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Paper Citation Record · LEDGER

Feature Partitioning for Efficient Multi-Task Architectures

As of 16 August 2026, this Paper Citation Record lists 39 of 39 outbound references and 1 inbound Pith citation observation for arXiv:1908.04339.

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

pith.paper-citation-record.v1
1908.04339 v1

Coverage vector

measured 39 of 39 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-14T13:48:45.703376Z

measured 40 of 40 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 1 of 1 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-10T17:39:42.992654Z

measured 0 of 1 external citation measurements

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

Source: pith, observed 2026-08-10T17:39:44.298775Z

Reference resolution

39 of 39 outbound references displayed

  • verified exact5
  • verified fuzzy5
  • unresolved29
  • parse uncertain0
  • malformed identifier0
  • metadata mismatch0

External citation measurements

No source-named external measurement is stored.

Outbound references

Observation a93f9279-6909-4c14-835a-64913ef243b2 · outbound

This paper cites Accelerating Neural Architecture Search using Performance Prediction.

Feature Partitioning for Efficient Multi-Task Architectures Accelerating Neural Architecture Search using Performance Prediction

Reference 1

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Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-14T13:48:45.120571Z digest=sha256:72cf0979687b996d3dc349aeea9155dfcb48ab88d763b2a94d4ab031c2260b7f

Observation b17b1c76-11c0-41f8-868e-71de72b1025b · outbound

This paper cites Combating catastrophic forgetting with developmental compression.

Feature Partitioning for Efficient Multi-Task Architectures Combating catastrophic forgetting with developmental compression

Reference 2

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local_arxiv, observed 2026-08-14T13:48:46.089376Z

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.

source=pdf_text observed=2026-08-14T13:48:45.158983Z digest=sha256:d0cb26f5a96951a766727ef74e56fb0f8a9fe793f8327ceec28213d3b046fb31

Observation 9d6a5056-4db8-47de-a641-dddf75d40690 · outbound

This paper cites SMASH: One-Shot Model Architecture Search through HyperNetworks.

Feature Partitioning for Efficient Multi-Task Architectures SMASH: One-Shot Model Architecture Search through HyperNetworks

Reference 3

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source=pdf_text observed=2026-08-14T13:48:45.189139Z digest=sha256:7caf6923024b90c3193be0072e5b3fff77701f447b6adcec2038715e60fafcf2

Observation 6687794c-fa87-47bc-9323-cb49a4d0db94 · outbound

This paper cites an unresolved cited work.

Feature Partitioning for Efficient Multi-Task Architectures Unresolved cited work

Reference 4

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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.

source=pdf_text observed=2026-08-14T13:48:45.247263Z digest=sha256:db2a4d01b4a7d6ee0afb03df036795396f094bda2c48e40a3e463a0669d0f0aa

Observation 21ff7864-f960-4294-afc4-a5ec2ab3c90b · outbound

This paper cites Unifying and Merging Well-trained Deep Neural Networks for Inference Stage.

Feature Partitioning for Efficient Multi-Task Architectures Unifying and Merging Well-trained Deep Neural Networks for Inference Stage

Reference 5

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local_arxiv, observed 2026-08-14T13:48:46.035130Z

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.

source=pdf_text observed=2026-08-14T13:48:45.251914Z digest=sha256:694102b2af5bcdea5864fdf161d8e426706a911f2fa9a98613d87945ec15bf64

Observation aedb617d-6b65-4ac1-80ef-be6601020a37 · outbound

This paper cites Peephole: Predicting Network Performance Before Training.

Feature Partitioning for Efficient Multi-Task Architectures Peephole: Predicting Network Performance Before Training

Reference 6

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source=pdf_text observed=2026-08-14T13:48:45.255964Z digest=sha256:f046ad623b008f8209b58543e0198d726602adf22942a405d5839a8d93021b85

Observation 3daed03e-eb30-4992-bee1-965747f5e13b · outbound

This paper cites IMPALA: Scalable Distributed Deep-RL with Importance Weighted Actor-Learner Architectures.

