Paper Citation Record · LEDGER

Weight Factorization and Centralization for Continual Learning in Speech Recognition

As of 20 August 2026, this Paper Citation Record lists 51 of 51 outbound references and 2 inbound Pith citation observations for arXiv:2506.16574.

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

pith.paper-citation-record.v1
2506.16574 v1

Coverage vector

measured 51 of 51 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-15T19:28:44.239884Z

measured 53 of 53 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 2 of 2 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-15T19:28:43.454599Z

measured 0 of 1 external citation measurements

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

Source: arxiv_reference, observed 2026-07-02T08:06:48.157851Z

Reference resolution

51 of 51 outbound references displayed

verified exact1
verified fuzzy33
unresolved15
parse uncertain0
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External citation measurements

No source-named external measurement is stored.

Outbound references

Observation 86bec183-9dfd-4ca4-ab9b-385c5ff53eca · outbound

This paper cites Weight Factorization and Centralization for Continual Learning in Speech Recognition.

Weight Factorization and Centralization for Continual Learning in Speech Recognition Weight Factorization and Centralization for Continual Learning in Speech Recognition

Reference 1

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Observation 16ac5445-9366-4879-a2dd-6d9474c637c3 · outbound

This paper cites Each dataset Dt consists of samples (xi t,y i t) be- ing the input utterances and labels respectively.

Weight Factorization and Centralization for Continual Learning in Speech Recognition Each dataset Dt consists of samples (xi t,y i t) be- ing the input utterances and labels respectively

Reference 2

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T19:28:46.341189Z

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Observation 3595a2cb-c809-4434-b269-e6b365001277 · outbound

This paper cites In the factoriza- tion phase, we aim to expand the knowledge base model using temporary, learnable adapters on the datasets to be learned.

Weight Factorization and Centralization for Continual Learning in Speech Recognition In the factoriza- tion phase, we aim to expand the knowledge base model using temporary, learnable adapters on the datasets to be learned

Reference 3

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T19:28:46.311349Z

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Observation 27881075-dbac-4495-805a-accf29746f66 · outbound

This paper cites In practice, however, model weights have different standard deviation because the regular- ization term is dominated by the main cross entropy loss, which depends on the data.

Weight Factorization and Centralization for Continual Learning in Speech Recognition In practice, however, model weights have different standard deviation because the regular- ization term is dominated by the main cross entropy loss, which depends on the data

Reference 4

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Observation 96161897-b426-4546-8415-b82d74b3147b · outbound

This paper cites recovered.

Weight Factorization and Centralization for Continual Learning in Speech Recognition recovered

Reference 5

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Observation 90f34976-d360-4f76-8003-7cc1cf69ec9c · outbound

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Weight Factorization and Centralization for Continual Learning in Speech Recognition Unresolved cited work

Reference 6

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Observation efa0318f-8f78-4a5a-8923-15fd957e5f3b · outbound

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Weight Factorization and Centralization for Continual Learning in Speech Recognition Unresolved cited work

Reference 7

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Observation c313520d-5190-40db-b651-a86776718844 · outbound

This paper cites Robust speech recognition via large-scale weak su- pervision,.

Weight Factorization and Centralization for Continual Learning in Speech Recognition Robust speech recognition via large-scale weak su- pervision,

Reference 8

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T19:28:46.103511Z

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Observation 33a0ee30-9c05-42c8-abc2-c051ec96a444 · outbound

This paper cites Code-Switching without Switching: Language Agnostic End-to-End Speech Translation.

Weight Factorization and Centralization for Continual Learning in Speech Recognition Code-Switching without Switching: Language Agnostic End-to-End Speech Translation

Reference 9

Resolution: unresolved; no resolver link, observed 2026-08-15T19:28:43.616801Z

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Observation 77fd3e5c-215e-4bfc-b812-eb95945f5c57 · outbound

This paper cites Automatic extraction of named entity translingual equivalence based on multi-feature cost minimization,.

Weight Factorization and Centralization for Continual Learning in Speech Recognition Automatic extraction of named entity translingual equivalence based on multi-feature cost minimization,

Reference 10

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T19:28:46.089905Z

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Observation fbe91850-21d1-4364-acaa-1728a545cfbe · outbound

This paper cites Towards better language models for spontaneous speech,.

Weight Factorization and Centralization for Continual Learning in Speech Recognition Towards better language models for spontaneous speech,

Reference 11

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T19:28:46.074462Z

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Observation 11043844-3805-4b0a-a7b2-7e1b951f8776 · outbound

This paper cites Multilingual ar- ticulatory features,.

