fast Physics Applications Neural Architecture Search: A Survey

Reference 1

Resolution: unresolved; no resolver link, observed 2026-08-10T21:17:53.954215Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 2c76e2c0-8114-4f3d-b44c-aa7c3a5b9fcf · outbound

This paper cites A survey on evolutionary neural architecture search.

Neural Architecture Codesign for Fast Physics Applications A survey on evolutionary neural architecture search

Reference 2

Resolution: unresolved; no resolver link, observed 2026-08-10T21:17:53.959251Z

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Observation ebb46261-ede9-4b38-b4d6-91f0a85a2f2d · outbound

This paper cites A survey on computationally efficient neural architecture search.

Neural Architecture Codesign for Fast Physics Applications A survey on computationally efficient neural architecture search

Reference 3

Resolution: unresolved; no resolver link, observed 2026-08-10T21:17:53.963211Z

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Observation 885efefe-6e82-4ed2-95c7-a3e3a06a7b8d · outbound

This paper cites A Survey on Multi-Objective Neural Architecture Search.

Neural Architecture Codesign for Fast Physics Applications A Survey on Multi-Objective Neural Architecture Search

Reference 4

Resolution: unresolved; no resolver link, observed 2026-08-10T21:17:53.967854Z

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Observation 9ec3a6c1-7238-449f-9775-df37dbc2fa60 · outbound

This paper cites Once-for-All: Train One Network and Specialize it for Efficient Deployment.

Neural Architecture Codesign for Fast Physics Applications Once-for-All: Train One Network and Specialize it for Efficient Deployment

Reference 5

Resolution: unresolved; no resolver link, observed 2026-08-10T21:17:53.972536Z

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Observation 470e56be-3f29-4c4e-8079-ce94e5b7a8f7 · outbound

This paper cites Xilinx/brevitas: v0.11.0.

Neural Architecture Codesign for Fast Physics Applications Xilinx/brevitas: v0.11.0

Reference 6

Resolution: unresolved; no resolver link, observed 2026-08-10T21:17:53.977087Z

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Observation eabdc18e-98d9-4759-979f-eb75c98e00b1 · outbound

This paper cites Automatic heterogeneous quantization of deep neural networks for low-latency inference on the edge for particle detectors.

Neural Architecture Codesign for Fast Physics Applications Automatic heterogeneous quantization of deep neural networks for low-latency inference on the edge for particle detectors

Reference 7

Resolution: unresolved; no resolver link, observed 2026-08-10T21:17:53.982061Z

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Observation d04479a0-ed50-484d-9435-ba9f7057650b · outbound

This paper cites Optuna: A next-generation hyperparameter optimization framework.

Neural Architecture Codesign for Fast Physics Applications Optuna: A next-generation hyperparameter optimization framework

Reference 8

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T21:17:54.767221Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.

Observation 22d3c219-c1d6-4aff-8a5f-f64fb055df6f · outbound

This paper cites Fast inference of deep neural networks in FPGAs for particle physics.

Neural Architecture Codesign for Fast Physics Applications Fast inference of deep neural networks in FPGAs for particle physics

Reference 9

Resolution: unresolved; no resolver link, observed 2026-08-10T21:17:53.989513Z

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Observation 94bd0417-9879-464a-9dcf-1d340f6b2f94 · outbound

This paper cites BraggNN: Fast X-ray Bragg Peak Analysis Using Deep Learning.

Neural Architecture Codesign for Fast Physics Applications BraggNN: Fast X-ray Bragg Peak Analysis Using Deep Learning

Reference 10

Resolution: verified exact; local_arxiv, observed 2026-08-10T21:17:54.455593Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.

Observation dd6ffec9-49d1-4c22-b0de-35b4e4f73786 · outbound

This paper cites Deep Sets.

Neural Architecture Codesign for Fast Physics Applications Deep Sets

Reference 11

Resolution: unresolved; no resolver link, observed 2026-08-10T21:17:53.998081Z

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Observation 09205545-5e75-4d56-961a-7aa87b0742af · outbound

This paper cites Energy Flow Networks: Deep Sets for Particle Jets.

