Paper Citation Record · LEDGER

MID-L: Matrix-Interpolated Dropout Layer with Layer-wise Neuron Selection

As of 19 August 2026, this Paper Citation Record lists 38 of 38 outbound references and 1 inbound Pith citation observation for arXiv:2505.11416.

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

pith.paper-citation-record.v1
2505.11416 v1

Coverage vector

measured 38 of 38 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-15T20:56:58.002333Z

measured 39 of 39 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-19T06:32:44.657259+00:00

measured 1 of 1 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-06T17:05:52.524197Z

measured 0 of 1 external citation measurements

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

Source: pith, observed 2026-08-06T17:05:52.770925Z

Reference resolution

38 of 38 outbound references displayed

verified exact2
verified fuzzy17
unresolved19
parse uncertain0
malformed identifier0
metadata mismatch0

External citation measurements

No source-named external measurement is stored.

Outbound references

Observation e6b024bc-f6d0-44dc-b0ed-50f544737f72 · outbound

This paper cites An ETF view of Dropout regularization.

MID-L: Matrix-Interpolated Dropout Layer with Layer-wise Neuron Selection An ETF view of Dropout regularization

Reference 1

Resolution: verified exact; local_arxiv, observed 2026-08-15T20:56:58.191208Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation 0f3b015f-f4e8-4a44-b30c-fe83109154b2 · outbound

This paper cites Adult [dataset].

MID-L: Matrix-Interpolated Dropout Layer with Layer-wise Neuron Selection Adult [dataset]

Reference 2

Resolution: unresolved; no resolver link, observed 2026-08-15T20:56:57.793753Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation e27f21b5-c215-48e7-9457-ab82b2409fb7 · outbound

This paper cites Can LLMs Improve Multimodal Fact-Checking by Asking Relevant Questions?.

MID-L: Matrix-Interpolated Dropout Layer with Layer-wise Neuron Selection Can LLMs Improve Multimodal Fact-Checking by Asking Relevant Questions?

Reference 3

Resolution: unresolved; no resolver link, observed 2026-08-15T20:56:57.800865Z

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Unavailable: canonical work link unavailable.

Observation 37948ebe-f6f8-4d9e-bd6e-93567e037df8 · outbound

This paper cites Estimating or Propagating Gradients Through Stochastic Neurons for Conditional Computation.

MID-L: Matrix-Interpolated Dropout Layer with Layer-wise Neuron Selection Estimating or Propagating Gradients Through Stochastic Neurons for Conditional Computation

Reference 4

Resolution: unresolved; no resolver link, observed 2026-08-15T20:56:57.807566Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation aeda6463-cbeb-484d-b52d-f46ca934576b · outbound

This paper cites Dynamic relu.

MID-L: Matrix-Interpolated Dropout Layer with Layer-wise Neuron Selection Dynamic relu

Reference 5

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T20:56:58.489675Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation 4d4adfb8-d660-4606-a263-878fd3a4b43e · outbound

This paper cites Generating Long Sequences with Sparse Transformers.

MID-L: Matrix-Interpolated Dropout Layer with Layer-wise Neuron Selection Generating Long Sequences with Sparse Transformers

Reference 6

Resolution: unresolved; no resolver link, observed 2026-08-15T20:56:57.819694Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 18555b06-f599-4876-ae95-1e1c7e5c654f · outbound

This paper cites Understanding dropout: training multi-layer perceptrons with auxiliary inde- pendent stochastic neurons.

MID-L: Matrix-Interpolated Dropout Layer with Layer-wise Neuron Selection Understanding dropout: training multi-layer perceptrons with auxiliary inde- pendent stochastic neurons

Reference 7

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T20:56:58.475847Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation 4eb8e218-95e2-4c5d-9297-f8e91956c4b9 · outbound

This paper cites Switch transformers: Scaling to trillion parameter models with simple and efficient sparsity.

