Deep Weight Factorization: Sparse Learning Through the Lens of Artificial Symmetries | Paper Citation Record

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This paper cites Sgd with large step sizes learns sparse features.

Deep Weight Factorization: Sparse Learning Through the Lens of Artificial Symmetries Sgd with large step sizes learns sparse features

Reference 1

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This paper cites Implicit regularization in deep matrix factorization.

Deep Weight Factorization: Sparse Learning Through the Lens of Artificial Symmetries Implicit regularization in deep matrix factorization

Reference 2

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This paper cites Optimization with sparsity-inducing penalties.

Deep Weight Factorization: Sparse Learning Through the Lens of Artificial Symmetries Optimization with sparsity-inducing penalties

Reference 3

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This paper cites Collapsible linear blocks for super-efficient super resolution.

Deep Weight Factorization: Sparse Learning Through the Lens of Artificial Symmetries Collapsible linear blocks for super-efficient super resolution

Reference 4

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This paper cites What is the state of neural network pruning? Proceedings of machine learning and systems, 2: 0 129--146, 2020.

Deep Weight Factorization: Sparse Learning Through the Lens of Artificial Symmetries What is the state of neural network pruning? Proceedings of machine learning and systems, 2: 0 129--146, 2020

Reference 5

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This paper cites Improving network slimming with nonconvex regularization.

Deep Weight Factorization: Sparse Learning Through the Lens of Artificial Symmetries Improving network slimming with nonconvex regularization

Reference 6

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Observation f6a0c5be-bf67-4aa1-a103-2e9d71d34148 · outbound

This paper cites Stochastic collapse: How gradient noise attracts sgd dynamics towards simpler subnetworks.

Deep Weight Factorization: Sparse Learning Through the Lens of Artificial Symmetries Stochastic collapse: How gradient noise attracts sgd dynamics towards simpler subnetworks

Reference 7

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This paper cites A survey on deep neural network pruning: Taxonomy, comparison, analysis, and recommendations.

Deep Weight Factorization: Sparse Learning Through the Lens of Artificial Symmetries A survey on deep neural network pruning: Taxonomy, comparison, analysis, and recommendations

Reference 8

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This paper cites Representation costs of linear neural networks: Analysis and design.

Deep Weight Factorization: Sparse Learning Through the Lens of Artificial Symmetries Representation costs of linear neural networks: Analysis and design

Reference 9

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Observation c2c7f986-8665-4cd8-807c-666d9a9ba400 · outbound

This paper cites Structured Sparsity Inducing Adaptive Optimizers for Deep Learning.

Deep Weight Factorization: Sparse Learning Through the Lens of Artificial Symmetries Structured Sparsity Inducing Adaptive Optimizers for Deep Learning

Reference 10

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Observation b947bc29-e265-43b0-8de4-3cbc7ad5e3c1 · outbound

This paper cites Shaving weights with occam's razor: Bayesian sparsification for neural networks using the marginal likelihood.

Deep Weight Factorization: Sparse Learning Through the Lens of Artificial Symmetries Shaving weights with occam's razor: Bayesian sparsification for neural networks using the marginal likelihood

Reference 11

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This paper cites Rigging the lottery: Making all tickets winners.

Deep Weight Factorization: Sparse Learning Through the Lens of Artificial Symmetries Rigging the lottery: Making all tickets winners

Reference 12

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Observation 6fb78ac7-c9c8-4e29-8d80-b4ed19874cf1 · outbound

This paper cites Variable selection via nonconcave penalized likelihood and its oracle properties.

Deep Weight Factorization: Sparse Learning Through the Lens of Artificial Symmetries Variable selection via nonconcave penalized likelihood and its oracle properties

Reference 13

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Deep Weight Factorization: Sparse Learning Through the Lens of Artificial Symmetries Unresolved cited work

Reference 14

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This paper cites The lottery ticket hypothesis: Finding sparse, trainable neural networks.

Deep Weight Factorization: Sparse Learning Through the Lens of Artificial Symmetries The lottery ticket hypothesis: Finding sparse, trainable neural networks

Reference 15

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This paper cites Pruning neural networks at initialization: Why are we missing the mark? In International Conference on Learning Representations, 2020.

