Paper Citation Record · LEDGER

LCA: Loss Change Allocation for Neural Network Training

As of 18 August 2026, this Paper Citation Record lists 36 of 36 outbound references and 1 inbound Pith citation observation for arXiv:1909.01440.

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

pith.paper-citation-record.v1
1909.01440 v2

Coverage vector

measured 36 of 36 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-14T05:23:19.751654Z

measured 37 of 37 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-18T06:34:40.430872+00:00

measured 1 of 1 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-14T10:10:09.804381Z

measured 0 of 1 external citation measurements

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

Source: pith, observed 2026-08-14T10:10:10.035485Z

Reference resolution

36 of 36 outbound references displayed

verified exact1
verified fuzzy16
unresolved18
parse uncertain0
malformed identifier1
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External citation measurements

No source-named external measurement is stored.

Outbound references

Observation 1278211e-ddab-4091-ae2c-3d7bf050033e · outbound

This paper cites Critical Learning Periods in Deep Neural Networks.

LCA: Loss Change Allocation for Neural Network Training Critical Learning Periods in Deep Neural Networks

Reference 1

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Observation 9eaeb2a4-4f61-47c8-b15b-3ea10fbbbc7e · outbound

This paper cites Alain and Y.

LCA: Loss Change Allocation for Neural Network Training Alain and Y

Reference 2

Resolution: verified fuzzy; raw_fallback, observed 2026-08-14T05:23:20.370337Z

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No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

Observation 3c24fb10-5a75-4d85-9a68-a5171f2ab56c · outbound

This paper cites Optimization methods for large-scale machine learning.

LCA: Loss Change Allocation for Neural Network Training Optimization methods for large-scale machine learning

Reference 3

Resolution: verified fuzzy; raw_fallback, observed 2026-08-14T05:23:20.353963Z

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Observation 9a02fdb2-dc21-4539-b3bf-72f5d1ba27ed · outbound

This paper cites The loss surfaces of multilayer networks.

LCA: Loss Change Allocation for Neural Network Training The loss surfaces of multilayer networks

Reference 4

Resolution: verified fuzzy; raw_fallback, observed 2026-08-14T05:23:20.336894Z

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Observation f97b57b8-42f5-4f09-9416-2b7fe76e1a63 · outbound

This paper cites Identifying and attacking the saddle point problem in high-dimensional non- convex optimization.

LCA: Loss Change Allocation for Neural Network Training Identifying and attacking the saddle point problem in high-dimensional non- convex optimization

Reference 5

Resolution: verified fuzzy; raw_fallback, observed 2026-08-14T05:23:20.319487Z

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Observation 925baf4f-c62f-4823-9a93-f90910108b47 · outbound

This paper cites The Lottery Ticket Hypothesis: Finding Sparse, Trainable Neural Networks.

LCA: Loss Change Allocation for Neural Network Training The Lottery Ticket Hypothesis: Finding Sparse, Trainable Neural Networks

Reference 6

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Source-reported events for the cited work

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Observation b621a332-9368-4663-8228-9af28b056197 · outbound

This paper cites Qualitatively characterizing neural network optimization problems.

LCA: Loss Change Allocation for Neural Network Training Qualitatively characterizing neural network optimization problems

Reference 7

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Observation 9ec53c71-e679-44ec-8e6e-6e9fb453721c · outbound

This paper cites A closer look at deep learning heuristics: Learning rate restarts, warmup and distillation.

LCA: Loss Change Allocation for Neural Network Training A closer look at deep learning heuristics: Learning rate restarts, warmup and distillation

Reference 8

Resolution: verified fuzzy; raw_fallback, observed 2026-08-14T05:23:20.303586Z

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Observation 9ae736c1-1d52-42be-9428-1a83acb4002f · outbound

This paper cites Deep Residual Learning for Image Recognition.

LCA: Loss Change Allocation for Neural Network Training Deep Residual Learning for Image Recognition

Reference 9

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Observation 21390072-047f-4bfb-b15f-6b651367063d · outbound

This paper cites Delving deep into rectiﬁers: Surpassing human-level performance on imagenet classiﬁcation.

LCA: Loss Change Allocation for Neural Network Training Delving deep into rectiﬁers: Surpassing human-level performance on imagenet classiﬁcation

Reference 10

Resolution: unresolved; no resolver link, observed 2026-08-14T05:23:19.622568Z

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Observation 9a40c5cd-1c44-4f4a-8ad0-a6382ad9ade9 · outbound

This paper cites Improving neural networks by preventing co-adaptation of feature detectors.