MID-L: Matrix-Interpolated Dropout Layer with Layer-wise Neuron Selection Switch transformers: Scaling to trillion parameter models with simple and efficient sparsity

Reference 8

Resolution: unresolved; no resolver link, observed 2026-08-15T20:56:57.834330Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 9f519882-3a84-4e60-b41a-72323755cf21 · outbound

This paper cites Pruning filters for efficient convnets.

MID-L: Matrix-Interpolated Dropout Layer with Layer-wise Neuron Selection Pruning filters for efficient convnets

Reference 9

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T20:56:58.454224Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation 6e7f7f9f-698d-43eb-83a3-b988157af59d · outbound

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

MID-L: Matrix-Interpolated Dropout Layer with Layer-wise Neuron Selection The Lottery Ticket Hypothesis: Finding Sparse, Trainable Neural Networks

Reference 10

Resolution: unresolved; no resolver link, observed 2026-08-15T20:56:57.848274Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 409b3838-78cf-4d32-8086-32d524c95c68 · outbound

This paper cites Dropout as a bayesian approximation: Representing model uncertainty in deep learning.

MID-L: Matrix-Interpolated Dropout Layer with Layer-wise Neuron Selection Dropout as a bayesian approximation: Representing model uncertainty in deep learning

Reference 11

Resolution: unresolved; no resolver link, observed 2026-08-15T20:56:57.854230Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 61e1178f-86ba-47cc-bea4-94565a3cf8a1 · outbound

This paper cites Concrete dropout.

MID-L: Matrix-Interpolated Dropout Layer with Layer-wise Neuron Selection Concrete dropout

Reference 12

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T20:56:58.432899Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation 43cdd46f-d5fe-4ed4-87cf-71a61c628059 · outbound

This paper cites Sliced mutual information: A scalable measure of statistical dependence.

MID-L: Matrix-Interpolated Dropout Layer with Layer-wise Neuron Selection Sliced mutual information: A scalable measure of statistical dependence

Reference 13

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T20:56:58.419323Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation 128c1b68-013e-4579-b858-0a8c8fd0e8e1 · outbound

This paper cites Adaptive Computation Time for Recurrent Neural Networks.

MID-L: Matrix-Interpolated Dropout Layer with Layer-wise Neuron Selection Adaptive Computation Time for Recurrent Neural Networks

Reference 14

Resolution: unresolved; no resolver link, observed 2026-08-15T20:56:57.872861Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 89e4e78e-35e6-42f8-a580-072c0bb5607e · outbound

This paper cites Deep Compression: Compressing Deep Neural Networks with Pruning, Trained Quantization and Huffman Coding.

MID-L: Matrix-Interpolated Dropout Layer with Layer-wise Neuron Selection Deep Compression: Compressing Deep Neural Networks with Pruning, Trained Quantization and Huffman Coding

Reference 15

Resolution: unresolved; no resolver link, observed 2026-08-15T20:56:57.880503Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation b7c290c1-d69f-48a2-8dec-17de44efdb7d · outbound

This paper cites Channel pruning for accelerating very deep neural networks.

MID-L: Matrix-Interpolated Dropout Layer with Layer-wise Neuron Selection Channel pruning for accelerating very deep neural networks

Reference 16

Resolution: unresolved; no resolver link, observed 2026-08-15T20:56:57.886954Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation c1af801a-1d81-4ab6-98d5-6d3ed7db06b7 · outbound

This paper cites Benchmarking neural network robustness to common corruptions and perturbations.

MID-L: Matrix-Interpolated Dropout Layer with Layer-wise Neuron Selection Benchmarking neural network robustness to common corruptions and perturbations

Reference 17

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T20:56:58.397630Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation 1e5b1b45-4ef2-4523-8ee2-cff8b7635d0d · outbound

This paper cites Deep neural networks for acoustic modeling in speech recognition: The shared views of four research groups.

MID-L: Matrix-Interpolated Dropout Layer with Layer-wise Neuron Selection Deep neural networks for acoustic modeling in speech recognition: The shared views of four research groups

Reference 18

Resolution: unresolved; no resolver link, observed 2026-08-15T20:56:57.901600Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation e4db06f7-b1c4-47d1-b758-671871e7442f · outbound

This paper cites Lora: Low-rank adaptation of large language models.

