Impact of Bottleneck Layers and Skip Connections on the Generalization of Linear Denoising Autoencoders | Paper Citation Record

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This paper cites The loss landscape of deep linear neural networks: a second-order analysis.

Impact of Bottleneck Layers and Skip Connections on the Generalization of Linear Denoising Autoencoders The loss landscape of deep linear neural networks: a second-order analysis

Reference 1

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Observation 32175d06-e524-4dba-9f1b-79242ec33cbe · outbound

This paper cites A Random Matrix Perspective on Mixtures of Nonlinearities in High Dimensions.

Impact of Bottleneck Layers and Skip Connections on the Generalization of Linear Denoising Autoencoders A Random Matrix Perspective on Mixtures of Nonlinearities in High Dimensions

Reference 2

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Observation 07e403a2-d3d2-4011-af8f-276fcab2fb81 · outbound

This paper cites Complex analysis.

Impact of Bottleneck Layers and Skip Connections on the Generalization of Linear Denoising Autoencoders Complex analysis

Reference 3

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Observation 6f881f1a-2c89-4b95-a7ae-fcb6a5417466 · outbound

This paper cites Generalization of two-layer neural networks: An asymptotic viewpoint.

Impact of Bottleneck Layers and Skip Connections on the Generalization of Linear Denoising Autoencoders Generalization of two-layer neural networks: An asymptotic viewpoint

Reference 4

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Observation 4fa62057-983b-48f2-8b6d-07c62e23d644 · outbound

This paper cites High-dimensional analysis of double descent for linear regression with random projections.

Impact of Bottleneck Layers and Skip Connections on the Generalization of Linear Denoising Autoencoders High-dimensional analysis of double descent for linear regression with random projections

Reference 5

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Observation 74a3a999-1ff5-40fa-8df8-93bc29121c0e · outbound

This paper cites Spectral analysis of large dimensional random matrices.

Impact of Bottleneck Layers and Skip Connections on the Generalization of Linear Denoising Autoencoders Spectral analysis of large dimensional random matrices

Reference 6

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Observation dd6eccf3-4b08-4fca-8598-34625f26e655 · outbound

This paper cites Eigenvalues of Large Sample Covariance Matrices of Spiked Population Models.

Impact of Bottleneck Layers and Skip Connections on the Generalization of Linear Denoising Autoencoders Eigenvalues of Large Sample Covariance Matrices of Spiked Population Models

Reference 7

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Observation b93c874f-1168-44fa-9321-a2326173fd42 · outbound

This paper cites Neural networks and principal component analysis: Learning from examples without local minima.

Impact of Bottleneck Layers and Skip Connections on the Generalization of Linear Denoising Autoencoders Neural networks and principal component analysis: Learning from examples without local minima

Reference 8

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Observation bb3564a2-b4aa-49f2-aecd-d9a8eec0287e · outbound

This paper cites Benign overfitting in linear regression.

Impact of Bottleneck Layers and Skip Connections on the Generalization of Linear Denoising Autoencoders Benign overfitting in linear regression

Reference 9

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Observation d5c6259c-316d-45a3-ba9f-bfd51249ca00 · outbound

This paper cites Reconciling modern machine-learning practice and the classical bias–variance trade-off.

Impact of Bottleneck Layers and Skip Connections on the Generalization of Linear Denoising Autoencoders Reconciling modern machine-learning practice and the classical bias–variance trade-off

Reference 10

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Observation 75d10350-b7d2-4618-9eb3-12c38d07242f · outbound

This paper cites On the Exact Covariance of Products of Random Variables.

Impact of Bottleneck Layers and Skip Connections on the Generalization of Linear Denoising Autoencoders On the Exact Covariance of Products of Random Variables

Reference 11

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Observation c23fe395-c66e-4399-9d77-4c6b25c4b8ac · outbound

This paper cites Random Matrix Methods for Machine Learning.

Impact of Bottleneck Layers and Skip Connections on the Generalization of Linear Denoising Autoencoders Random Matrix Methods for Machine Learning

Reference 12

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Observation ae9d378f-c170-46c7-8eb0-a7891cf841ca · outbound

This paper cites High-dimensional asymptotics of denoising autoencoders.

