Lattice theory and algebraic models for deep convolutional learning based on mathematical morphology | Paper Citation Record

Observation a16c705b-cffb-4cf3-bc19-3de815ef860e · outbound

This paper cites Some open questions on morphological operators and representations in the deep learning era,.

Lattice theory and algebraic models for deep convolutional learning based on mathematical morphology Some open questions on morphological operators and representations in the deep learning era,

Reference 1

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This paper cites Nonlinear Representation Theory of Equivariant CNNs on Homogeneous Spaces Using Group Morphology,.

Lattice theory and algebraic models for deep convolutional learning based on mathematical morphology Nonlinear Representation Theory of Equivariant CNNs on Homogeneous Spaces Using Group Morphology,

Reference 2

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This paper cites Group morphology fixed points on homogeneous spaces for deep learning equivariant networks,.

Lattice theory and algebraic models for deep convolutional learning based on mathematical morphology Group morphology fixed points on homogeneous spaces for deep learning equivariant networks,

Reference 3

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This paper cites A mathematical morphology view of the universal representation of scatter- ingnetworks,.

Lattice theory and algebraic models for deep convolutional learning based on mathematical morphology A mathematical morphology view of the universal representation of scatter- ingnetworks,

Reference 4

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This paper cites Understanding deep neural net- works with rectified linear units,.

Lattice theory and algebraic models for deep convolutional learning based on mathematical morphology Understanding deep neural net- works with rectified linear units,

Reference 5

Resolution: verified fuzzy; raw_fallback, observed 2026-07-09T01:05:49.932828Z

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Observation 83185f0a-95e7-4906-9bad-b2fbec4593ef · outbound

This paper cites Decomposition of mappings between complete lattices by mathematical morphology, Part I: General lattices,.

Lattice theory and algebraic models for deep convolutional learning based on mathematical morphology Decomposition of mappings between complete lattices by mathematical morphology, Part I: General lattices,

Reference 6

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Observation 986c9b3b-2321-4950-a5fc-78a628e261d0 · outbound

This paper cites Morphological adjunctions rep- resented by matrices in max-plus algebra for signal and image processing,.

Lattice theory and algebraic models for deep convolutional learning based on mathematical morphology Morphological adjunctions rep- resented by matrices in max-plus algebra for signal and image processing,

Reference 7

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This paper cites Training morphological neural networks with gradient descent: some theoret- ical insights,.

Lattice theory and algebraic models for deep convolutional learning based on mathematical morphology Training morphological neural networks with gradient descent: some theoret- ical insights,

Reference 8

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Observation 945e2733-c0f6-444b-9375-c5c67ec9d2b0 · outbound

This paper cites Improving mor- phological networks for learning image-to-image transforms,.

Lattice theory and algebraic models for deep convolutional learning based on mathematical morphology Improving mor- phological networks for learning image-to-image transforms,

Reference 9

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This paper cites Categorical foundations of gradient-based learning,.

Lattice theory and algebraic models for deep convolutional learning based on mathematical morphology Categorical foundations of gradient-based learning,

Reference 10

Resolution: verified fuzzy; raw_fallback, observed 2026-07-09T01:05:49.913243Z

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Observation e4eb333a-1ece-4e16-a657-54330f64a724 · outbound

This paper cites Theory of morphological neural networks,.

Lattice theory and algebraic models for deep convolutional learning based on mathematical morphology Theory of morphological neural networks,

Reference 11

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This paper cites Advances in morphological neural networks: training, pruning and enforcing shape constraints,.

Lattice theory and algebraic models for deep convolutional learning based on mathematical morphology Advances in morphological neural networks: training, pruning and enforcing shape constraints,

Reference 12

Resolution: verified fuzzy; raw_fallback, observed 2026-07-09T01:05:49.914986Z

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Observation baeacf6f-ac48-4c17-a409-d0448775d916 · outbound

This paper cites Morphological neural networks: expressing and learning better geometric features,.

Lattice theory and algebraic models for deep convolutional learning based on mathematical morphology Morphological neural networks: expressing and learning better geometric features,

Reference 13

Resolution: verified fuzzy; raw_fallback, observed 2026-07-09T01:05:49.907735Z

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Observation e19c7319-f4d7-4649-9cd7-1096a24c3e91 · outbound

This paper cites Learning morphological representations of image transformations: influence of initialization and layer differentiability,.

