Paper Citation Record · LEDGER

Scalable Machine Learning Training Infrastructure for Online Ads Recommendation and Auction Scoring Modeling at Google

As of 16 August 2026, this Paper Citation Record lists 35 of 35 outbound references and 0 inbound Pith citation observations for arXiv:2501.10546.

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

pith.paper-citation-record.v1
2501.10546 v1

Coverage vector

measured 35 of 35 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-10T19:11:32.558277Z

measured 35 of 35 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-16T06:30:59.297886+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

35 of 35 outbound references displayed

verified exact2
verified fuzzy20
unresolved13
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External citation measurements

No source-named external measurement is stored.

Outbound references

Observation 9a059ed0-6105-4ab9-87a3-9fc609e6fd22 · outbound

This paper cites an unresolved cited work.

Scalable Machine Learning Training Infrastructure for Online Ads Recommendation and Auction Scoring Modeling at Google Unresolved cited work

Reference 1

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Observation 21568c50-c70a-47b3-bbc1-cdf03b52ddcd · outbound

This paper cites an unresolved cited work.

Scalable Machine Learning Training Infrastructure for Online Ads Recommendation and Auction Scoring Modeling at Google Unresolved cited work

Reference 2

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Observation e91ebe94-bcfb-4b6b-92df-ef1269db1bba · outbound

This paper cites Understanding training efficiency of deep learning recommendation models at scale,.

Scalable Machine Learning Training Infrastructure for Online Ads Recommendation and Auction Scoring Modeling at Google Understanding training efficiency of deep learning recommendation models at scale,

Reference 3

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T19:11:33.281551Z

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Observation faf91269-c3d0-4865-a09d-9e549d98abe3 · outbound

This paper cites On the Factory Floor: ML Engineering for Industrial-Scale Ads Recommendation Models.

Scalable Machine Learning Training Infrastructure for Online Ads Recommendation and Auction Scoring Modeling at Google On the Factory Floor: ML Engineering for Industrial-Scale Ads Recommendation Models

Reference 4

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Observation 870e4b03-334c-4083-977d-2284bf1a06a7 · outbound

This paper cites Memory efficient adaptive optimization,.

Scalable Machine Learning Training Infrastructure for Online Ads Recommendation and Auction Scoring Modeling at Google Memory efficient adaptive optimization,

Reference 5

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Observation 5d90b8eb-ccc2-4197-afb4-b143325c6bf9 · outbound

This paper cites tf.data service: A Case for Disaggregating ML Input Data Processing.

Scalable Machine Learning Training Infrastructure for Online Ads Recommendation and Auction Scoring Modeling at Google tf.data service: A Case for Disaggregating ML Input Data Processing

Reference 6

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Source-reported events for the cited work

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Observation 12d133a5-c84a-4fa9-a6e0-a875e5a495e7 · outbound

This paper cites cuDNN: Efficient Primitives for Deep Learning.

Scalable Machine Learning Training Infrastructure for Online Ads Recommendation and Auction Scoring Modeling at Google cuDNN: Efficient Primitives for Deep Learning

Reference 7

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Source-reported events for the cited work

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Observation cd27264d-2f46-4186-81e6-6bd9dae42e91 · outbound

This paper cites Large scale distributed deep networks,.

Scalable Machine Learning Training Infrastructure for Online Ads Recommendation and Auction Scoring Modeling at Google Large scale distributed deep networks,

Reference 8

Resolution: unresolved; no resolver link, observed 2026-08-10T19:11:32.414065Z

Source-reported events for the cited work

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Observation 24324da1-4343-4c5f-bb78-4cda9d553a34 · outbound

This paper cites Materialization and reuse optimizations for production data science pipelines,.

Scalable Machine Learning Training Infrastructure for Online Ads Recommendation and Auction Scoring Modeling at Google Materialization and reuse optimizations for production data science pipelines,

Reference 9

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Source-reported events for the cited work

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Observation 0adcd9c1-ca6c-444e-8415-e1a6dbf5c59a · outbound

This paper cites Mtia: First generation silicon targeting meta’s recommendation systems,.

Scalable Machine Learning Training Infrastructure for Online Ads Recommendation and Auction Scoring Modeling at Google Mtia: First generation silicon targeting meta’s recommendation systems,

Reference 10

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T19:11:33.213400Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

Observation abf70725-f093-4b35-a77c-da1748868665 · outbound

This paper cites Cachew: Machine learning input data processing as a service,.

Scalable Machine Learning Training Infrastructure for Online Ads Recommendation and Auction Scoring Modeling at Google Cachew: Machine learning input data processing as a service,

Reference 11

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T19:11:33.193308Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

Observation f089cbf1-93e2-459b-8d75-6f17677d5a13 · outbound

This paper cites Nectar: automatic management of data and computation in datacen- ters,.