Impact of Bottleneck Layers and Skip Connections on the Generalization of Linear Denoising Autoencoders High-dimensional asymptotics of denoising autoencoders

Reference 13

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T12:28:00.471164Z

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Observation d8dacd5a-9afc-4edd-bbba-dfc58e2d2d00 · outbound

This paper cites A U-turn on Double Descent: Rethinking Parameter Counting in Statistical Learning.

Impact of Bottleneck Layers and Skip Connections on the Generalization of Linear Denoising Autoencoders A U-turn on Double Descent: Rethinking Parameter Counting in Statistical Learning

Reference 14

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Observation dabfaf67-b9b9-4601-975c-fd2674324083 · outbound

This paper cites Procedures for Reduced-Rank Regression.

Impact of Bottleneck Layers and Skip Connections on the Generalization of Linear Denoising Autoencoders Procedures for Reduced-Rank Regression

Reference 15

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T12:28:00.092546Z

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Observation a374868f-0da5-4e64-9635-2e1954727d5c · outbound

This paper cites On the empirical distribution of eigenvalues of large dimensional information-plus-noise-type matrices.

Impact of Bottleneck Layers and Skip Connections on the Generalization of Linear Denoising Autoencoders On the empirical distribution of eigenvalues of large dimensional information-plus-noise-type matrices

Reference 16

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T12:27:59.882105Z

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Observation 5d16ee29-df90-4365-985b-f41bbb2f5b99 · outbound

This paper cites Probability: theory and examples.

Impact of Bottleneck Layers and Skip Connections on the Generalization of Linear Denoising Autoencoders Probability: theory and examples

Reference 17

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Observation 7a97a089-b584-4c47-882d-bad1b4d8c50a · outbound

This paper cites The approximation of one matrix by another of lower rank.

Impact of Bottleneck Layers and Skip Connections on the Generalization of Linear Denoising Autoencoders The approximation of one matrix by another of lower rank

Reference 18

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Observation d22f530d-53df-4985-a004-5d7ad2a2996a · outbound

This paper cites The rank of a random matrix.

Impact of Bottleneck Layers and Skip Connections on the Generalization of Linear Denoising Autoencoders The rank of a random matrix

Reference 19

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Observation 2e7b1b9a-31f0-451d-a914-c8e67e95c0f1 · outbound

This paper cites No Double Descent in Prin- cipal Component Regression: A High-Dimensional Analysis.

Impact of Bottleneck Layers and Skip Connections on the Generalization of Linear Denoising Autoencoders No Double Descent in Prin- cipal Component Regression: A High-Dimensional Analysis

Reference 20

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T12:27:59.335513Z

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Observation 42333fd8-4a77-42ee-9f2c-6141b85aff37 · outbound

This paper cites Asymptotic errors for convex penalized linear regression beyond Gaussian matrices.

Impact of Bottleneck Layers and Skip Connections on the Generalization of Linear Denoising Autoencoders Asymptotic errors for convex penalized linear regression beyond Gaussian matrices

Reference 21

Resolution: verified exact; local_arxiv, observed 2026-08-07T12:27:50.629765Z

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Observation 6316d234-b4ad-4374-9509-3e79ac379202 · outbound

This paper cites Surprises in High-Dimensional Ridgeless Least Squares Interpolation.

Impact of Bottleneck Layers and Skip Connections on the Generalization of Linear Denoising Autoencoders Surprises in High-Dimensional Ridgeless Least Squares Interpolation

Reference 22

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T12:27:59.139335Z

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Observation 2bd4384f-9de7-44a3-bacc-8d432eac5a6d · outbound

This paper cites Deep Residual Learning for Image Recognition.

Impact of Bottleneck Layers and Skip Connections on the Generalization of Linear Denoising Autoencoders Deep Residual Learning for Image Recognition

Reference 23

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Observation 003c0e7f-cf6b-4e47-91c1-454efebccd66 · outbound

This paper cites Denoising Diffusion Probabilistic Models.