LCA: Loss Change Allocation for Neural Network Training Improving neural networks by preventing co-adaptation of feature detectors

Reference 11

Resolution: unresolved; no resolver link, observed 2026-08-14T05:23:19.627225Z

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Observation 26b1cae8-1a20-4998-b194-fbb3e140c4d5 · outbound

This paper cites Fix your classifier: the marginal value of training the last weight layer.

LCA: Loss Change Allocation for Neural Network Training Fix your classifier: the marginal value of training the last weight layer

Reference 12

Resolution: verified exact; local_arxiv, observed 2026-08-14T05:23:19.982832Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

Observation fdf194b1-1b2b-4736-bd24-987f79de429a · outbound

This paper cites Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift.

LCA: Loss Change Allocation for Neural Network Training Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift

Reference 13

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Observation 36797494-6ff9-4d58-837a-c079faaa5f92 · outbound

This paper cites On the Relation Between the Sharpest Directions of DNN Loss and the SGD Step Length.

LCA: Loss Change Allocation for Neural Network Training On the Relation Between the Sharpest Directions of DNN Loss and the SGD Step Length

Reference 14

Resolution: unresolved; no resolver link, observed 2026-08-14T05:23:19.643754Z

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Observation 9e4aa06e-134f-4bce-aaac-0851722461a8 · outbound

This paper cites On Large-Batch Training for Deep Learning: Generalization Gap and Sharp Minima.

LCA: Loss Change Allocation for Neural Network Training On Large-Batch Training for Deep Learning: Generalization Gap and Sharp Minima

Reference 15

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Observation fdad05f1-61f5-49f6-b02d-14877c8fc8f1 · outbound

This paper cites Rusu, Kieran Milan, John Quan, Tiago Ramalho, Agnieszka Grabska-Barwinska, Demis Hassabis, Claudia Clopath, Dharshan Kumaran, and Raia Hadsell.

LCA: Loss Change Allocation for Neural Network Training Rusu, Kieran Milan, John Quan, Tiago Ramalho, Agnieszka Grabska-Barwinska, Demis Hassabis, Claudia Clopath, Dharshan Kumaran, and Raia Hadsell

Reference 16

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Observation cb2d9ab3-525e-49f9-8121-3311a0762785 · outbound

This paper cites Beitrag zur näherungweisen integration totaler differentialgleichungen.

LCA: Loss Change Allocation for Neural Network Training Beitrag zur näherungweisen integration totaler differentialgleichungen

Reference 17

Resolution: verified fuzzy; raw_fallback, observed 2026-08-14T05:23:20.274993Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

Observation a5c367a6-9fd6-4568-a354-2660f5a0ff75 · outbound

This paper cites Gradient-based learning applied to document recognition.

LCA: Loss Change Allocation for Neural Network Training Gradient-based learning applied to document recognition

Reference 18

Resolution: unresolved; no resolver link, observed 2026-08-14T05:23:19.663521Z

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Observation 16221b14-f67b-41e7-b3b7-69e7022306a9 · outbound

This paper cites Measuring the Intrinsic Dimension of Objective Landscapes.

LCA: Loss Change Allocation for Neural Network Training Measuring the Intrinsic Dimension of Objective Landscapes

Reference 19

Resolution: verified fuzzy; raw_fallback, observed 2026-08-14T05:23:20.246918Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

Observation 98e3768b-3bc2-4956-9929-ffe98b8b6906 · outbound

This paper cites Visualizing the loss landscape of neural nets.

LCA: Loss Change Allocation for Neural Network Training Visualizing the loss landscape of neural nets

Reference 20

Resolution: verified fuzzy; raw_fallback, observed 2026-08-14T05:23:20.230870Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

Observation 561e7922-3bc0-49e3-b5e2-304c7827d9d6 · outbound

This paper cites The loss surface of deep and wide neural networks.

LCA: Loss Change Allocation for Neural Network Training The loss surface of deep and wide neural networks

Reference 21

Resolution: verified fuzzy; raw_fallback, observed 2026-08-14T05:23:20.212767Z

Source-reported events for the cited work

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Observation f0980e5d-f3ac-4391-9432-447a4b329d50 · outbound

This paper cites Raghu, J.

LCA: Loss Change Allocation for Neural Network Training Raghu, J

Reference 22

Resolution: verified fuzzy; raw_fallback, observed 2026-08-14T05:23:20.195558Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

Observation 5b40fb9c-34f3-4fb8-853b-1e9d7349c366 · outbound

This paper cites Über die numerische auﬂösung von differentialgleichungen.

LCA: Loss Change Allocation for Neural Network Training Über die numerische auﬂösung von differentialgleichungen

Reference 23

Resolution: verified fuzzy; raw_fallback, observed 2026-08-14T05:23:20.177571Z

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Observation 390fd79c-859f-4bee-a385-f1e3fcbfc383 · outbound

This paper cites On the quality of the initial basin in overspeciﬁed neural networks.