Deep Weight Factorization: Sparse Learning Through the Lens of Artificial Symmetries Pruning neural networks at initialization: Why are we missing the mark? In International Conference on Learning Representations, 2020

Reference 16

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This paper cites Regularization paths for generalized linear models via coordinate descent.

Deep Weight Factorization: Sparse Learning Through the Lens of Artificial Symmetries Regularization paths for generalized linear models via coordinate descent

Reference 17

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This paper cites The State of Sparsity in Deep Neural Networks.

Deep Weight Factorization: Sparse Learning Through the Lens of Artificial Symmetries The State of Sparsity in Deep Neural Networks

Reference 18

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This paper cites The implicit bias of depth: How incremental learning drives generalization.

Deep Weight Factorization: Sparse Learning Through the Lens of Artificial Symmetries The implicit bias of depth: How incremental learning drives generalization

Reference 19

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This paper cites Hypersparse neural networks: Shifting exploration to exploitation through adaptive regularization.

Deep Weight Factorization: Sparse Learning Through the Lens of Artificial Symmetries Hypersparse neural networks: Shifting exploration to exploitation through adaptive regularization

Reference 20

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This paper cites Understanding the difficulty of training deep feedforward neural networks.

Deep Weight Factorization: Sparse Learning Through the Lens of Artificial Symmetries Understanding the difficulty of training deep feedforward neural networks

Reference 21

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This paper cites Least absolute shrinkage is equivalent to quadratic penalization.

Deep Weight Factorization: Sparse Learning Through the Lens of Artificial Symmetries Least absolute shrinkage is equivalent to quadratic penalization

Reference 22

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This paper cites Implicit bias of gradient descent on linear convolutional networks.

Deep Weight Factorization: Sparse Learning Through the Lens of Artificial Symmetries Implicit bias of gradient descent on linear convolutional networks

Reference 23

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This paper cites Expandnets: Linear over-parameterization to train compact convolutional networks.

Deep Weight Factorization: Sparse Learning Through the Lens of Artificial Symmetries Expandnets: Linear over-parameterization to train compact convolutional networks

Reference 24

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This paper cites Learning both weights and connections for efficient neural network.

Deep Weight Factorization: Sparse Learning Through the Lens of Artificial Symmetries Learning both weights and connections for efficient neural network

Reference 25

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This paper cites Matrix completion and low-rank svd via fast alternating least squares.

Deep Weight Factorization: Sparse Learning Through the Lens of Artificial Symmetries Matrix completion and low-rank svd via fast alternating least squares

Reference 26

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

Deep Weight Factorization: Sparse Learning Through the Lens of Artificial Symmetries Delving deep into rectifiers: Surpassing human-level performance on imagenet classification

Reference 27

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This paper cites Deep residual learning for image recognition.

Deep Weight Factorization: Sparse Learning Through the Lens of Artificial Symmetries Deep residual learning for image recognition

Reference 28

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This paper cites Structured pruning for deep convolutional neural networks: A survey.

Deep Weight Factorization: Sparse Learning Through the Lens of Artificial Symmetries Structured pruning for deep convolutional neural networks: A survey

Reference 29

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

Deep Weight Factorization: Sparse Learning Through the Lens of Artificial Symmetries Channel pruning for accelerating very deep neural networks

Reference 30

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This paper cites Sparsity in deep learning: Pruning and growth for efficient inference and training in neural networks.

Deep Weight Factorization: Sparse Learning Through the Lens of Artificial Symmetries Sparsity in deep learning: Pruning and growth for efficient inference and training in neural networks

Reference 31

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This paper cites Lasso, fractional norm and structured sparse estimation using a hadamard product parametrization.

Deep Weight Factorization: Sparse Learning Through the Lens of Artificial Symmetries Lasso, fractional norm and structured sparse estimation using a hadamard product parametrization

Reference 32

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Deep Weight Factorization: Sparse Learning Through the Lens of Artificial Symmetries Group sparse optimization via _ p,q regularization

Reference 33

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This paper cites Batch normalization: Accelerating deep network training by reducing internal covariate shift.

Deep Weight Factorization: Sparse Learning Through the Lens of Artificial Symmetries Batch normalization: Accelerating deep network training by reducing internal covariate shift

Reference 34

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This paper cites Implicit bias of large depth networks: a notion of rank for nonlinear functions.