Impact of Bottleneck Layers and Skip Connections on the Generalization of Linear Denoising Autoencoders Denoising Diffusion Probabilistic Models

Reference 24

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Observation 84173857-16ca-417a-80e5-00f029483f00 · outbound

This paper cites No Double Descent in Self-Supervised Learning.

Impact of Bottleneck Layers and Skip Connections on the Generalization of Linear Denoising Autoencoders No Double Descent in Self-Supervised Learning

Reference 25

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T12:27:58.986701Z

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Observation 060068b9-6cd5-4109-907b-f752a9812f1a · outbound

This paper cites On the Distribution of the Largest Eigenvalue in Principal Components Analysis.

Impact of Bottleneck Layers and Skip Connections on the Generalization of Linear Denoising Autoencoders On the Distribution of the Largest Eigenvalue in Principal Components Analysis

Reference 26

Resolution: verified exact; raw_fallback, observed 2026-08-07T12:27:50.354029Z

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Observation 424d10e9-36f8-44fd-9333-7a5275423a3a · outbound

This paper cites Double Descent and Overfitting under Noisy Inputs and Distribution Shift for Linear Denoisers.

Impact of Bottleneck Layers and Skip Connections on the Generalization of Linear Denoising Autoencoders Double Descent and Overfitting under Noisy Inputs and Distribution Shift for Linear Denoisers

Reference 27

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Observation 4179fc1e-5a88-4eb9-ad7a-5ceed14ee473 · outbound

This paper cites Deep Learning without Poor Local Minima.

Impact of Bottleneck Layers and Skip Connections on the Generalization of Linear Denoising Autoencoders Deep Learning without Poor Local Minima

Reference 28

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Observation e4a2ee23-fa2f-42c5-a54a-640c8bf8aa61 · outbound

This paper cites Learning multiple layers of features from tiny images.

Impact of Bottleneck Layers and Skip Connections on the Generalization of Linear Denoising Autoencoders Learning multiple layers of features from tiny images

Reference 29

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T12:27:58.827643Z

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Observation 124a79b6-9d2f-4509-a23f-6538458ab8fa · outbound

This paper cites Does Double Descent Occur in Self-Supervised Learning?.

Impact of Bottleneck Layers and Skip Connections on the Generalization of Linear Denoising Autoencoders Does Double Descent Occur in Self-Supervised Learning?

Reference 30

Resolution: verified exact; local_arxiv, observed 2026-08-07T12:27:50.039042Z

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Observation 6a874912-3319-4b96-9339-7cde935143e8 · outbound

This paper cites Reduced rank ridge regression and its kernel extensions.

Impact of Bottleneck Layers and Skip Connections on the Generalization of Linear Denoising Autoencoders Reduced rank ridge regression and its kernel extensions

Reference 31

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T12:27:58.642037Z

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Observation 90226f3f-0d0d-4e3c-b957-5a81266f1804 · outbound

This paper cites Deep Double Descent: Where Bigger Models and More Data Hurt.

Impact of Bottleneck Layers and Skip Connections on the Generalization of Linear Denoising Autoencoders Deep Double Descent: Where Bigger Models and More Data Hurt

Reference 32

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T12:27:58.417106Z

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Observation e7e23329-b414-4882-a99a-c80dcb7858dc · outbound

This paper cites Learning dynamics of linear denoising autoencoders.

Impact of Bottleneck Layers and Skip Connections on the Generalization of Linear Denoising Autoencoders Learning dynamics of linear denoising autoencoders

Reference 33

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T12:27:58.220630Z

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Observation a992d39d-bc81-48ef-8174-2f5eef014dbc · outbound

This paper cites Multiple Descents in Unsupervised Learning: The Role of Noise, Domain Shift and Anomalies.

Impact of Bottleneck Layers and Skip Connections on the Generalization of Linear Denoising Autoencoders Multiple Descents in Unsupervised Learning: The Role of Noise, Domain Shift and Anomalies

Reference 34

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Observation e790c74d-f8c0-432f-93e8-514647dca8db · outbound

This paper cites U-Net: Convolutional Networks for Biomedical Image Segmentation.