MID-L: Matrix-Interpolated Dropout Layer with Layer-wise Neuron Selection Lora: Low-rank adaptation of large language models

Reference 19

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T20:56:58.376237Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation b6757944-f637-4c0b-9a13-7fd80f51c338 · outbound

This paper cites Multi-Sample Dropout for Accelerated Training and Better Generalization.

MID-L: Matrix-Interpolated Dropout Layer with Layer-wise Neuron Selection Multi-Sample Dropout for Accelerated Training and Better Generalization

Reference 20

Resolution: unresolved; no resolver link, observed 2026-08-15T20:56:57.911180Z

Source-reported events for the cited work

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Observation 0428315a-1929-4072-91d7-0f71c36aafaa · outbound

This paper cites Information Geometry of Dropout Training.

MID-L: Matrix-Interpolated Dropout Layer with Layer-wise Neuron Selection Information Geometry of Dropout Training

Reference 21

Resolution: verified exact; local_arxiv, observed 2026-08-15T20:56:58.078708Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation 5a34317d-3cb4-479b-98d6-9676bdebb660 · outbound

This paper cites Training strategies for modality dropout resilient multi-modal target speaker extraction.

MID-L: Matrix-Interpolated Dropout Layer with Layer-wise Neuron Selection Training strategies for modality dropout resilient multi-modal target speaker extraction

Reference 22

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T20:56:58.363596Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation ad01e523-4078-471e-a6b8-e970f81f8c82 · outbound

This paper cites Cifar-10 (canadian institute for advanced research).

MID-L: Matrix-Interpolated Dropout Layer with Layer-wise Neuron Selection Cifar-10 (canadian institute for advanced research)

Reference 23

Resolution: unresolved; no resolver link, observed 2026-08-15T20:56:57.933946Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 5fd73e31-187e-4337-a1bb-d7408e847737 · outbound

This paper cites Cifar-100 (canadian institute for advanced research).

MID-L: Matrix-Interpolated Dropout Layer with Layer-wise Neuron Selection Cifar-100 (canadian institute for advanced research)

Reference 24

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T20:56:58.342806Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation 8938da8a-9b45-442c-9dd3-bd3f59f784b7 · outbound

This paper cites Imagenet classification with deep convolutional neural networks.

MID-L: Matrix-Interpolated Dropout Layer with Layer-wise Neuron Selection Imagenet classification with deep convolutional neural networks

Reference 25

Resolution: unresolved; no resolver link, observed 2026-08-15T20:56:57.948462Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 8a85f098-75ab-40ad-8062-99ffddb74836 · outbound

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

MID-L: Matrix-Interpolated Dropout Layer with Layer-wise Neuron Selection Gradient-based learning applied to document recognition

Reference 26

Resolution: unresolved; no resolver link, observed 2026-08-15T20:56:57.955464Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation e9ebaf4b-c66d-4915-979e-c14aabedd03b · outbound

This paper cites Modout: Learning to fuse modalities via stochastic regularization.

MID-L: Matrix-Interpolated Dropout Layer with Layer-wise Neuron Selection Modout: Learning to fuse modalities via stochastic regularization

Reference 27

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T20:56:58.313240Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation e56b28f0-7214-426f-b20e-ff454fc49d40 · outbound

This paper cites Learning word vectors for sentiment analysis.

MID-L: Matrix-Interpolated Dropout Layer with Layer-wise Neuron Selection Learning word vectors for sentiment analysis

Reference 28

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T20:56:58.300262Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation 9709fe07-9b4f-4eac-a222-ccfd37721671 · outbound

This paper cites Reading digits in natural images with unsupervised feature learning.

MID-L: Matrix-Interpolated Dropout Layer with Layer-wise Neuron Selection Reading digits in natural images with unsupervised feature learning

Reference 29

Resolution: unresolved; no resolver link, observed 2026-08-15T20:56:57.969774Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 6f780f06-6b65-4de7-be6f-03edee8ee9ec · outbound

This paper cites Multiple hypothesis dropout: estimating the parameters of multi-modal output distributions.