Deep Weight Factorization: Sparse Learning Through the Lens of Artificial Symmetries Implicit bias of large depth networks: a notion of rank for nonlinear functions

Reference 35

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This paper cites Implicit rank-minimizing autoencoder.

Deep Weight Factorization: Sparse Learning Through the Lens of Artificial Symmetries Implicit rank-minimizing autoencoder

Reference 36

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This paper cites Dynamic sparse training: Find efficient sparse network from scratch with trainable masked layers.

Deep Weight Factorization: Sparse Learning Through the Lens of Artificial Symmetries Dynamic sparse training: Find efficient sparse network from scratch with trainable masked layers

Reference 37

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This paper cites Smoothing the Edges: Smooth Optimization for Sparse Regularization using Hadamard Overparametrization.

Deep Weight Factorization: Sparse Learning Through the Lens of Artificial Symmetries Smoothing the Edges: Smooth Optimization for Sparse Regularization using Hadamard Overparametrization

Reference 38

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Deep Weight Factorization: Sparse Learning Through the Lens of Artificial Symmetries Neural mechanics: Symmetry and broken conservation laws in deep learning dynamics

Reference 39

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Deep Weight Factorization: Sparse Learning Through the Lens of Artificial Symmetries Soft threshold weight reparameterization for learnable sparsity

Reference 40

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This paper cites Training invariances and the low-rank phenomenon: beyond linear networks.

Deep Weight Factorization: Sparse Learning Through the Lens of Artificial Symmetries Training invariances and the low-rank phenomenon: beyond linear networks

Reference 41

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Deep Weight Factorization: Sparse Learning Through the Lens of Artificial Symmetries Optimal brain damage

Reference 42

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Deep Weight Factorization: Sparse Learning Through the Lens of Artificial Symmetries Gradient-based learning applied to document recognition

Reference 43

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Deep Weight Factorization: Sparse Learning Through the Lens of Artificial Symmetries Efficient backprop

Reference 44

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Deep Weight Factorization: Sparse Learning Through the Lens of Artificial Symmetries Snip: single-shot network pruning based on connection sensitivity

Reference 45

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This paper cites Pruning filters for efficient convnets.

Deep Weight Factorization: Sparse Learning Through the Lens of Artificial Symmetries Pruning filters for efficient convnets

Reference 46

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This paper cites Implicit sparse regularization: The impact of depth and early stopping.

Deep Weight Factorization: Sparse Learning Through the Lens of Artificial Symmetries Implicit sparse regularization: The impact of depth and early stopping

Reference 47

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This paper cites Improving adaptivity via over-parameterization in sequence models.

Deep Weight Factorization: Sparse Learning Through the Lens of Artificial Symmetries Improving adaptivity via over-parameterization in sequence models

Reference 48

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This paper cites Reconciling modern deep learning with traditional optimization analyses: The intrinsic learning rate.

Deep Weight Factorization: Sparse Learning Through the Lens of Artificial Symmetries Reconciling modern deep learning with traditional optimization analyses: The intrinsic learning rate

Reference 49

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This paper cites Learning efficient convolutional networks through network slimming.

Deep Weight Factorization: Sparse Learning Through the Lens of Artificial Symmetries Learning efficient convolutional networks through network slimming

Reference 50

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Deep Weight Factorization: Sparse Learning Through the Lens of Artificial Symmetries Omnigrok: Grokking beyond algorithmic data

Reference 51

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This paper cites Sgdr: Stochastic gradient descent with warm restarts.

Deep Weight Factorization: Sparse Learning Through the Lens of Artificial Symmetries Sgdr: Stochastic gradient descent with warm restarts

Reference 52

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Deep Weight Factorization: Sparse Learning Through the Lens of Artificial Symmetries Unresolved cited work

Reference 53

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Observation 126dd1a8-b6b8-4512-adb0-635038e6e608 · outbound

This paper cites Spectral regularization algorithms for learning large incomplete matrices.

Deep Weight Factorization: Sparse Learning Through the Lens of Artificial Symmetries Spectral regularization algorithms for learning large incomplete matrices

Reference 54

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This paper cites High-dimensional graphs and variable selection with the lasso.

Deep Weight Factorization: Sparse Learning Through the Lens of Artificial Symmetries High-dimensional graphs and variable selection with the lasso

Reference 55

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Observation f948bffc-fbf5-4a87-8d3a-465ed38ecb6d · outbound

This paper cites Implicit bias of the step size in linear diagonal neural networks.