Impact of Bottleneck Layers and Skip Connections on the Generalization of Linear Denoising Autoencoders U-Net: Convolutional Networks for Biomedical Image Segmentation

Reference 35

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Observation 369c895a-e7a8-4d79-864c-cf8e37779a1a · outbound

This paper cites Smallest singular value of a random rectangular matrix.

Impact of Bottleneck Layers and Skip Connections on the Generalization of Linear Denoising Autoencoders Smallest singular value of a random rectangular matrix

Reference 36

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T12:27:58.016057Z

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Observation 778fd46c-c8cd-4dd7-ab69-80c8b70045fd · outbound

This paper cites On the Empirical Distribution of Eigenvalues of a Class of Large Dimensional Random Matrices.

Impact of Bottleneck Layers and Skip Connections on the Generalization of Linear Denoising Autoencoders On the Empirical Distribution of Eigenvalues of a Class of Large Dimensional Random Matrices

Reference 37

Resolution: unresolved; no resolver link, observed 2026-08-07T12:27:46.074059Z

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Observation 5500fdf2-914d-410a-87f3-45672d00bcbf · outbound

This paper cites Training Data Size Induced Double Descent For Denoising Neural Networks and the Role of Training Noise Level.

Impact of Bottleneck Layers and Skip Connections on the Generalization of Linear Denoising Autoencoders Training Data Size Induced Double Descent For Denoising Neural Networks and the Role of Training Noise Level

Reference 38

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T12:27:57.826532Z

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Observation d05cf9f2-ec45-4e2d-815d-6e0e1ba5ee14 · outbound

This paper cites Topics in random matrix theory.

Impact of Bottleneck Layers and Skip Connections on the Generalization of Linear Denoising Autoencoders Topics in random matrix theory

Reference 39

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Observation b33f2293-636f-4986-b77b-8d4b7b26394f · outbound

This paper cites Dimensionality Reduction, Regulariza- tion, and Generalization in Overparameterized Regressions.

Impact of Bottleneck Layers and Skip Connections on the Generalization of Linear Denoising Autoencoders Dimensionality Reduction, Regulariza- tion, and Generalization in Overparameterized Regressions

Reference 40

Resolution: unresolved; no resolver link, observed 2026-08-07T12:27:46.354318Z

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Observation ff6eb2e7-00a3-44c5-80e3-5e9112f224d0 · outbound

This paper cites Pure and Spurious Critical Points: a Geometric Study of Linear Networks.

Impact of Bottleneck Layers and Skip Connections on the Generalization of Linear Denoising Autoencoders Pure and Spurious Critical Points: a Geometric Study of Linear Networks

Reference 41

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Observation 08c7c6ad-2850-42fa-802e-e2711cdf6d22 · outbound

This paper cites Why are Big Data Matrices Approximately Low Rank?.

Impact of Bottleneck Layers and Skip Connections on the Generalization of Linear Denoising Autoencoders Why are Big Data Matrices Approximately Low Rank?

Reference 42

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T12:27:57.386944Z

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Observation f4e9e3a2-36a9-4236-af53-fbc1f4e07682 · outbound

This paper cites High-dimensional probability: An introduction with applications in data science.

Impact of Bottleneck Layers and Skip Connections on the Generalization of Linear Denoising Autoencoders High-dimensional probability: An introduction with applications in data science

Reference 43

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T12:27:57.209168Z

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Observation 47a05f12-5448-4cc0-acb6-530197062b70 · outbound

This paper cites Stacked denoising autoencoders: Learning useful representations in a deep network with a local denoising criterion.

Impact of Bottleneck Layers and Skip Connections on the Generalization of Linear Denoising Autoencoders Stacked denoising autoencoders: Learning useful representations in a deep network with a local denoising criterion

Reference 44

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T12:27:57.027388Z

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 672e4306-bc06-4489-81c8-86a3b5dee2f2 · outbound

This paper cites The weighted Moore–Penrose inverse of modified matrices.