Weight Factorization and Centralization for Continual Learning in Speech Recognition Multilingual ar- ticulatory features,

Reference 12

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T19:28:45.966295Z

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Observation c3acd7f7-9041-4238-823e-9a37715ae02e · outbound

This paper cites Simultaneous translation of open do- main lectures and speeches,.

Weight Factorization and Centralization for Continual Learning in Speech Recognition Simultaneous translation of open do- main lectures and speeches,

Reference 13

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Observation d9eb4fa1-659a-4838-b344-f86aa05c241e · outbound

This paper cites Chil: Computers in the human interaction loop,.

Weight Factorization and Centralization for Continual Learning in Speech Recognition Chil: Computers in the human interaction loop,

Reference 14

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Observation 5ff45b22-5a7a-4134-86ff-179bab844f75 · outbound

This paper cites Efficient Weight Factorization for Multilingual Speech Recognition,.

Weight Factorization and Centralization for Continual Learning in Speech Recognition Efficient Weight Factorization for Multilingual Speech Recognition,

Reference 15

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T19:28:45.880195Z

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Observation d859b56d-fa85-4d56-82ec-b9ae0f076eff · outbound

This paper cites LoRA: Low-Rank Adaptation of Large Language Models.

Weight Factorization and Centralization for Continual Learning in Speech Recognition LoRA: Low-Rank Adaptation of Large Language Models

Reference 16

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Observation 9a50ccb8-819e-4e2d-a424-2f4bba5b2a8c · outbound

This paper cites Catastrophic forgetting, rehearsal and pseudore- hearsal,.

Weight Factorization and Centralization for Continual Learning in Speech Recognition Catastrophic forgetting, rehearsal and pseudore- hearsal,

Reference 17

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T19:28:45.866488Z

Source-reported events for the cited work

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Observation 712872d8-3af5-48f3-bc12-762bf2c70380 · outbound

This paper cites Continual learning with deep generative replay,.

Weight Factorization and Centralization for Continual Learning in Speech Recognition Continual learning with deep generative replay,

Reference 18

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T19:28:45.854702Z

Source-reported events for the cited work

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Observation c00229c5-57da-4fc9-8a91-7252e2eff6cd · outbound

This paper cites Overcoming catastrophic forgetting in neural net- works,.

Weight Factorization and Centralization for Continual Learning in Speech Recognition Overcoming catastrophic forgetting in neural net- works,

Reference 19

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T19:28:45.718274Z

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Observation 9f25b8c8-1bac-4c70-8b8b-aaf2b7465105 · outbound

This paper cites Regularize, expand and compress: Non- expansive continual learning,.

Weight Factorization and Centralization for Continual Learning in Speech Recognition Regularize, expand and compress: Non- expansive continual learning,

Reference 20

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T19:28:45.683431Z

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Observation 042ed793-8cc5-4e7b-8d7b-63e224b6bf19 · outbound

This paper cites Progressive Prompts: Continual Learning for Language Models.

Weight Factorization and Centralization for Continual Learning in Speech Recognition Progressive Prompts: Continual Learning for Language Models

Reference 21

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Observation ccb23292-478d-4e65-822e-c78129e04f40 · outbound

This paper cites Towards continually learning new languages,.

Weight Factorization and Centralization for Continual Learning in Speech Recognition Towards continually learning new languages,

Reference 22

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T19:28:45.671172Z

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Observation 86715044-9260-4c6d-99e2-00496d2b7794 · outbound

This paper cites Averaging Weights Leads to Wider Optima and Better Generalization.

Weight Factorization and Centralization for Continual Learning in Speech Recognition Averaging Weights Leads to Wider Optima and Better Generalization

Reference 23

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Observation 7a8723db-cf55-485a-aa2a-c9176b3814b7 · outbound

This paper cites Streaming diloco with overlapping communication: Towards a distributed free lunch.

Weight Factorization and Centralization for Continual Learning in Speech Recognition Streaming diloco with overlapping communication: Towards a distributed free lunch

Reference 24

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T19:28:45.657716Z

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Observation e281d27e-9fa8-4840-a92c-1b90df5683ca · outbound

This paper cites wav2vec 2.0: A framework for self-supervised learning of speech representa- tions,.

Weight Factorization and Centralization for Continual Learning in Speech Recognition wav2vec 2.0: A framework for self-supervised learning of speech representa- tions,

Reference 25

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T19:28:45.612187Z

Source-reported events for the cited work

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Observation a8e4924f-f6e9-4b69-a21e-fa289d6a5c86 · outbound

This paper cites Continual learning through synaptic intelligence,.