Feature Partitioning for Efficient Multi-Task Architectures IMPALA: Scalable Distributed Deep-RL with Importance Weighted Actor-Learner Architectures

Reference 7

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source=pdf_text observed=2026-08-14T13:48:45.260051Z digest=sha256:1c6d3eb1d274fe6df562fb7ad9021884bf9b21d741dfaf0378decb74aa4effb7

Observation 60e64d79-696a-4d30-952d-4d9b2d57712f · outbound

This paper cites PathNet: Evolution Channels Gradient Descent in Super Neural Networks.

Feature Partitioning for Efficient Multi-Task Architectures PathNet: Evolution Channels Gradient Descent in Super Neural Networks

Reference 8

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source=pdf_text observed=2026-08-14T13:48:45.263111Z digest=sha256:2550aa850d89a788d498334b554696b501e971417047c13e5f9cdd068f579ff8

Observation 94a8378f-adb5-4b53-b333-6ea9788b4fc7 · outbound

This paper cites an unresolved cited work.

Feature Partitioning for Efficient Multi-Task Architectures Unresolved cited work

Reference 9

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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.

source=pdf_text observed=2026-08-14T13:48:45.267692Z digest=sha256:35be98f7bff5b3b1a9fceeec8ce2c5daa12b4956b5dd21e3827831805ccf838f

Observation a7701be5-04c3-450d-89b0-3f1d2a3903cc · outbound

This paper cites Multi-Task Zipping via Layer-wise Neuron Sharing.

Feature Partitioning for Efficient Multi-Task Architectures Multi-Task Zipping via Layer-wise Neuron Sharing

Reference 10

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local_arxiv, observed 2026-08-14T13:48:45.992999Z

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

source=pdf_text observed=2026-08-14T13:48:45.270696Z digest=sha256:98cc3bad5a8881b76b2dcebff994804a7c8de83c9d62dd60e25bcf99bf0dfa00

Observation afdc6f0e-341f-461d-9ccc-1a63e5eb4554 · outbound

This paper cites Jiang, Z.

Feature Partitioning for Efficient Multi-Task Architectures Jiang, Z

Reference 11

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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.

source=pdf_text observed=2026-08-14T13:48:45.273920Z digest=sha256:d275f182e69850845b8256fbfb516b3aabcb7770a2e8cf9327447411f264b866

Observation 3edaf94f-8c75-45d0-b1f9-3fac8a0fcd20 · outbound

This paper cites One Model To Learn Them All.

Feature Partitioning for Efficient Multi-Task Architectures One Model To Learn Them All

Reference 12

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source=pdf_text observed=2026-08-14T13:48:45.277322Z digest=sha256:589552b70ce96f13627573bedf8056eb98f7739e43c30db4c4392e0647262a4d

Observation 1a48352f-1986-4db8-8d50-4bb9820293de · outbound

This paper cites Parallel Architecture and Hyperparameter Search via Successive Halving and Classification.

Feature Partitioning for Efficient Multi-Task Architectures Parallel Architecture and Hyperparameter Search via Successive Halving and Classification

Reference 13

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local_arxiv, observed 2026-08-14T13:48:45.969136Z

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.

source=pdf_text observed=2026-08-14T13:48:45.280917Z digest=sha256:2f9b7552913e64333c52c9462410a14f88af48423a744a2bfd5b6d0bcf34603d

Observation 4084755f-01a0-48d4-8621-fa5269e88ba8 · outbound

This paper cites Random Search and Reproducibility for Neural Architecture Search.

Feature Partitioning for Efficient Multi-Task Architectures Random Search and Reproducibility for Neural Architecture Search

Reference 14

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source=pdf_text observed=2026-08-14T13:48:45.284290Z digest=sha256:18e3fa75aeea6641a729b593fe30713cdf6bf331599203a240ff4bde1ee0917b

Observation df485cd3-4600-4775-a9f7-b84d98350847 · outbound

This paper cites Evolutionary Architecture Search For Deep Multitask Networks.

