Paper Citation Record · LEDGER

Training with Confidence: Catching Silent Errors in Deep Learning Training with Automated Proactive Checks

As of 8 August 2026, this Paper Citation Record lists 52 of 52 outbound references and 1 inbound Pith citation observation for arXiv:2506.14813.

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

pith.paper-citation-record.v1
2506.14813 v1

Coverage vector

measured 52 of 52 reference resolution

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measured 1 of 1 inbound itemization

Pith citing papers itemized under the disclosed page cap.

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Source: arxiv_reference, observed 2026-05-11T07:50:58.845346Z

Reference resolution

52 of 52 outbound references displayed

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External citation measurements

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Outbound references

Observation 979f9707-4cdf-44c8-b3eb-a6b09f56a574 · outbound

This paper cites TensorFlow: a system for large-scale machine learning.

Training with Confidence: Catching Silent Errors in Deep Learning Training with Automated Proactive Checks TensorFlow: a system for large-scale machine learning

Reference 1

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This paper cites PyTorch 2: Faster Machine Learning Through Dynamic Python Bytecode Transformation and Graph Compila- tion.

Training with Confidence: Catching Silent Errors in Deep Learning Training with Automated Proactive Checks PyTorch 2: Faster Machine Learning Through Dynamic Python Bytecode Transformation and Graph Compila- tion

Reference 2

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This paper cites Varuna: scal- able, low-cost training of massive deep learning models.

Training with Confidence: Catching Silent Errors in Deep Learning Training with Automated Proactive Checks Varuna: scal- able, low-cost training of massive deep learning models

Reference 3

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This paper cites BLOOM: Megatron-DeepSpeed.

Training with Confidence: Catching Silent Errors in Deep Learning Training with Automated Proactive Checks BLOOM: Megatron-DeepSpeed

Reference 4

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This paper cites Weights and Biases.

Training with Confidence: Catching Silent Errors in Deep Learning Training with Automated Proactive Checks Weights and Biases

Reference 5

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Observation a0468e63-b964-47a9-9b7c-8a9d62038d73 · outbound

This paper cites Chronicles of Big- Science TR11-176B-ML Training.

Training with Confidence: Catching Silent Errors in Deep Learning Training with Automated Proactive Checks Chronicles of Big- Science TR11-176B-ML Training

Reference 6

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This paper cites Dropout with Theano.

Training with Confidence: Catching Silent Errors in Deep Learning Training with Automated Proactive Checks Dropout with Theano

Reference 7

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This paper cites Hudson, Ehsan Adeli, and et al.

Training with Confidence: Catching Silent Errors in Deep Learning Training with Automated Proactive Checks Hudson, Ehsan Adeli, and et al

Reference 8

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Observation a1954582-eda7-4cab-9a26-2de9f5b64f50 · outbound

This paper cites Toward understanding deep learning framework bugs.ACM Trans.

Training with Confidence: Catching Silent Errors in Deep Learning Training with Automated Proactive Checks Toward understanding deep learning framework bugs.ACM Trans

Reference 9

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Observation 13b7bea0-dfdc-4b31-baae-66626e861d22 · outbound

This paper cites CodeParrot Clean Train Dataset, 2021.

Training with Confidence: Catching Silent Errors in Deep Learning Training with Automated Proactive Checks CodeParrot Clean Train Dataset, 2021

Reference 10

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This paper cites Fu, Stefano Ermon, Atri Rudra, and Christopher Ré.

Training with Confidence: Catching Silent Errors in Deep Learning Training with Automated Proactive Checks Fu, Stefano Ermon, Atri Rudra, and Christopher Ré

Reference 11

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This paper cites DeepSpeed GitHub Is- sues.

Training with Confidence: Catching Silent Errors in Deep Learning Training with Automated Proactive Checks DeepSpeed GitHub Is- sues

Reference 12

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This paper cites Un- derstanding software-2.0: A study of machine learning library usage and evolution.ACM Trans.

Training with Confidence: Catching Silent Errors in Deep Learning Training with Automated Proactive Checks Un- derstanding software-2.0: A study of machine learning library usage and evolution.ACM Trans

Reference 13

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Observation dacf0b86-f9b6-4bf1-b20d-0b4b73760938 · outbound

This paper cites The Llama 3 Herd of Models, 2024.

Training with Confidence: Catching Silent Errors in Deep Learning Training with Automated Proactive Checks The Llama 3 Herd of Models, 2024

Reference 14

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Observation 492eb19f-fe7d-473f-b7e5-a0b55ef9238d · outbound

This paper cites Bugs as deviant behavior: a general approach to inferring errors in systems code.

Training with Confidence: Catching Silent Errors in Deep Learning Training with Automated Proactive Checks Bugs as deviant behavior: a general approach to inferring errors in systems code

Reference 15

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This paper cites Ernst, Jake Cockrell, William G.

Training with Confidence: Catching Silent Errors in Deep Learning Training with Automated Proactive Checks Ernst, Jake Cockrell, William G

Reference 16

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Observation da19880f-d5ef-4b2e-9fb4-1d1499eb28aa · outbound

This paper cites A guide to deep learning in healthcare.Na- ture Medicine, 25(1):24–29, January 2019.