Weight Factorization and Centralization for Continual Learning in Speech Recognition Continual learning through synaptic intelligence,

Reference 26

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T19:28:45.566720Z

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Observation f6aa6011-2416-4a7d-9528-520eeaad4080 · outbound

This paper cites Variational Continual Learning.

Weight Factorization and Centralization for Continual Learning in Speech Recognition Variational Continual Learning

Reference 27

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Observation 71114be4-e726-4dc0-82f2-3070e1c1f632 · outbound

This paper cites A Unifying Bayesian View of Continual Learning.

Weight Factorization and Centralization for Continual Learning in Speech Recognition A Unifying Bayesian View of Continual Learning

Reference 28

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Observation f37296ca-1387-4ba5-a38f-1f215d4e07a8 · outbound

This paper cites Gradient episodic memory for continual learning,.

Weight Factorization and Centralization for Continual Learning in Speech Recognition Gradient episodic memory for continual learning,

Reference 29

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Observation 2d16ff2c-0212-43ef-bf55-bee7b39e3ca8 · outbound

This paper cites Coda-prompt: Con- tinual decomposed attention-based prompting for rehearsal-free continual learning,.

Weight Factorization and Centralization for Continual Learning in Speech Recognition Coda-prompt: Con- tinual decomposed attention-based prompting for rehearsal-free continual learning,

Reference 30

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T19:28:45.544374Z

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Observation 9881e036-f86a-4a45-99b2-f9f66ab4e9ad · outbound

This paper cites Learn and don’t forget: Adding a new language to asr foundation models,.

Weight Factorization and Centralization for Continual Learning in Speech Recognition Learn and don’t forget: Adding a new language to asr foundation models,

Reference 31

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T19:28:45.462694Z

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Observation 541a83ca-17dd-44ab-ac5b-1162d732618f · outbound

This paper cites Continuously learning new words in automatic speech recognition,.

Weight Factorization and Centralization for Continual Learning in Speech Recognition Continuously learning new words in automatic speech recognition,

Reference 32

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T19:28:45.363527Z

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Observation c5ec2e8b-5fde-45a9-b6f7-e15118c557c2 · outbound

This paper cites Rehearsal-free online continual learning for automatic speech recognition,.

Weight Factorization and Centralization for Continual Learning in Speech Recognition Rehearsal-free online continual learning for automatic speech recognition,

Reference 33

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T19:28:45.351163Z

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Observation f1b9bf22-3cc2-4a38-bd86-bc590111b06a · outbound

This paper cites Lvcsr-based language iden- tification,.

Weight Factorization and Centralization for Continual Learning in Speech Recognition Lvcsr-based language iden- tification,

Reference 34

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Observation bf82ba1f-3108-4487-8950-b6c92be4cef0 · outbound

This paper cites Experiments on cross-language acous- tic modeling.

Weight Factorization and Centralization for Continual Learning in Speech Recognition Experiments on cross-language acous- tic modeling

Reference 35

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Observation 86ea7d43-f9aa-456c-9037-763aa1dc7d40 · outbound

This paper cites Language-agnostic Code-Switching in Sequence-To-Sequence Speech Recognition.

Weight Factorization and Centralization for Continual Learning in Speech Recognition Language-agnostic Code-Switching in Sequence-To-Sequence Speech Recognition

Reference 36

Resolution: unresolved; no resolver link, observed 2026-08-15T19:28:43.972090Z

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Observation 1c84c5bc-f9e7-43e5-8be2-21b8fc2afb92 · outbound

This paper cites Arzen: A speech corpus for code-switched egyptian arabic-english,.

Weight Factorization and Centralization for Continual Learning in Speech Recognition Arzen: A speech corpus for code-switched egyptian arabic-english,

Reference 37

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T19:28:45.230581Z

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Observation 6e07ff12-a1ac-413e-90e7-1ffd5d500043 · outbound

This paper cites End-to-end speech translation for code switched speech,.

Weight Factorization and Centralization for Continual Learning in Speech Recognition End-to-end speech translation for code switched speech,

Reference 38

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T19:28:45.160762Z

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Observation aa528a4b-12ce-46e7-af97-787447e6f6f1 · outbound

This paper cites Seame: a mandarin-english code- switching speech corpus in south-east asia.

Weight Factorization and Centralization for Continual Learning in Speech Recognition Seame: a mandarin-english code- switching speech corpus in south-east asia

Reference 39

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T19:28:45.149582Z

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Observation d3cd27a0-c9dd-4217-8a63-26216d407df0 · outbound

This paper cites Leveraging data collec- tion and unsupervised learning for code-switched tunisian arabic automatic speech recognition,.