Feature Partitioning for Efficient Multi-Task Architectures Evolutionary Architecture Search For Deep Multitask Networks

Reference 15

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local_arxiv, observed 2026-08-14T13:48:45.945921Z

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.

source=pdf_text observed=2026-08-14T13:48:45.298147Z digest=sha256:a0718e0e05781e1c260612d1f20bf8397911f7445f0460208a1374d22f9105d0

Observation 84981c47-7659-4d00-8995-bf392c91bba9 · outbound

This paper cites Progressive Neural Architecture Search.

Feature Partitioning for Efficient Multi-Task Architectures Progressive Neural Architecture Search

Reference 16

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source=pdf_text observed=2026-08-14T13:48:45.344249Z digest=sha256:fdb52a11551fa2d73aedeecf1bf1738d399f94bbb0af58c4d6e43318bfc4a487

Observation 4a1629b9-e150-4c31-9d3d-56835b585deb · outbound

This paper cites Hierarchical Representations for Efficient Architecture Search.

Feature Partitioning for Efficient Multi-Task Architectures Hierarchical Representations for Efficient Architecture Search

Reference 17

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source=pdf_text observed=2026-08-14T13:48:45.402006Z digest=sha256:7ad28a64e08a08576c0068edf25a3461574f1460ccb6afb08a262950d6d1cd65

Observation 3a5b3ea8-e961-4f1b-8331-d22f824836ce · outbound

This paper cites End-to-End Multi-Task Learning with Attention.

Feature Partitioning for Efficient Multi-Task Architectures End-to-End Multi-Task Learning with Attention

Reference 18

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source=pdf_text observed=2026-08-14T13:48:45.434360Z digest=sha256:52998e7b3a19163f50cc08bd68c364749fef30c829c0bc503a6937b4a38cfdc7

Observation 01bc20a8-0b77-4102-9b74-7b992ea87ae8 · outbound

This paper cites Piggyback: Adapting a Single Network to Multiple Tasks by Learning to Mask Weights.

Feature Partitioning for Efficient Multi-Task Architectures Piggyback: Adapting a Single Network to Multiple Tasks by Learning to Mask Weights

Reference 19

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source=pdf_text observed=2026-08-14T13:48:45.438621Z digest=sha256:bd5a88e5ad562c0dec3aada896e624a09e858b21bf6b8028aa1a5c005db79db4

Observation 11e3ff6d-0fc1-4bf5-8fce-f192cf6d8ec7 · outbound

This paper cites Simple random search provides a competitive approach to reinforcement learning.

Feature Partitioning for Efficient Multi-Task Architectures Simple random search provides a competitive approach to reinforcement learning

Reference 20

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source=pdf_text observed=2026-08-14T13:48:45.442202Z digest=sha256:35440223e2fc1c341fd6ae8f1a2d611cb5166097819d81ca35786c8cd01084bd

Observation 66fb5556-903d-4335-bd44-a786089d0b55 · outbound

This paper cites Beyond Shared Hierarchies: Deep Multitask Learning through Soft Layer Ordering.

Feature Partitioning for Efficient Multi-Task Architectures Beyond Shared Hierarchies: Deep Multitask Learning through Soft Layer Ordering

Reference 21

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source=pdf_text observed=2026-08-14T13:48:45.445904Z digest=sha256:63b238b02d5da702a74e8b22228b7f8be2e56c46d8649ce3bace980f00f224ad

Observation 729a35d0-98bd-4958-ad00-834d76b6b745 · outbound

This paper cites Misra, A.

Feature Partitioning for Efficient Multi-Task Architectures Misra, A

Reference 22

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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.

source=pdf_text observed=2026-08-14T13:48:45.449115Z digest=sha256:ae2fbe9e0e2fba68e95a852a11c4d791c6fcef820b4c69eebf4137bf82dca376

Observation 2f21672a-0fc1-4401-b739-7df629c26086 · outbound

This paper cites Efficient Neural Architecture Search via Parameter Sharing.

Feature Partitioning for Efficient Multi-Task Architectures Efficient Neural Architecture Search via Parameter Sharing

Reference 23

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source=pdf_text observed=2026-08-14T13:48:45.452439Z digest=sha256:e019e0a04bfd2a881310bc18ed7f7e2be9ea071c4319548ba9343760f98de3ab

Observation 5114cc11-b047-4188-a69a-9b4ee6cc3e30 · outbound

This paper cites Regularized Evolution for Image Classifier Architecture Search.