Lattice theory and algebraic models for deep convolutional learning based on mathematical morphology Learning morphological representations of image transformations: influence of initialization and layer differentiability,

Reference 14

Resolution: verified fuzzy; raw_fallback, observed 2026-07-09T01:05:49.902634Z

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Observation ab3c32fa-1dc1-4b90-b55d-44073a2bdfbb · outbound

This paper cites Backprop as functor: a compositional perspec- tive on supervised learning,.

Lattice theory and algebraic models for deep convolutional learning based on mathematical morphology Backprop as functor: a compositional perspec- tive on supervised learning,

Reference 15

Resolution: verified fuzzy; raw_fallback, observed 2026-07-09T01:05:49.904338Z

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Observation a3bb27e2-4177-437f-bb0b-edee05d6b438 · outbound

This paper cites Training Deep Morphological Neural Networks as Universal Approximators.

Lattice theory and algebraic models for deep convolutional learning based on mathematical morphology Training Deep Morphological Neural Networks as Universal Approximators

Reference 16

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Observation c952cf17-0550-41c7-8e95-c08ff10664bd · outbound

This paper cites Deep morphological networks,.

Lattice theory and algebraic models for deep convolutional learning based on mathematical morphology Deep morphological networks,

Reference 17

Resolution: verified fuzzy; raw_fallback, observed 2026-07-09T01:05:49.906019Z

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Observation 370e3938-8b7e-4a39-b90a-e0b9666e7199 · outbound

This paper cites Goodfellow, Y.

Lattice theory and algebraic models for deep convolutional learning based on mathematical morphology Goodfellow, Y

Reference 18

Resolution: verified fuzzy; raw_fallback, observed 2026-07-09T01:05:49.949563Z

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Observation 5315ff3c-929b-4999-b44e-9c02922d5cec · outbound

This paper cites Nonlinear multiresolution signal decomposition schemes – Part I: morphological pyramids,.

Lattice theory and algebraic models for deep convolutional learning based on mathematical morphology Nonlinear multiresolution signal decomposition schemes – Part I: morphological pyramids,

Reference 19

Resolution: verified fuzzy; raw_fallback, observed 2026-07-09T01:05:49.938796Z

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Observation 536695f2-3870-4c2a-b5ad-f1ea24a56e15 · outbound

This paper cites The algebraic basis of mathematical morphology I. Dilations and erosions,.

Lattice theory and algebraic models for deep convolutional learning based on mathematical morphology The algebraic basis of mathematical morphology I. Dilations and erosions,

Reference 20

Resolution: verified fuzzy; raw_fallback, observed 2026-07-09T01:05:49.940669Z

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Observation 0fac954f-7047-4836-8a0a-e300d41b7f72 · outbound

This paper cites an unresolved cited work.

Lattice theory and algebraic models for deep convolutional learning based on mathematical morphology Unresolved cited work

Reference 21

Resolution: unresolved; raw_fallback, observed 2026-07-09T01:05:49.965471Z

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Observation cf65bf86-d2f0-47e9-bc2c-5028aba66b79 · outbound

This paper cites Delving deep into rectifiers: surpassing human-level performance on ImageNet classification,.

Lattice theory and algebraic models for deep convolutional learning based on mathematical morphology Delving deep into rectifiers: surpassing human-level performance on ImageNet classification,

Reference 22

Resolution: verified fuzzy; raw_fallback, observed 2026-07-09T01:05:49.927301Z

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Observation 08f3dfb1-10b3-4b1a-a46c-caf4b21f0b31 · outbound

This paper cites Deep residual learning for image recognition,.

Lattice theory and algebraic models for deep convolutional learning based on mathematical morphology Deep residual learning for image recognition,

Reference 23

Resolution: verified fuzzy; raw_fallback, observed 2026-07-09T01:05:49.918308Z

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Observation b958e0fe-efb6-4323-8014-bf847dded272 · outbound

This paper cites Learning grayscale mathematical morphology with smooth morphological layers,.

Lattice theory and algebraic models for deep convolutional learning based on mathematical morphology Learning grayscale mathematical morphology with smooth morphological layers,

Reference 24

Resolution: verified fuzzy; raw_fallback, observed 2026-07-09T01:05:49.945761Z

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Observation e49100cb-9a54-4061-9fc1-a802136d8e2e · outbound

This paper cites A Morphological View on Traditional Signal Processing,.

Lattice theory and algebraic models for deep convolutional learning based on mathematical morphology A Morphological View on Traditional Signal Processing,

Reference 25

Resolution: verified fuzzy; raw_fallback, observed 2026-07-09T01:05:49.947603Z

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Observation 3d3639ab-ee98-4fc2-8a4d-7a6a041288e9 · outbound

This paper cites Implementation of linear digital filters based on mor- phological representation theory,.