Training with Confidence: Catching Silent Errors in Deep Learning Training with Automated Proactive Checks A guide to deep learning in healthcare.Na- ture Medicine, 25(1):24–29, January 2019

Reference 17

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This paper cites Audee: automated testing for deep learning frameworks.

Training with Confidence: Catching Silent Errors in Deep Learning Training with Automated Proactive Checks Audee: automated testing for deep learning frameworks

Reference 18

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Training with Confidence: Catching Silent Errors in Deep Learning Training with Automated Proactive Checks Unresolved cited work

Reference 19

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This paper cites Dataloader not ran- domly sampling in PyTorch.

Training with Confidence: Catching Silent Errors in Deep Learning Training with Automated Proactive Checks Dataloader not ran- domly sampling in PyTorch

Reference 20

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This paper cites Oobleck: Resilient Distributed Training of Large Models Using Pipeline Templates.

Training with Confidence: Catching Silent Errors in Deep Learning Training with Automated Proactive Checks Oobleck: Resilient Distributed Training of Large Models Using Pipeline Templates

Reference 21

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This paper cites A static an- alyzer for detecting tensor shape errors in deep neural network training code.

Training with Confidence: Catching Silent Errors in Deep Learning Training with Automated Proactive Checks A static an- alyzer for detecting tensor shape errors in deep neural network training code

Reference 22

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This paper cites Deep learning.Nature, 521(7553):436–444, May 2015.

Training with Confidence: Catching Silent Errors in Deep Learning Training with Automated Proactive Checks Deep learning.Nature, 521(7553):436–444, May 2015

Reference 23

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This paper cites Reliability Assurance for Deep Neural Network Architectures against Numerical Defects.

Training with Confidence: Catching Silent Errors in Deep Learning Training with Automated Proactive Checks Reliability Assurance for Deep Neural Network Architectures against Numerical Defects

Reference 24

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This paper cites Universal Checkpointing: Efficient and Flex- ible Checkpointing for Large Scale Distributed Training, 2024.

Training with Confidence: Catching Silent Errors in Deep Learning Training with Automated Proactive Checks Universal Checkpointing: Efficient and Flex- ible Checkpointing for Large Scale Distributed Training, 2024

Reference 25

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This paper cites NN- Smith: Generating Diverse and Valid Test Cases for Deep Learning Compilers.

Training with Confidence: Catching Silent Errors in Deep Learning Training with Automated Proactive Checks NN- Smith: Generating Diverse and Valid Test Cases for Deep Learning Compilers

Reference 26

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This paper cites NeuRI: Diversifying DNN Generation via In- ductive Rule Inference.

Training with Confidence: Catching Silent Errors in Deep Learning Training with Automated Proactive Checks NeuRI: Diversifying DNN Generation via In- ductive Rule Inference

Reference 27

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This paper cites Demystify- ing and Checking Silent Semantic Violations in Large Distributed Systems.

Training with Confidence: Catching Silent Errors in Deep Learning Training with Automated Proactive Checks Demystify- ing and Checking Silent Semantic Violations in Large Distributed Systems

Reference 28

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This paper cites Sakallah.

Training with Confidence: Catching Silent Errors in Deep Learning Training with Automated Proactive Checks Sakallah

Reference 29

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This paper cites Efficient large-scale language model training on GPU clusters using megatron-LM.

Training with Confidence: Catching Silent Errors in Deep Learning Training with Automated Proactive Checks Efficient large-scale language model training on GPU clusters using megatron-LM

Reference 30

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This paper cites DeepXplore: Automated Whitebox Testing of Deep Learning Systems.

Training with Confidence: Catching Silent Errors in Deep Learning Training with Automated Proactive Checks DeepXplore: Automated Whitebox Testing of Deep Learning Systems

Reference 31

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This paper cites CRADLE: Cross-Backend Validation to De- tect and Localize Bugs in Deep Learning Libraries.

Training with Confidence: Catching Silent Errors in Deep Learning Training with Automated Proactive Checks CRADLE: Cross-Backend Validation to De- tect and Localize Bugs in Deep Learning Libraries

Reference 32

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This paper cites PyTorch Discussion Forum.

Training with Confidence: Catching Silent Errors in Deep Learning Training with Automated Proactive Checks PyTorch Discussion Forum

Reference 33

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Training with Confidence: Catching Silent Errors in Deep Learning Training with Automated Proactive Checks PyTorch Examples

Reference 34

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This paper cites PyTorch GitHub Issues.

Training with Confidence: Catching Silent Errors in Deep Learning Training with Automated Proactive Checks PyTorch GitHub Issues

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This paper cites A Bug That Plagues Thousands of Open-Source ML Projects.

Training with Confidence: Catching Silent Errors in Deep Learning Training with Automated Proactive Checks A Bug That Plagues Thousands of Open-Source ML Projects

Reference 36

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Training with Confidence: Catching Silent Errors in Deep Learning Training with Automated Proactive Checks Unresolved cited work

Reference 37

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This paper cites Language Models are Un- supervised Multitask Learners.