Feature Partitioning for Efficient Multi-Task Architectures Regularized Evolution for Image Classifier Architecture Search

Reference 24

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source=pdf_text observed=2026-08-14T13:48:45.455667Z digest=sha256:b13ced2cd9a5d2af42c2c7a53ff8e613a7b14146f3b43b253089181675f4106c

Observation 3c1d5de6-84f0-4597-b486-87e052576f11 · outbound

This paper cites Rebuffi, H.

Feature Partitioning for Efficient Multi-Task Architectures Rebuffi, H

Reference 25

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

source=pdf_text observed=2026-08-14T13:48:45.459011Z digest=sha256:aefcd38080acd842f6d629d83d092340642a4da3534c5b8de1d6ddcdf3d3d4fe

Observation 0292eeb9-7e9b-4d26-b75d-b1ccf4560563 · outbound

This paper cites Rebuffi, H.

Feature Partitioning for Efficient Multi-Task Architectures Rebuffi, H

Reference 26

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

source=pdf_text observed=2026-08-14T13:48:45.461608Z digest=sha256:b7caf5864d192feac9c059f772b195ecf72a308535d6e0af8a0a60fe5ce6af5a

Observation ba6e41c5-f6aa-4408-b828-51c7fdad2482 · outbound

This paper cites Routing Networks: Adaptive Selection of Non-linear Functions for Multi-Task Learning.

Feature Partitioning for Efficient Multi-Task Architectures Routing Networks: Adaptive Selection of Non-linear Functions for Multi-Task Learning

Reference 27

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source=pdf_text observed=2026-08-14T13:48:45.464569Z digest=sha256:17e5d4a930399b3a1ff0735241dee1ded15c4dfd30ce96727c2537acc7acabd0

Observation 671abd4e-c100-4853-bd25-f98fc3ef1601 · outbound

This paper cites Incremental Learning Through Deep Adaptation.

Feature Partitioning for Efficient Multi-Task Architectures Incremental Learning Through Deep Adaptation

Reference 28

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source=pdf_text observed=2026-08-14T13:48:45.467820Z digest=sha256:9cc43f5fec4be6e57f32d3ecf8d66cb8d3b7ae9d502e4e5de037560d82efca1f

Observation cd3a64ed-3906-4727-8948-55b17c8a0147 · outbound

This paper cites An Overview of Multi-Task Learning in Deep Neural Networks.

Feature Partitioning for Efficient Multi-Task Architectures An Overview of Multi-Task Learning in Deep Neural Networks

Reference 29

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source=pdf_text observed=2026-08-14T13:48:45.471517Z digest=sha256:bc526b0bc3d1538b2aec08dd21440646d976e46db6c8040da48c1c90447913aa

Observation 4de0f676-4a73-4b57-bba7-fa80ae4a0c93 · outbound

This paper cites Latent Multi-task Architecture Learning.

Feature Partitioning for Efficient Multi-Task Architectures Latent Multi-task Architecture Learning

Reference 30

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source=pdf_text observed=2026-08-14T13:48:45.475055Z digest=sha256:0406bb95a62c412634308d873f7daa68639a354ec25734162d099792ec735095

Observation 10e5dd78-efb5-44da-835d-ce9a97664265 · outbound

This paper cites Policy Distillation.

Feature Partitioning for Efficient Multi-Task Architectures Policy Distillation

Reference 31

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source=pdf_text observed=2026-08-14T13:48:45.479129Z digest=sha256:eda771337c480ae1724f7604334fe5f0b1de316be10397322a909a6fe6b692cd

Observation 3b3be701-7cd8-4b75-be77-73e5a9f0424b · outbound

This paper cites Learning to Multi-Task by Active Sampling.