Deep Weight Factorization: Sparse Learning Through the Lens of Artificial Symmetries Implicit bias of the step size in linear diagonal neural networks

Reference 56

Resolution: verified fuzzy; raw_fallback, observed 2026-08-09T12:04:05.846401Z

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No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation 09fc183d-1831-4c16-abc4-e001de11a79a · outbound

This paper cites In search of the real inductive bias: On the role of implicit regularization in deep learning.

Deep Weight Factorization: Sparse Learning Through the Lens of Artificial Symmetries In search of the real inductive bias: On the role of implicit regularization in deep learning

Reference 57

Resolution: verified fuzzy; raw_fallback, observed 2026-08-09T12:04:05.835865Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation 136e14fa-fc5d-4f6d-bf9d-f5ed0b0df805 · outbound

This paper cites Decoupled Weight Decay for Any $p$ Norm.

Deep Weight Factorization: Sparse Learning Through the Lens of Artificial Symmetries Decoupled Weight Decay for Any $p$ Norm

Reference 58

Resolution: verified exact; local_arxiv, observed 2026-08-09T12:04:05.462095Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation aafbba80-6ac6-49f7-81dc-9c2165621b7e · outbound

This paper cites Kurdyka-- ojasiewicz exponent via hadamard parametrization.

Deep Weight Factorization: Sparse Learning Through the Lens of Artificial Symmetries Kurdyka-- ojasiewicz exponent via hadamard parametrization

Reference 59

Resolution: verified fuzzy; raw_fallback, observed 2026-08-09T12:04:05.824860Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation 4443064b-fcc8-49cf-a926-af3f6a866f40 · outbound

This paper cites Deep learning meets sparse regularization: A signal processing perspective.

Deep Weight Factorization: Sparse Learning Through the Lens of Artificial Symmetries Deep learning meets sparse regularization: A signal processing perspective

Reference 60

Resolution: verified fuzzy; raw_fallback, observed 2026-08-09T12:04:05.812794Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation 5307d155-306a-49ab-9f08-a28ca50254ab · outbound

This paper cites Implicit bias of sgd for diagonal linear networks: a provable benefit of stochasticity.

Deep Weight Factorization: Sparse Learning Through the Lens of Artificial Symmetries Implicit bias of sgd for diagonal linear networks: a provable benefit of stochasticity

Reference 61

Resolution: verified fuzzy; raw_fallback, observed 2026-08-09T12:04:05.801427Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation 309ff82c-e912-4c76-bfb3-8b53a5862353 · outbound

This paper cites The exp-normal distribution is infinitely divisible.

Deep Weight Factorization: Sparse Learning Through the Lens of Artificial Symmetries The exp-normal distribution is infinitely divisible

Reference 62

Resolution: verified exact; local_arxiv, observed 2026-08-09T12:04:05.446144Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation 9972556b-b7cd-4bbd-837f-55bbe5176b6e · outbound

This paper cites Smooth bilevel programming for sparse regularization.

Deep Weight Factorization: Sparse Learning Through the Lens of Artificial Symmetries Smooth bilevel programming for sparse regularization

Reference 63

Resolution: verified fuzzy; raw_fallback, observed 2026-08-09T12:04:05.789820Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation e8fe5e76-ad00-470a-9965-36f26aa9520a · outbound

This paper cites Smooth over-parameterized solvers for non-smooth structured optimization.

Deep Weight Factorization: Sparse Learning Through the Lens of Artificial Symmetries Smooth over-parameterized solvers for non-smooth structured optimization

Reference 64

Resolution: verified fuzzy; raw_fallback, observed 2026-08-09T12:04:05.778650Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation bd435dd2-936c-4dd4-b594-fca179c85271 · outbound

This paper cites Grokking: Generalization Beyond Overfitting on Small Algorithmic Datasets.

Deep Weight Factorization: Sparse Learning Through the Lens of Artificial Symmetries Grokking: Generalization Beyond Overfitting on Small Algorithmic Datasets

Reference 65

Resolution: unresolved; no resolver link, observed 2026-08-09T12:04:05.057837Z

Source-reported events for the cited work

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Observation a9a0a3a8-f806-4a72-aa84-4f5183ffcc3b · outbound

This paper cites Winning the lottery with continuous sparsification.