Training with Confidence: Catching Silent Errors in Deep Learning Training with Automated Proactive Checks Language Models are Un- supervised Multitask Learners

Reference 38

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This paper cites DeepSpeed: System Optimizations Enable Training Deep Learning Models with Over 100 Billion Parameters.

Training with Confidence: Catching Silent Errors in Deep Learning Training with Automated Proactive Checks DeepSpeed: System Optimizations Enable Training Deep Learning Models with Over 100 Billion Parameters

Reference 39

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This paper cites StackOverflow - Questions and Answers on PyTorch.

Training with Confidence: Catching Silent Errors in Deep Learning Training with Automated Proactive Checks StackOverflow - Questions and Answers on PyTorch

Reference 40

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This paper cites Bamboo: Making Preemptible Instances Resilient for Affordable Training of Large DNNs.

Training with Confidence: Catching Silent Errors in Deep Learning Training with Automated Proactive Checks Bamboo: Making Preemptible Instances Resilient for Affordable Training of Large DNNs

Reference 41

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This paper cites DeepTest: automated testing of deep-neural-network- driven autonomous cars.

Training with Confidence: Catching Silent Errors in Deep Learning Training with Automated Proactive Checks DeepTest: automated testing of deep-neural-network- driven autonomous cars

Reference 42

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This paper cites Deep learning library testing via ef- fective model generation.

Training with Confidence: Catching Silent Errors in Deep Learning Training with Automated Proactive Checks Deep learning library testing via ef- fective model generation

Reference 43

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Observation f60a2a93-554b-4f30-9dce-caa6a16d7db0 · outbound

This paper cites BloombergGPT: A Large Language Model for Finance, 2023.

Training with Confidence: Catching Silent Errors in Deep Learning Training with Automated Proactive Checks BloombergGPT: A Large Language Model for Finance, 2023

Reference 44

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This paper cites Unawareness of Deep Learning Mistakes.

Training with Confidence: Catching Silent Errors in Deep Learning Training with Automated Proactive Checks Unawareness of Deep Learning Mistakes

Reference 45

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Observation c582e20d-e144-4cf0-9894-ff64e2bbb56d · outbound

This paper cites Fight Against Silent Bugs in Deep Learning Libraries.

Training with Confidence: Catching Silent Errors in Deep Learning Training with Automated Proactive Checks Fight Against Silent Bugs in Deep Learning Libraries

Reference 46

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Observation c7424eb9-88a7-4391-a388-fb74e75f5c82 · outbound

This paper cites DuoAI: Fast, Automated Inference of Inductive In- variants for Verifying Distributed Protocols.

Training with Confidence: Catching Silent Errors in Deep Learning Training with Automated Proactive Checks DuoAI: Fast, Automated Inference of Inductive In- variants for Verifying Distributed Protocols

Reference 47

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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation 534df0f8-898b-44df-8c50-6ee6608985dd · outbound

This paper cites DistAI: Data-Driven Automated Invariant Learning for Distributed Protocols.

Training with Confidence: Catching Silent Errors in Deep Learning Training with Automated Proactive Checks DistAI: Data-Driven Automated Invariant Learning for Distributed Protocols

Reference 48

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Observation f36e6f7d-b37d-4c74-9a26-81f5bf855b27 · outbound

This paper cites Hyper-Parameter Optimization: A Review of Algorithms and Applications, 2020.

Training with Confidence: Catching Silent Errors in Deep Learning Training with Automated Proactive Checks Hyper-Parameter Optimization: A Review of Algorithms and Applications, 2020

Reference 49

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Observation cebf8c2d-aa26-4f3a-b838-7e8a3da9fbe2 · outbound

This paper cites An empirical study on program failures of deep learning jobs.

Training with Confidence: Catching Silent Errors in Deep Learning Training with Automated Proactive Checks An empirical study on program failures of deep learning jobs

Reference 50

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Observation ed27a268-4401-40a1-9472-eaa26421656b · outbound

This paper cites OPT: Open Pre-trained Trans- former Language Models, 2022.

Training with Confidence: Catching Silent Errors in Deep Learning Training with Automated Proactive Checks OPT: Open Pre-trained Trans- former Language Models, 2022

Reference 51

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T10:19:21.337989Z

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Observation ce357882-c49d-467a-93c0-cedbbc073b1a · outbound

This paper cites An Empirical Study of Common Chal- lenges in Developing Deep Learning Applications.

Training with Confidence: Catching Silent Errors in Deep Learning Training with Automated Proactive Checks An Empirical Study of Common Chal- lenges in Developing Deep Learning Applications

Reference 52

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T10:19:21.066022Z

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Pith citing papers

Observation c470cfbb-88e0-4246-9273-ecf72f2d3f32 · inbound

Demystifying the Silence of Correctness Bugs in PyTorch Compiler cites this paper.

Demystifying the Silence of Correctness Bugs in PyTorch Compiler Training with Confidence: Catching Silent Errors in Deep Learning Training with Automated Proactive Checks

Reference 22

Resolution: verified exact; arxiv_id, observed 2026-05-11T07:50:58.852348Z

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