Impact of Bottleneck Layers and Skip Connections on the Generalization of Linear Denoising Autoencoders The weighted Moore–Penrose inverse of modified matrices

Reference 45

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Observation 44a9cc3a-2efc-4be3-b4b5-f0fe195de5f7 · outbound

This paper cites Optimal exact least squares rank minimization.

Impact of Bottleneck Layers and Skip Connections on the Generalization of Linear Denoising Autoencoders Optimal exact least squares rank minimization

Reference 46

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T12:27:56.883247Z

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Observation e461eb08-d054-4641-81e8-73a2d862a280 · outbound

This paper cites Critical Points of Neural Networks: Analytical Forms and Landscape Properties.

Impact of Bottleneck Layers and Skip Connections on the Generalization of Linear Denoising Autoencoders Critical Points of Neural Networks: Analytical Forms and Landscape Properties

Reference 47

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Observation ade236b3-d254-49fe-bf79-c0023557612a · outbound

This paper cites The above eq.

Impact of Bottleneck Layers and Skip Connections on the Generalization of Linear Denoising Autoencoders The above eq

Reference 51

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T12:27:56.661887Z

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

Observation 6e028243-7526-480b-a3d3-6cf72973fdd6 · outbound

This paper cites For a real probability measureµ with support supp(µ), the Stieltjes Transform of µ is defined as mµ(α) = Z 1 x − α dµ(x), α ∈ C\supp(µ).

Impact of Bottleneck Layers and Skip Connections on the Generalization of Linear Denoising Autoencoders For a real probability measureµ with support supp(µ), the Stieltjes Transform of µ is defined as mµ(α) = Z 1 x − α dµ(x), α ∈ C\supp(µ)

Reference 52

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T12:27:56.489778Z

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

Observation 9b370979-922b-4fa8-a6cd-546f29ed25f7 · outbound

This paper cites Let A ∈ Rp×p, and x, y ∈ Rp.

Impact of Bottleneck Layers and Skip Connections on the Generalization of Linear Denoising Autoencoders Let A ∈ Rp×p, and x, y ∈ Rp

Reference 53

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T12:27:56.282275Z

Source-reported events for the cited work

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Observation e5120d7e-6d2d-437d-8e20-d718ed3a3fa5 · outbound

This paper cites For α ∈ C\R+, let ˜Q(α) = ˜A ˜AT − αId −1 = NP i=1 ˜ai˜aT i − αId −1 , where ˜A ∈ Rd×N is a i.i.d real gaussian random matrix, whose entries are sampled from N (0, 1).

Impact of Bottleneck Layers and Skip Connections on the Generalization of Linear Denoising Autoencoders For α ∈ C\R+, let ˜Q(α) = ˜A ˜AT − αId −1 = NP i=1 ˜ai˜aT i − αId −1 , where ˜A ∈ Rd×N is a i.i.d real gaussian random matrix, whose entries are sampled from N (0, 1)

Reference 54

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T12:27:56.113121Z

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 f8a27999-54ab-4a92-97f3-f00dff2cbbd4 · outbound

This paper cites For symmetric and positive semi-definite B, M ∈ Rd×d, and α ∈ C\R+, let M := hP j=1 lixixT i for fixed h, and li ∈ R, xi ∈ Rd.

Impact of Bottleneck Layers and Skip Connections on the Generalization of Linear Denoising Autoencoders For symmetric and positive semi-definite B, M ∈ Rd×d, and α ∈ C\R+, let M := hP j=1 lixixT i for fixed h, and li ∈ R, xi ∈ Rd

Reference 55

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T12:27:55.927324Z

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 af255f0f-70b2-42e2-ad6f-e74040c846a9 · outbound

This paper cites For A ∈ Rp×q, B ∈ Rq×p, and λ ∈ R\{0}.