Deep Weight Factorization: Sparse Learning Through the Lens of Artificial Symmetries Winning the lottery with continuous sparsification

Reference 66

Resolution: verified fuzzy; raw_fallback, observed 2026-08-09T12:04:05.768378Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation beeba61a-d4f0-4668-9c72-91c5bd072dfa · outbound

This paper cites Group sparse regularization for deep neural networks.

Deep Weight Factorization: Sparse Learning Through the Lens of Artificial Symmetries Group sparse regularization for deep neural networks

Reference 67

Resolution: unresolved; no resolver link, observed 2026-08-09T12:04:05.067450Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 700c5dfb-eec1-4f96-91f8-6ea67fde666f · outbound

This paper cites Powerpropagation: A sparsity inducing weight reparameterisation.

Deep Weight Factorization: Sparse Learning Through the Lens of Artificial Symmetries Powerpropagation: A sparsity inducing weight reparameterisation

Reference 68

Resolution: verified fuzzy; raw_fallback, observed 2026-08-09T12:04:05.750367Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation b9811267-b1ca-4350-bef8-1531580405a9 · outbound

This paper cites A unified scalable equivalent formulation for schatten quasi-norms.

Deep Weight Factorization: Sparse Learning Through the Lens of Artificial Symmetries A unified scalable equivalent formulation for schatten quasi-norms

Reference 69

Resolution: verified fuzzy; raw_fallback, observed 2026-08-09T12:04:05.738742Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation b16635a4-1baa-4dfa-bccf-404da5762e1c · outbound

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

Deep Weight Factorization: Sparse Learning Through the Lens of Artificial Symmetries Very Deep Convolutional Networks for Large-Scale Image Recognition

Reference 70

Resolution: unresolved; no resolver link, observed 2026-08-09T12:04:05.077734Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 0da044a0-ad4c-4cba-b14c-e3b23039271e · outbound

This paper cites Geometry of the loss landscape in overparameterized neural networks: Symmetries and invariances.

Deep Weight Factorization: Sparse Learning Through the Lens of Artificial Symmetries Geometry of the loss landscape in overparameterized neural networks: Symmetries and invariances

Reference 71

Resolution: verified fuzzy; raw_fallback, observed 2026-08-09T12:04:05.727781Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation ce40cb9a-afb7-4433-9926-c137f1ab3fcf · outbound

This paper cites an unresolved cited work.

Deep Weight Factorization: Sparse Learning Through the Lens of Artificial Symmetries Unresolved cited work

Reference 72

Resolution: unresolved; raw_fallback, observed 2026-08-09T12:04:05.718151Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation 0025309a-58bd-4f88-9d79-a416e9eabc64 · outbound

This paper cites Pruning neural networks without any data by iteratively conserving synaptic flow.

Deep Weight Factorization: Sparse Learning Through the Lens of Artificial Symmetries Pruning neural networks without any data by iteratively conserving synaptic flow

Reference 73

Resolution: verified fuzzy; raw_fallback, observed 2026-08-09T12:04:05.708493Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation e68645e6-334e-45b4-8a1c-9792d1c23ac2 · outbound

This paper cites A comprehensive survey on regularization strategies in machine learning.

Deep Weight Factorization: Sparse Learning Through the Lens of Artificial Symmetries A comprehensive survey on regularization strategies in machine learning

Reference 74

Resolution: verified fuzzy; raw_fallback, observed 2026-08-09T12:04:05.697344Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation 919ac0f0-0f44-4ac2-bb06-07c2f83f0dc6 · outbound

This paper cites Regression shrinkage and selection via the lasso.

Deep Weight Factorization: Sparse Learning Through the Lens of Artificial Symmetries Regression shrinkage and selection via the lasso

Reference 75

Resolution: unresolved; no resolver link, observed 2026-08-09T12:04:05.095304Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 28eaf8e3-34cf-48d1-88f3-c4fca7b41caf · outbound

This paper cites Equivalences between sparse models and neural networks.

Deep Weight Factorization: Sparse Learning Through the Lens of Artificial Symmetries Equivalences between sparse models and neural networks

Reference 76

Resolution: verified fuzzy; raw_fallback, observed 2026-08-09T12:04:05.681010Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation e34270ba-e0ac-43bf-9f7a-139efa9191c8 · outbound

This paper cites Implicit regularization for optimal sparse recovery.