Scalable Machine Learning Training Infrastructure for Online Ads Recommendation and Auction Scoring Modeling at Google Nectar: automatic management of data and computation in datacen- ters,

Reference 12

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T19:11:33.176665Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

Observation 432bf6a7-abe7-4e0f-acb4-0e218e580ed6 · outbound

This paper cites Practical lessons from predicting clicks on ads at facebook,.

Scalable Machine Learning Training Infrastructure for Online Ads Recommendation and Auction Scoring Modeling at Google Practical lessons from predicting clicks on ads at facebook,

Reference 13

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T19:11:33.158662Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

Observation a52c5f38-1f45-49ee-94ab-be08cb1a583e · outbound

This paper cites Xdl: an industrial deep learning framework for high-dimensional sparse data,.

Scalable Machine Learning Training Infrastructure for Online Ads Recommendation and Auction Scoring Modeling at Google Xdl: an industrial deep learning framework for high-dimensional sparse data,

Reference 14

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T19:11:33.141799Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

Observation af3f14f2-b9ae-4821-bc14-849b787c2896 · outbound

This paper cites Tpu v4: An optically reconfigurable supercomputer for machine learning with hardware sup- port for embeddings,.

Scalable Machine Learning Training Infrastructure for Online Ads Recommendation and Auction Scoring Modeling at Google Tpu v4: An optically reconfigurable supercomputer for machine learning with hardware sup- port for embeddings,

Reference 15

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Source-reported events for the cited work

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Observation dbc8214f-ed6c-4da8-8dc0-874ee1404090 · outbound

This paper cites Persia: An open, hybrid system scaling deep learning-based recommenders up to 100 trillion parameters,.

Scalable Machine Learning Training Infrastructure for Online Ads Recommendation and Auction Scoring Modeling at Google Persia: An open, hybrid system scaling deep learning-based recommenders up to 100 trillion parameters,

Reference 16

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T19:11:33.108571Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

Observation 03dc2364-6a75-4294-87ab-ea5cbd37d152 · outbound

This paper cites Monolith: Real Time Recommendation System With Collisionless Embedding Table.

Scalable Machine Learning Training Infrastructure for Online Ads Recommendation and Auction Scoring Modeling at Google Monolith: Real Time Recommendation System With Collisionless Embedding Table

Reference 17

Resolution: unresolved; no resolver link, observed 2026-08-10T19:11:32.456543Z

Source-reported events for the cited work

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Observation 58cf9bd2-b3bb-4fcb-8326-468a30a4dfce · outbound

This paper cites Ad click prediction: a view from the trenches,.

Scalable Machine Learning Training Infrastructure for Online Ads Recommendation and Auction Scoring Modeling at Google Ad click prediction: a view from the trenches,

Reference 18

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T19:11:33.076492Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

Observation b28e7dd2-b33d-4670-b863-2e9622c53aee · outbound

This paper cites Efficient Estimation of Word Representations in Vector Space.

Scalable Machine Learning Training Infrastructure for Online Ads Recommendation and Auction Scoring Modeling at Google Efficient Estimation of Word Representations in Vector Space

Reference 19

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Source-reported events for the cited work

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Observation 7fdbbc8b-0947-4c5d-8e0c-d133b4b6f0b1 · outbound

This paper cites Module: tf.math — tensorflow,.

Scalable Machine Learning Training Infrastructure for Online Ads Recommendation and Auction Scoring Modeling at Google Module: tf.math — tensorflow,

Reference 20

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T19:11:32.974904Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

Observation 8b5588e9-6404-45c7-9a1d-f3972707c8b7 · outbound

This paper cites Software-hardware co-design for fast and scalable training of deep learning recommendation models,.

Scalable Machine Learning Training Infrastructure for Online Ads Recommendation and Auction Scoring Modeling at Google Software-hardware co-design for fast and scalable training of deep learning recommendation models,

Reference 21

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T19:11:32.944693Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

Observation 6284a352-494d-480f-936a-75ca53a73e38 · outbound

This paper cites Deep Learning Recommendation Model for Personalization and Recommendation Systems.

Scalable Machine Learning Training Infrastructure for Online Ads Recommendation and Auction Scoring Modeling at Google Deep Learning Recommendation Model for Personalization and Recommendation Systems

Reference 22

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Source-reported events for the cited work

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Observation be06f740-baaa-478a-87d8-22fc3c0b8554 · outbound

This paper cites The design process for google’s training chips: Tpuv2 and tpuv3,.