Impact of Bottleneck Layers and Skip Connections on the Generalization of Linear Denoising Autoencoders For A ∈ Rp×q, B ∈ Rq×p, and λ ∈ R\{0}

Reference 56

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T12:27:55.746290Z

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 d91b332f-155f-4538-a66a-7cb25b2c076e · outbound

This paper cites For A ∈ Rp×p, U ∈ Rp×q, V ∈ Rq×p, we have that (A + UVT )−1 = A−1 − A−1U(Iq + VT A−1U)−1VT A−1.

Impact of Bottleneck Layers and Skip Connections on the Generalization of Linear Denoising Autoencoders For A ∈ Rp×p, U ∈ Rp×q, V ∈ Rq×p, we have that (A + UVT )−1 = A−1 − A−1U(Iq + VT A−1U)−1VT A−1

Reference 57

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T12:27:55.529423Z

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 1acf796c-d1a0-4e15-8c9c-a166d3b130b9 · outbound

This paper cites Furthermore, B has rB eigenvalues equal to 1 and the rest equal to 0.

Impact of Bottleneck Layers and Skip Connections on the Generalization of Linear Denoising Autoencoders Furthermore, B has rB eigenvalues equal to 1 and the rest equal to 0

Reference 58

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T12:27:55.332808Z

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 c2419cf3-f5b0-4cad-afcb-595230adeb5c · outbound

This paper cites an unresolved cited work.

Impact of Bottleneck Layers and Skip Connections on the Generalization of Linear Denoising Autoencoders Unresolved cited work

Reference 59

Resolution: unresolved; raw_fallback, observed 2026-08-07T12:27:55.128152Z

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 b8446f14-2f57-4e04-9caf-721b2f336f60 · outbound

This paper cites an unresolved cited work.

Impact of Bottleneck Layers and Skip Connections on the Generalization of Linear Denoising Autoencoders Unresolved cited work

Reference 60

Resolution: unresolved; raw_fallback, observed 2026-08-07T12:27:54.984576Z

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 403995a4-c0c5-4fd1-955c-ded5ba981de3 · outbound

This paper cites an unresolved cited work.

Impact of Bottleneck Layers and Skip Connections on the Generalization of Linear Denoising Autoencoders Unresolved cited work

Reference 61

Resolution: unresolved; raw_fallback, observed 2026-08-07T12:27:54.634950Z

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 e87cb913-9f44-4547-b442-23f14d0697ef · outbound

This paper cites Furthermore, assume rZ = n.

Impact of Bottleneck Layers and Skip Connections on the Generalization of Linear Denoising Autoencoders Furthermore, assume rZ = n

Reference 62

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T12:27:54.242374Z

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 fc516b88-f6b4-43ba-87b5-c7d17acbf6b9 · outbound

This paper cites an unresolved cited work.

Impact of Bottleneck Layers and Skip Connections on the Generalization of Linear Denoising Autoencoders Unresolved cited work

Reference 63

Resolution: unresolved; raw_fallback, observed 2026-08-07T12:27:53.851726Z

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 7afabcea-52eb-4040-88f3-637340b72d58 · outbound

This paper cites ⇐" direction of the proof follows from a straightforward calculation, and we therefore omit the details. For the.

Impact of Bottleneck Layers and Skip Connections on the Generalization of Linear Denoising Autoencoders ⇐" direction of the proof follows from a straightforward calculation, and we therefore omit the details. For the

Reference 64

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T12:27:53.521222Z

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 3b295444-e6d8-4d3e-81a4-48549653f2bd · outbound

This paper cites Due to the fact that AI a A†(AI a A†)⊤ = UATaU⊤ A = AI a A†, we have that − Tr(AI a A†(Wsc c )⊤) = Tr(AI a A†(AI a A†)⊤) − Tr(AI a A†H⊤(K1)−1H)− Tr(AI a A†H⊤K−⊤ 1 Z(P⊤P)−⊤D ˜U⊤).