Weight Factorization and Centralization for Continual Learning in Speech Recognition Leveraging data collec- tion and unsupervised learning for code-switched tunisian arabic automatic speech recognition,

Reference 40

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T19:28:45.059833Z

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Observation 09f182a6-c8f8-4ab2-8e05-e94f350bfcf1 · outbound

This paper cites ASCEND: A spontaneous Chinese-English dataset for code- switching in multi-turn conversation,.

Weight Factorization and Centralization for Continual Learning in Speech Recognition ASCEND: A spontaneous Chinese-English dataset for code- switching in multi-turn conversation,

Reference 41

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T19:28:45.029948Z

Source-reported events for the cited work

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Observation d4fcc40c-33ca-4774-8c4c-2c1b1ebc7028 · outbound

This paper cites Talcs: An open-source mandarin-english code-switching corpus and a speech recognition baseline.

Weight Factorization and Centralization for Continual Learning in Speech Recognition Talcs: An open-source mandarin-english code-switching corpus and a speech recognition baseline

Reference 42

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T19:28:45.018508Z

Source-reported events for the cited work

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Observation fd2a9c69-4084-49b4-b153-034f7168c3ea · outbound

This paper cites PIER: A Novel Metric for Evaluating What Matters in Code-Switching.

Weight Factorization and Centralization for Continual Learning in Speech Recognition PIER: A Novel Metric for Evaluating What Matters in Code-Switching

Reference 43

Resolution: unresolved; no resolver link, observed 2026-08-15T19:28:44.207119Z

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Observation 430c6860-3231-4a31-a6c7-9316dabc0a1e · outbound

This paper cites Ted-lium 3: Twice as much data and corpus repartition for experiments on speaker adaptation,.

Weight Factorization and Centralization for Continual Learning in Speech Recognition Ted-lium 3: Twice as much data and corpus repartition for experiments on speaker adaptation,

Reference 44

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T19:28:44.932216Z

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 769d5bf9-f891-4643-9701-af3b309e9089 · outbound

This paper cites Mls: A large-scale multilingual dataset for speech research,.

Weight Factorization and Centralization for Continual Learning in Speech Recognition Mls: A large-scale multilingual dataset for speech research,

Reference 45

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T19:28:44.869096Z

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 00f0b73e-234d-40a8-99b8-66e0d7a02ee9 · outbound

This paper cites The mgb-2 challenge: Arabic multi-dialect broadcast media recognition,.

Weight Factorization and Centralization for Continual Learning in Speech Recognition The mgb-2 challenge: Arabic multi-dialect broadcast media recognition,

Reference 46

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T19:28:44.858605Z

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 3249b781-8d7d-4e15-b078-6815244fef8f · outbound

This paper cites Multilingual speech recognition for turkic languages,.

Weight Factorization and Centralization for Continual Learning in Speech Recognition Multilingual speech recognition for turkic languages,

Reference 47

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T19:28:44.834564Z

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 670cade1-326d-4b92-8b54-8d986c3c307b · outbound

This paper cites Aishell-1: An open- source mandarin speech corpus and a speech recognition base- line,.

Weight Factorization and Centralization for Continual Learning in Speech Recognition Aishell-1: An open- source mandarin speech corpus and a speech recognition base- line,

Reference 48

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T19:28:44.675398Z

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 314b773e-53eb-4d1b-8086-d61fce78bbfa · outbound

This paper cites Common voice: A massively- multilingual speech corpus,.

Weight Factorization and Centralization for Continual Learning in Speech Recognition Common voice: A massively- multilingual speech corpus,

Reference 49

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T19:28:44.663990Z

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 3e8243ba-65b3-4ecd-b4e5-ba2cafabc1dc · outbound

This paper cites Decm: Evaluating bilin- gual asr performance on a code-switching/mixing benchmark,.

Weight Factorization and Centralization for Continual Learning in Speech Recognition Decm: Evaluating bilin- gual asr performance on a code-switching/mixing benchmark,

Reference 50

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T19:28:44.652263Z

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 df4bf1f7-62c9-4e15-bb4a-884b4e7ee884 · outbound

This paper cites Dark experience for general continual learning: a strong, sim- ple baseline,.

Weight Factorization and Centralization for Continual Learning in Speech Recognition Dark experience for general continual learning: a strong, sim- ple baseline,

Reference 51

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Observation 86bec183-9dfd-4ca4-ab9b-385c5ff53eca · inbound