Deep Weight Factorization: Sparse Learning Through the Lens of Artificial Symmetries Implicit regularization for optimal sparse recovery

Reference 77

Resolution: verified fuzzy; raw_fallback, observed 2026-08-09T12:04:05.669970Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation 111ed457-227b-44fb-abc7-48e2117d3a6e · outbound

This paper cites Picking winning tickets before training by preserving gradient flow.

Deep Weight Factorization: Sparse Learning Through the Lens of Artificial Symmetries Picking winning tickets before training by preserving gradient flow

Reference 78

Resolution: unresolved; no resolver link, observed 2026-08-09T12:04:05.105178Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 967c4304-90e5-4274-a4ed-637c00b25d0d · outbound

This paper cites Why is the State of Neural Network Pruning so Confusing? On the Fairness, Comparison Setup, and Trainability in Network Pruning.

Deep Weight Factorization: Sparse Learning Through the Lens of Artificial Symmetries Why is the State of Neural Network Pruning so Confusing? On the Fairness, Comparison Setup, and Trainability in Network Pruning

Reference 79

Resolution: verified exact; local_arxiv, observed 2026-08-09T12:04:05.407473Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation ca96eb09-9d47-4891-86c5-5ef5f53d1d52 · outbound

This paper cites Random Weight Factorization Improves the Training of Continuous Neural Representations.

Deep Weight Factorization: Sparse Learning Through the Lens of Artificial Symmetries Random Weight Factorization Improves the Training of Continuous Neural Representations

Reference 80

Resolution: unresolved; no resolver link, observed 2026-08-09T12:04:05.112761Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation aa61edc1-4fbe-4359-a578-2a44c055ec90 · outbound

This paper cites Learning structured sparsity in deep neural networks.

Deep Weight Factorization: Sparse Learning Through the Lens of Artificial Symmetries Learning structured sparsity in deep neural networks

Reference 81

Resolution: verified fuzzy; raw_fallback, observed 2026-08-09T12:04:05.652335Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation fc4cf71a-fc03-4869-bbb7-38aa7bd3e965 · outbound

This paper cites Kernel and rich regimes in overparametrized models.

Deep Weight Factorization: Sparse Learning Through the Lens of Artificial Symmetries Kernel and rich regimes in overparametrized models

Reference 82

Resolution: unresolved; no resolver link, observed 2026-08-09T12:04:05.120352Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation e8afe785-06a5-4720-b873-13d7f5838641 · outbound

This paper cites L1/2 regularization.

Deep Weight Factorization: Sparse Learning Through the Lens of Artificial Symmetries L1/2 regularization

Reference 83

Resolution: verified fuzzy; raw_fallback, observed 2026-08-09T12:04:05.633547Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation 943ebc3b-ec84-44de-acba-8e34a6a73e76 · outbound

This paper cites Proxsgd: Training structured neural networks under regularization and constraints.

Deep Weight Factorization: Sparse Learning Through the Lens of Artificial Symmetries Proxsgd: Training structured neural networks under regularization and constraints

Reference 84

Resolution: verified fuzzy; raw_fallback, observed 2026-08-09T12:04:05.622291Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation 8558e657-d7dc-4818-b4f4-d34cdbb39ccf · outbound

This paper cites 92.45\ https://torch.ch/blog/2015/07/30/cifar.html, 2015.

Deep Weight Factorization: Sparse Learning Through the Lens of Artificial Symmetries 92.45\ https://torch.ch/blog/2015/07/30/cifar.html, 2015

Reference 85

Resolution: verified fuzzy; raw_fallback, observed 2026-08-09T12:04:05.611306Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation b774b5df-b27b-4713-b984-7af8d58ad069 · outbound

This paper cites Wide residual networks.

Deep Weight Factorization: Sparse Learning Through the Lens of Artificial Symmetries Wide residual networks

Reference 86

Resolution: unresolved; no resolver link, observed 2026-08-09T12:04:05.134231Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation b362e031-e64c-4978-826e-864628dd5a4c · outbound

This paper cites Nearly unbiased variable selection under minimax concave penalty.