Neural Architecture Codesign for Fast Physics Applications Energy Flow Networks: Deep Sets for Particle Jets

Reference 12

Resolution: unresolved; no resolver link, observed 2026-08-10T21:17:54.001929Z

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Observation bfef1f75-079c-417e-8884-349f28c4e16d · outbound

This paper cites Ultrafast jet classification on FPGAs for the HL-LHC.

Neural Architecture Codesign for Fast Physics Applications Ultrafast jet classification on FPGAs for the HL-LHC

Reference 13

Resolution: verified exact; local_arxiv, observed 2026-08-10T21:17:54.416176Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.

Observation c2838377-c78c-48f9-a305-3c3d849ca2d9 · outbound

This paper cites Machine Learning in High Energy Physics: A review of heavy-flavor jet tagging at the LHC.

Neural Architecture Codesign for Fast Physics Applications Machine Learning in High Energy Physics: A review of heavy-flavor jet tagging at the LHC

Reference 14

Resolution: unresolved; no resolver link, observed 2026-08-10T21:17:54.010681Z

Source-reported events for the cited work

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Observation 75fe09f9-d72a-461d-bb10-cf498f05178d · outbound

This paper cites fastmachinelearning/nac-opt: v0.2.0.

Neural Architecture Codesign for Fast Physics Applications fastmachinelearning/nac-opt: v0.2.0

Reference 15

Resolution: verified exact; doi, observed 2026-08-10T21:17:54.153196Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.

Observation b75d972c-cd36-40f6-8953-92f186b93917 · outbound

This paper cites A comprehensive survey of neural architecture search: Challenges and solutions.

Neural Architecture Codesign for Fast Physics Applications A comprehensive survey of neural architecture search: Challenges and solutions

Reference 16

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T21:17:54.755239Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.

Observation 3685f165-a69e-4737-9c17-c6bfd511b0d5 · outbound

This paper cites Zero-cost proxies for lightweight NAS.

Neural Architecture Codesign for Fast Physics Applications Zero-cost proxies for lightweight NAS

Reference 17

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T21:17:54.742663Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.

Observation c945bab6-ed58-41a7-b893-d023fa2b9a79 · outbound

This paper cites Algorithms for hyper-parameter optimization.

Neural Architecture Codesign for Fast Physics Applications Algorithms for hyper-parameter optimization

Reference 18

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T21:17:54.730189Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.

Observation c0b53d6e-1f37-4d9f-b154-4fdd3b7f6fae · outbound

This paper cites A fast and elitist multiobjective genetic algorithm: Nsga-ii.

Neural Architecture Codesign for Fast Physics Applications A fast and elitist multiobjective genetic algorithm: Nsga-ii

Reference 19

Resolution: unresolved; no resolver link, observed 2026-08-10T21:17:54.031845Z

Source-reported events for the cited work

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Observation 44123b4e-d907-49bf-82a0-b962070711d7 · outbound

This paper cites Pruning and Quantization for Deep Neural Network Acceleration: A Survey.

Neural Architecture Codesign for Fast Physics Applications Pruning and Quantization for Deep Neural Network Acceleration: A Survey

Reference 20

Resolution: unresolved; no resolver link, observed 2026-08-10T21:17:54.035691Z

Source-reported events for the cited work

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Observation 299c92eb-c34b-4037-a1e5-fc945314c233 · outbound

This paper cites What is the State of Neural Network Pruning?.

Neural Architecture Codesign for Fast Physics Applications What is the State of Neural Network Pruning?

Reference 21

Resolution: unresolved; no resolver link, observed 2026-08-10T21:17:54.040712Z

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Observation 13f2f2cc-efde-42ee-acdd-017c75218fcf · outbound

This paper cites A hardware-friendly high-precision CNN pruning method and its FPGA implementation.