Scalable Machine Learning Training Infrastructure for Online Ads Recommendation and Auction Scoring Modeling at Google The design process for google’s training chips: Tpuv2 and tpuv3,

Reference 23

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T19:11:32.926688Z

Source-reported events for the cited work

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Observation d2369357-9f7a-4539-98d3-2a53d7fb1e13 · outbound

This paper cites The design process for google’s training chips: Tpuv2 and tpuv3,.

Scalable Machine Learning Training Infrastructure for Online Ads Recommendation and Auction Scoring Modeling at Google The design process for google’s training chips: Tpuv2 and tpuv3,

Reference 24

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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

Observation 61bde9f8-db42-406b-a551-d83ece60f541 · outbound

This paper cites Uplift: parallelization strategies for feature transformations in machine learning workloads,.

Scalable Machine Learning Training Infrastructure for Online Ads Recommendation and Auction Scoring Modeling at Google Uplift: parallelization strategies for feature transformations in machine learning workloads,

Reference 25

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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

Observation 7bb0c304-af97-4078-8308-63b7a974f3b9 · outbound

This paper cites Recshard: statistical feature-based memory optimization for industry-scale neural recommendation,.

Scalable Machine Learning Training Infrastructure for Online Ads Recommendation and Auction Scoring Modeling at Google Recshard: statistical feature-based memory optimization for industry-scale neural recommendation,

Reference 26

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No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

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This paper cites FlexShard: Flexible Sharding for Industry-Scale Sequence Recommendation Models.

Scalable Machine Learning Training Infrastructure for Online Ads Recommendation and Auction Scoring Modeling at Google FlexShard: Flexible Sharding for Industry-Scale Sequence Recommendation Models

Reference 27

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Observation a8cd1e60-f2fd-4d6d-ac76-bb2739e6518f · outbound

This paper cites Outrageously Large Neural Networks: The Sparsely-Gated Mixture-of-Experts Layer.

Scalable Machine Learning Training Infrastructure for Online Ads Recommendation and Auction Scoring Modeling at Google Outrageously Large Neural Networks: The Sparsely-Gated Mixture-of-Experts Layer

Reference 28

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Scalable Machine Learning Training Infrastructure for Online Ads Recommendation and Auction Scoring Modeling at Google Attention is all you need,

Reference 29

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Source-reported events for the cited work

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Observation 10d93129-8877-4b33-8c90-537817cea2b5 · outbound

This paper cites Large-scale cluster management at google with borg,.

Scalable Machine Learning Training Infrastructure for Online Ads Recommendation and Auction Scoring Modeling at Google Large-scale cluster management at google with borg,

Reference 30

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Observation e189d636-3dc8-4611-bf94-37bbd8eff476 · outbound

This paper cites El-rec: Efficient large-scale recommendation model training via tensor- train embedding table,.

Scalable Machine Learning Training Infrastructure for Online Ads Recommendation and Auction Scoring Modeling at Google El-rec: Efficient large-scale recommendation model training via tensor- train embedding table,

Reference 31

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T19:11:32.829912Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

Observation 28b8dce1-3ff9-4677-bd9d-a3520a6a2b33 · outbound

This paper cites Agile and accurate ctr prediction model training for massive-scale online ad- vertising systems,.

Scalable Machine Learning Training Infrastructure for Online Ads Recommendation and Auction Scoring Modeling at Google Agile and accurate ctr prediction model training for massive-scale online ad- vertising systems,

Reference 32

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T19:11:32.812324Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

Observation 28ae38a2-b4e1-4e70-a7d9-1224e71471d1 · outbound

This paper cites Large Batch Optimization for Deep Learning: Training BERT in 76 minutes.

Scalable Machine Learning Training Infrastructure for Online Ads Recommendation and Auction Scoring Modeling at Google Large Batch Optimization for Deep Learning: Training BERT in 76 minutes

Reference 33

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This paper cites Wukong: Towards a Scaling Law for Large-Scale Recommendation.

Scalable Machine Learning Training Infrastructure for Online Ads Recommendation and Auction Scoring Modeling at Google Wukong: Towards a Scaling Law for Large-Scale Recommendation

Reference 34

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Observation 015e38aa-510a-4e43-898d-b1a579cc8101 · outbound

This paper cites Understanding data storage and ingestion for large-scale deep recommendation model training: Industrial product,.

Scalable Machine Learning Training Infrastructure for Online Ads Recommendation and Auction Scoring Modeling at Google Understanding data storage and ingestion for large-scale deep recommendation model training: Industrial product,

Reference 35

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T19:11:32.792945Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

Pith citing papers

No inbound Pith citation observations are available.