LCA: Loss Change Allocation for Neural Network Training On the quality of the initial basin in overspeciﬁed neural networks

Reference 24

Resolution: verified fuzzy; raw_fallback, observed 2026-08-14T05:23:20.158951Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

Observation f1dcfb32-9651-49c3-9197-597e91c4d949 · outbound

This paper cites Opening the Black Box of Deep Neural Networks via Information.

LCA: Loss Change Allocation for Neural Network Training Opening the Black Box of Deep Neural Networks via Information

Reference 25

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Observation 5a924dc4-7a14-4e20-8af9-fcc461144ea0 · outbound

This paper cites Very Deep Convolutional Networks for Large-Scale Image Recognition.

LCA: Loss Change Allocation for Neural Network Training Very Deep Convolutional Networks for Large-Scale Image Recognition

Reference 26

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Observation 18337fff-039f-4213-9008-e29644c9934e · outbound

This paper cites Deep Inside Convolutional Networks: Visualising Image Classification Models and Saliency Maps.

LCA: Loss Change Allocation for Neural Network Training Deep Inside Convolutional Networks: Visualising Image Classification Models and Saliency Maps

Reference 27

Resolution: unresolved; no resolver link, observed 2026-08-14T05:23:19.707362Z

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Observation 6f9bf117-3aa3-4ede-82e0-a23455d66736 · outbound

This paper cites No bad local minima: Data independent training error guarantees for multilayer neural networks.

LCA: Loss Change Allocation for Neural Network Training No bad local minima: Data independent training error guarantees for multilayer neural networks

Reference 28

Resolution: unresolved; no resolver link, observed 2026-08-14T05:23:19.713359Z

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Observation ea030a86-f11e-4fcf-9898-eabe6d4c87ca · outbound

This paper cites Striving for Simplicity: The All Convolutional Net.

LCA: Loss Change Allocation for Neural Network Training Striving for Simplicity: The All Convolutional Net

Reference 29

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Observation 97b7e272-7b30-477d-a634-f02bec09fc38 · outbound

This paper cites On the importance of initialization and momentum in deep learning.

LCA: Loss Change Allocation for Neural Network Training On the importance of initialization and momentum in deep learning

Reference 30

Resolution: verified fuzzy; raw_fallback, observed 2026-08-14T05:23:20.142295Z

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Observation 0cafcc36-e7c0-4a7f-aba1-01e0662fc196 · outbound

This paper cites Simpson’s rule.

LCA: Loss Change Allocation for Neural Network Training Simpson’s rule

Reference 31

Resolution: verified fuzzy; raw_fallback, observed 2026-08-14T05:23:20.124207Z

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Observation 79642e13-dda4-4558-8a77-355700df41aa · outbound

This paper cites A walk with sgd.

LCA: Loss Change Allocation for Neural Network Training A walk with sgd

Reference 32

Resolution: verified fuzzy; raw_fallback, observed 2026-08-14T05:23:20.106421Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

Observation 307786b3-8e0a-43a9-a579-6a58b20c30da · outbound

This paper cites Yosinski, J.

LCA: Loss Change Allocation for Neural Network Training Yosinski, J

Reference 33

Resolution: verified fuzzy; raw_fallback, observed 2026-08-14T05:23:20.089308Z

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No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

Observation 3c229f5e-fb3e-4449-a5f8-b4d74fed3e8d · outbound

This paper cites Continual Learning Through Synaptic Intelligence.

LCA: Loss Change Allocation for Neural Network Training Continual Learning Through Synaptic Intelligence

Reference 34

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Observation c0c89bd2-f78f-418a-9db1-9bac5f8939c4 · outbound

This paper cites Are All Layers Created Equal?.

LCA: Loss Change Allocation for Neural Network Training Are All Layers Created Equal?

Reference 35

Resolution: unresolved; no resolver link, observed 2026-08-14T05:23:19.746568Z

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Observation 5c223eb3-c64b-4c70-94c8-86ab54a98166 · outbound

This paper cites Deconstructing Lottery Tickets: Zeros, Signs, and the Supermask.

LCA: Loss Change Allocation for Neural Network Training Deconstructing Lottery Tickets: Zeros, Signs, and the Supermask

Reference 36

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Pith citing papers

Observation a6f5d4d0-6046-4dd4-bf8f-ac58c800683c · inbound

Partitioned integrators for thermodynamic parameterization of neural networks cites this paper.

Partitioned integrators for thermodynamic parameterization of neural networks LCA: Loss Change Allocation for Neural Network Training

Reference 27

Resolution: verified exact; local_arxiv, observed 2026-08-14T10:10:10.041065Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.