Neural Architecture Codesign for Fast Physics Applications A hardware-friendly high-precision CNN pruning method and its FPGA implementation

Reference 22

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T21:17:54.718594Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.

Observation 83c20571-7e4d-49c9-affe-ce3ad66c9a36 · outbound

This paper cites Learning Best Combination for Efficient N:M Sparsity.

Neural Architecture Codesign for Fast Physics Applications Learning Best Combination for Efficient N:M Sparsity

Reference 23

Resolution: verified exact; local_arxiv, observed 2026-08-10T21:17:54.283055Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.

Observation 768e4aaa-5d06-4893-9dda-89c3427ae8b5 · outbound

This paper cites Applications and Techniques for Fast Machine Learning in Science.

Neural Architecture Codesign for Fast Physics Applications Applications and Techniques for Fast Machine Learning in Science

Reference 24

Resolution: unresolved; no resolver link, observed 2026-08-10T21:17:54.055077Z

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Observation 333de4c0-ab8a-457a-a053-605d5a5f3159 · outbound

This paper cites fastmachinelearning/hls4ml: v0.8.0.

Neural Architecture Codesign for Fast Physics Applications fastmachinelearning/hls4ml: v0.8.0

Reference 25

Resolution: verified exact; doi, observed 2026-08-10T21:17:54.140583Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.

Observation 79b941d4-affc-48e2-b815-bf42b8bf1ffa · outbound

This paper cites Far-field high-energy diffraction microscopy: a non-destructive tool for characterizing the microstructure and micromechanical state of polycrystalline materials.

Neural Architecture Codesign for Fast Physics Applications Far-field high-energy diffraction microscopy: a non-destructive tool for characterizing the microstructure and micromechanical state of polycrystalline materials

Reference 26

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T21:17:54.707574Z

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No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.

Observation d5a1a556-e6a3-4e2b-9fdf-8c68b062f1bd · outbound

This paper cites OpenHLS: High-Level Synthesis for Low-Latency Deep Neural Networks for Experimental Science.

Neural Architecture Codesign for Fast Physics Applications OpenHLS: High-Level Synthesis for Low-Latency Deep Neural Networks for Experimental Science

Reference 27

Resolution: verified exact; local_arxiv, observed 2026-08-10T21:17:54.195936Z

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No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.

Observation ede7790d-d1e4-46c6-a991-3d9d9e8bc8f9 · outbound

This paper cites fastmachinelearning/l1-jet-id: v0.2.0.

Neural Architecture Codesign for Fast Physics Applications fastmachinelearning/l1-jet-id: v0.2.0

Reference 28

Resolution: verified exact; doi, observed 2026-08-10T21:17:54.127454Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.

Observation 13c856aa-0826-4f0b-af00-30e9df8f8888 · outbound

This paper cites Sherlock: A multi-objective design space exploration framework.

Neural Architecture Codesign for Fast Physics Applications Sherlock: A multi-objective design space exploration framework

Reference 29

Resolution: unresolved; no resolver link, observed 2026-08-10T21:17:54.075565Z

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Observation d8226d84-b957-4afe-a39b-4a6f9620ef41 · outbound

This paper cites rule4ml: An Open-Source Tool for Resource Utilization and Latency Estimation for ML Models on FPGA.

Neural Architecture Codesign for Fast Physics Applications rule4ml: An Open-Source Tool for Resource Utilization and Latency Estimation for ML Models on FPGA

Reference 30

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T21:17:54.694748Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.

Observation 2cde00c4-6893-4046-954f-71a739ddd268 · outbound

This paper cites End-to-end codesign of Hessian-aware quantized neural networks for FPGAs and ASICs.

Neural Architecture Codesign for Fast Physics Applications End-to-end codesign of Hessian-aware quantized neural networks for FPGAs and ASICs

Reference 31

Resolution: verified exact; local_arxiv, observed 2026-08-10T21:17:54.179075Z

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No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.

Pith citing papers

No inbound Pith citation observations are available.