Feature Partitioning for Efficient Multi-Task Architectures Learning to Multi-Task by Active Sampling

Reference 32

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source=pdf_text observed=2026-08-14T13:48:45.565773Z digest=sha256:f9c662dcd6d6179c4929b98bbd2872116ffe0650df66ff7f19f01a91d696935c

Observation 02d40ef2-1a39-44e5-9a95-5b8c0469ac40 · outbound

This paper cites Wierstra, T.

Feature Partitioning for Efficient Multi-Task Architectures Wierstra, T

Reference 33

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raw_fallback, observed 2026-08-14T13:48:46.200483Z

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

source=pdf_text observed=2026-08-14T13:48:45.681194Z digest=sha256:813ae9e93e540c5e52bf4ed747a44de598e7588e01d5ba9f3fcc79c7e3e2a29d

Observation 955a2468-9503-41bb-9c32-9249620eb4e9 · outbound

This paper cites an unresolved cited work.

Feature Partitioning for Efficient Multi-Task Architectures Unresolved cited work

Reference 34

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

source=pdf_text observed=2026-08-14T13:48:45.684757Z digest=sha256:2bbf742fb2ded400471dc844887b2539a8b6693f592183c13c94f32f4bd1aa12

Observation bf0a9037-31af-4f59-8985-c4e521bfaf26 · outbound

This paper cites an unresolved cited work.

Feature Partitioning for Efficient Multi-Task Architectures Unresolved cited work

Reference 35

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source=pdf_text observed=2026-08-14T13:48:45.688811Z digest=sha256:78c6660cf8f765ffb1ab66b767c1d90d32c00ddfd73e7c9b7a9ffab66ef0624e

Observation 394570f2-8970-4544-b58d-f9171025936b · outbound

This paper cites an unresolved cited work.

Feature Partitioning for Efficient Multi-Task Architectures Unresolved cited work

Reference 36

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

source=pdf_text observed=2026-08-14T13:48:45.692067Z digest=sha256:ddcb768b93c0af6916a87bfb23ece719ddc3040067ace98fd8bce9d66ddf97b7

Observation 372e991a-b9f4-427f-af29-80b8be56d603 · outbound

This paper cites Towards Automated Deep Learning: Efficient Joint Neural Architecture and Hyperparameter Search.

Feature Partitioning for Efficient Multi-Task Architectures Towards Automated Deep Learning: Efficient Joint Neural Architecture and Hyperparameter Search

Reference 37

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source=pdf_text observed=2026-08-14T13:48:45.696321Z digest=sha256:94e59e1ce6e0dc63b4e8ca0cd53fb5e61ab1c3dafd4286e41fc3870d020cb666

Observation 2c5f8d7d-2e96-41fb-b9b8-046e3d6ae77c · outbound

This paper cites A Survey on Multi-Task Learning.

Feature Partitioning for Efficient Multi-Task Architectures A Survey on Multi-Task Learning

Reference 38

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Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-14T13:48:45.699797Z digest=sha256:3068cb73e4d799e0f97cec7f7870a3c761da1d0210b5df93a52fe607e25944e9

Observation 57d3b437-3398-4c01-bfcb-40f5abc42511 · outbound

This paper cites Neural Architecture Search with Reinforcement Learning.

Feature Partitioning for Efficient Multi-Task Architectures Neural Architecture Search with Reinforcement Learning

Reference 39

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unresolved
no resolver link, observed 2026-08-14T13:48:45.703376Z

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source=pdf_text observed=2026-08-14T13:48:45.703376Z digest=sha256:782f8c0bd56f3ca2d6fd1c4f533f257b7847660f2d8e0d898c05408b72164344

Pith citing papers

Observation 99a35d9c-0a63-44ff-8d8d-1b1b9a40d7ad · inbound

Learning in Deep Networks under Dale's Constraint cites this paper.

Learning in Deep Networks under Dale's Constraint Feature Partitioning for Efficient Multi-Task Architectures

Reference 167

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local_arxiv, observed 2026-08-10T17:39:44.303990Z

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source=arxiv_source observed=2026-08-10T17:39:42.992654Z digest=sha256:3b470121266f6556dece0d0b2df8a3869f193a4635cdc678b6402bd47418ab67