Impact of Bottleneck Layers and Skip Connections on the Generalization of Linear Denoising Autoencoders Due to the fact that AI a A†(AI a A†)⊤ = UATaU⊤ A = AI a A†, we have that − Tr(AI a A†(Wsc c )⊤) = Tr(AI a A†(AI a A†)⊤) − Tr(AI a A†H⊤(K1)−1H)− Tr(AI a A†H⊤K−⊤ 1 Z(P⊤P)−⊤D ˜U⊤)

Reference 65

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T12:27:53.188571Z

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 7491b7f2-99ad-4d39-bdf8-32125b190b02 · outbound

This paper cites The second term has also mean 0 due to Lemma E.9, thus only the variance needs to be bounded for this term.

Impact of Bottleneck Layers and Skip Connections on the Generalization of Linear Denoising Autoencoders The second term has also mean 0 due to Lemma E.9, thus only the variance needs to be bounded for this term

Reference 66

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T12:27:52.874067Z

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 a5858bda-f9b3-4408-8e1b-4eb8e0ed98a6 · outbound

This paper cites The first term is, from Lemma E.7, and Lemma 8 of [27], − Tr(AI a A†H⊤K−1 1 H) = − |I a| n η2 trn c Tr((η2 trnD−2 + Ir)−1) + o |I a| n.

Impact of Bottleneck Layers and Skip Connections on the Generalization of Linear Denoising Autoencoders The first term is, from Lemma E.7, and Lemma 8 of [27], − Tr(AI a A†H⊤K−1 1 H) = − |I a| n η2 trn c Tr((η2 trnD−2 + Ir)−1) + o |I a| n

Reference 67

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T12:27:52.629355Z

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 900a3e4e-e53d-44b5-8fcf-ff6f54c37a94 · outbound

This paper cites The first term has zero mean, as shown in Lemma E.9.

Impact of Bottleneck Layers and Skip Connections on the Generalization of Linear Denoising Autoencoders The first term has zero mean, as shown in Lemma E.9

Reference 68

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T12:27:52.350175Z

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 1cbf5831-78a7-437a-935a-21f29466d353 · outbound

This paper cites For the element-wise variance, Lemma E.3 and Lemmas 4, 6, 7, and 8 from [27] imply that it is of order o(1).

Impact of Bottleneck Layers and Skip Connections on the Generalization of Linear Denoising Autoencoders For the element-wise variance, Lemma E.3 and Lemmas 4, 6, 7, and 8 from [27] imply that it is of order o(1)

Reference 69

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T12:27:52.040518Z

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 f2389f91-f460-474b-ba28-6f7c485bb69d · outbound

This paper cites an unresolved cited work.

Impact of Bottleneck Layers and Skip Connections on the Generalization of Linear Denoising Autoencoders Unresolved cited work

Reference 70

Resolution: unresolved; raw_fallback, observed 2026-08-07T12:27:51.747961Z

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 a399c26f-aaff-4f3e-ac5c-dfcfc11cffc8 · outbound

This paper cites Why are Big Data Matrices Approximately Low Rank?.

Impact of Bottleneck Layers and Skip Connections on the Generalization of Linear Denoising Autoencoders Why are Big Data Matrices Approximately Low Rank?

Reference 2018

Resolution: unresolved; no resolver link, observed 2026-08-07T12:27:46.656377Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 971d7c18-f602-4a74-b27b-dd9f142e949b · outbound

This paper cites Deep Double Descent: Where Bigger Models and More Data Hurt.

Impact of Bottleneck Layers and Skip Connections on the Generalization of Linear Denoising Autoencoders Deep Double Descent: Where Bigger Models and More Data Hurt

Reference 2019

Resolution: unresolved; no resolver link, observed 2026-08-07T12:27:45.771869Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation bc7151f9-61b2-451d-9adb-6889dac84207 · outbound

This paper cites Surprises in High-Dimensional Ridgeless Least Squares Interpolation.

Impact of Bottleneck Layers and Skip Connections on the Generalization of Linear Denoising Autoencoders Surprises in High-Dimensional Ridgeless Least Squares Interpolation

Reference 2020

Resolution: unresolved; no resolver link, observed 2026-08-07T12:27:45.045673Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Coverage vector

Reference resolution

Outbound references

Pith citing papers