MID-L: Matrix-Interpolated Dropout Layer with Layer-wise Neuron Selection Multiple hypothesis dropout: estimating the parameters of multi-modal output distributions

Reference 30

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T20:56:58.278806Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation 4098170e-9bdf-4dae-ab2e-1f5441b16fd0 · outbound

This paper cites Dropgnn: Random dropouts increase the expressiveness of graph neural networks.

MID-L: Matrix-Interpolated Dropout Layer with Layer-wise Neuron Selection Dropgnn: Random dropouts increase the expressiveness of graph neural networks

Reference 31

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T20:56:58.266170Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation 23bb6159-ee8b-4d38-9822-c29404e2f887 · outbound

This paper cites Revisiting dropout regularization for cross-modality person re-identification.

MID-L: Matrix-Interpolated Dropout Layer with Layer-wise Neuron Selection Revisiting dropout regularization for cross-modality person re-identification

Reference 32

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T20:56:58.253224Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation 8916fc09-a4aa-4d3a-9220-d7928121701c · outbound

This paper cites A Multimodal Physics-Informed Neural Network Approach for Mean Radiant Temperature Modeling.

MID-L: Matrix-Interpolated Dropout Layer with Layer-wise Neuron Selection A Multimodal Physics-Informed Neural Network Approach for Mean Radiant Temperature Modeling

Reference 33

Resolution: unresolved; no resolver link, observed 2026-08-15T20:56:57.984398Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation a7ad95ac-e3ff-461f-8286-01a31a3e4c2f · outbound

This paper cites A semi-supervised fake news detection using sentiment encoding and lstm with self-attention.

MID-L: Matrix-Interpolated Dropout Layer with Layer-wise Neuron Selection A semi-supervised fake news detection using sentiment encoding and lstm with self-attention

Reference 34

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T20:56:58.239899Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation 2117533d-5392-41fb-bc39-4dc25fa9b2ea · outbound

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

MID-L: Matrix-Interpolated Dropout Layer with Layer-wise Neuron Selection Outrageously Large Neural Networks: The Sparsely-Gated Mixture-of-Experts Layer

Reference 35

Resolution: unresolved; no resolver link, observed 2026-08-15T20:56:57.991434Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 03566c0b-bfa4-4b9f-9d1b-cc7c7c3df2a2 · outbound

This paper cites Dropout: a simple way to prevent neural networks from overfitting.

MID-L: Matrix-Interpolated Dropout Layer with Layer-wise Neuron Selection Dropout: a simple way to prevent neural networks from overfitting

Reference 36

Resolution: unresolved; no resolver link, observed 2026-08-15T20:56:57.995130Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 10ec3779-9a07-45df-bf04-e44763e05283 · outbound

This paper cites Attention is all you need.

MID-L: Matrix-Interpolated Dropout Layer with Layer-wise Neuron Selection Attention is all you need

Reference 37

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T20:56:58.217628Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation 8e474142-bef4-492d-98ff-fa58176e9ee6 · outbound

This paper cites Table 7: Calibration metrics on CIFAR-10.

MID-L: Matrix-Interpolated Dropout Layer with Layer-wise Neuron Selection Table 7: Calibration metrics on CIFAR-10

Reference 38

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T20:56:58.204814Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Pith citing papers

Observation 9c396ffe-aa18-456e-9fc9-30ac4daeefcd · inbound

MNIST-Gen: A Modular MNIST-Style Dataset Generation Using Hierarchical Semantics, Reinforcement Learning, and Category Theory cites this paper.

MNIST-Gen: A Modular MNIST-Style Dataset Generation Using Hierarchical Semantics, Reinforcement Learning, and Category Theory MID-L: Matrix-Interpolated Dropout Layer with Layer-wise Neuron Selection

Reference 6

Resolution: verified exact; local_arxiv, observed 2026-08-06T17:05:52.774100Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.