Deep Weight Factorization: Sparse Learning Through the Lens of Artificial Symmetries Nearly unbiased variable selection under minimax concave penalty

Reference 87

Resolution: unresolved; no resolver link, observed 2026-08-09T12:04:05.137733Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation f3516908-d452-4913-8c73-a14de7f67101 · outbound

This paper cites The sparsity and bias of the lasso selection in high-dimensional linear regression.

Deep Weight Factorization: Sparse Learning Through the Lens of Artificial Symmetries The sparsity and bias of the lasso selection in high-dimensional linear regression

Reference 88

Resolution: verified fuzzy; raw_fallback, observed 2026-08-09T12:04:05.586155Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation 4aa5c346-c683-41a5-a931-3ffd85f595d6 · outbound

This paper cites How sparse can we prune a deep network: A fundamental limit perspective.

Deep Weight Factorization: Sparse Learning Through the Lens of Artificial Symmetries How sparse can we prune a deep network: A fundamental limit perspective

Reference 89

Resolution: verified fuzzy; raw_fallback, observed 2026-08-09T12:04:05.573187Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation b811e75a-6bae-4798-b202-d873ab74269f · outbound

This paper cites High-dimensional linear regression via implicit regularization.

Deep Weight Factorization: Sparse Learning Through the Lens of Artificial Symmetries High-dimensional linear regression via implicit regularization

Reference 90

Resolution: verified fuzzy; raw_fallback, observed 2026-08-09T12:04:05.562118Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation 665e1cf3-8c11-4a05-a0c3-11e99a11372c · outbound

This paper cites Effective sparsification of neural networks with global sparsity constraint.

Deep Weight Factorization: Sparse Learning Through the Lens of Artificial Symmetries Effective sparsification of neural networks with global sparsity constraint

Reference 91

Resolution: verified fuzzy; raw_fallback, observed 2026-08-09T12:04:05.551158Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation d34e6172-be2b-416a-bdf1-de894fbb14a4 · outbound

This paper cites Symmetry induces structure and constraint of learning.

Deep Weight Factorization: Sparse Learning Through the Lens of Artificial Symmetries Symmetry induces structure and constraint of learning

Reference 92

Resolution: verified fuzzy; raw_fallback, observed 2026-08-09T12:04:05.539229Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation 6470c0f4-13b4-4536-8257-8e413336c392 · outbound

This paper cites spred: Solving l1 penalty with sgd.

Deep Weight Factorization: Sparse Learning Through the Lens of Artificial Symmetries spred: Solving l1 penalty with sgd

Reference 93

Resolution: verified fuzzy; raw_fallback, observed 2026-08-09T12:04:05.528020Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation 20fef001-8622-46aa-9d73-ef0ad1fe1927 · outbound

This paper cites Type-II Saddles and Probabilistic Stability of Stochastic Gradient Descent.

Deep Weight Factorization: Sparse Learning Through the Lens of Artificial Symmetries Type-II Saddles and Probabilistic Stability of Stochastic Gradient Descent

Reference 94

Resolution: unresolved; no resolver link, observed 2026-08-09T12:04:05.161574Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation e9fadd08-deef-48aa-b347-612b8773ef82 · outbound

This paper cites write newline.

Deep Weight Factorization: Sparse Learning Through the Lens of Artificial Symmetries write newline

Reference 95

Resolution: unresolved; no resolver link, observed 2026-08-09T12:04:05.165596Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 66533bae-adcf-4fd5-ab1c-66eeb4bf1b86 · outbound

This paper cites @esa (Ref.

Deep Weight Factorization: Sparse Learning Through the Lens of Artificial Symmetries @esa (Ref

Reference 96

Resolution: unresolved; no resolver link, observed 2026-08-09T12:04:05.170153Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 60b7f51d-75a5-4ffd-979d-d18d7bc04051 · outbound

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Deep Weight Factorization: Sparse Learning Through the Lens of Artificial Symmetries Unresolved cited work

Reference 97

Resolution: unresolved; no resolver link, observed 2026-08-09T12:04:05.174042Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 6b298491-a2b6-45d2-8326-20c60dccf530 · outbound

This paper cites an unresolved cited work.

Deep Weight Factorization: Sparse Learning Through the Lens of Artificial Symmetries Unresolved cited work

Reference 98

Resolution: malformed identifier; no resolver link, observed 2026-08-09T12:04:05.177853Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

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Pith citing papers