Lattice theory and algebraic models for deep convolutional learning based on mathematical morphology Implementation of linear digital filters based on mor- phological representation theory,

Reference 26

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Observation 6b94a331-e0c1-49c2-a18b-517599698655 · outbound

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

Lattice theory and algebraic models for deep convolutional learning based on mathematical morphology Gradient-based learning applied to document recognition,

Reference 27

Resolution: verified fuzzy; raw_fallback, observed 2026-07-09T01:05:49.916636Z

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Observation 97ace582-cae4-4b4c-a1ae-8f79b3eae9fd · outbound

This paper cites Rectifier nonlinearities improve neural net- work acoustic models,.

Lattice theory and algebraic models for deep convolutional learning based on mathematical morphology Rectifier nonlinearities improve neural net- work acoustic models,

Reference 28

Resolution: verified fuzzy; raw_fallback, observed 2026-07-09T01:05:49.962138Z

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Observation e45e2e79-3b81-418d-a906-5276a0381edb · outbound

This paper cites Group invariant scattering,.

Lattice theory and algebraic models for deep convolutional learning based on mathematical morphology Group invariant scattering,

Reference 29

Resolution: verified fuzzy; raw_fallback, observed 2026-07-09T01:05:49.925502Z

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Observation 474a0cd1-5ab6-41fe-a1a2-940521751035 · outbound

This paper cites Morphological filters – Part I: their set-theoretic analysis and relations to linear shift-invariant filters,.

Lattice theory and algebraic models for deep convolutional learning based on mathematical morphology Morphological filters – Part I: their set-theoretic analysis and relations to linear shift-invariant filters,

Reference 30

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Observation 7ac31b85-cfe9-4b3a-83de-0d63050caa0a · outbound

This paper cites Arepresentationtheoryformorphologicalimageandsignalprocessing,.

Lattice theory and algebraic models for deep convolutional learning based on mathematical morphology Arepresentationtheoryformorphologicalimageandsignalprocessing,

Reference 31

Resolution: verified fuzzy; raw_fallback, observed 2026-07-09T01:05:49.942324Z

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Observation 0da1bd95-a5ca-4850-b16b-8d1589d55f99 · outbound

This paper cites Tropical geometry, morphological analysis, and deep neural networks,.

Lattice theory and algebraic models for deep convolutional learning based on mathematical morphology Tropical geometry, morphological analysis, and deep neural networks,

Reference 32

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Observation 818e6243-1bc9-46b6-a097-69b1adf32869 · outbound

This paper cites The lattice overparameterization paradigm for the machine learningoflatticeoperators,.

Lattice theory and algebraic models for deep convolutional learning based on mathematical morphology The lattice overparameterization paradigm for the machine learningoflatticeoperators,

Reference 33

Resolution: verified fuzzy; raw_fallback, observed 2026-07-09T01:05:49.976172Z

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Observation c7ed3786-b219-45dd-b06b-f0881556d47e · outbound

This paper cites Matheron,Random Sets and Integral Geometry, Wiley, New York, 1975.

Lattice theory and algebraic models for deep convolutional learning based on mathematical morphology Matheron,Random Sets and Integral Geometry, Wiley, New York, 1975

Reference 34

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Observation 3b97298d-6cc3-40cb-aef4-3ef6436aa1d5 · outbound

This paper cites On the number of linear regions of deep neural networks,.

Lattice theory and algebraic models for deep convolutional learning based on mathematical morphology On the number of linear regions of deep neural networks,

Reference 35

Resolution: verified fuzzy; raw_fallback, observed 2026-07-09T01:05:49.972568Z

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Observation b15b2dcc-27ee-4cf1-99b3-712fd3335d7b · outbound

This paper cites Max-min representation of piecewise linear functions,.

Lattice theory and algebraic models for deep convolutional learning based on mathematical morphology Max-min representation of piecewise linear functions,

Reference 36

Resolution: verified fuzzy; raw_fallback, observed 2026-07-09T01:05:49.978047Z

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Observation 5412deb3-9b85-401d-a7fb-a259d9ee910c · outbound

This paper cites Group equivariant networks using morphological operators,.

Lattice theory and algebraic models for deep convolutional learning based on mathematical morphology Group equivariant networks using morphological operators,

Reference 37

Resolution: verified fuzzy; raw_fallback, observed 2026-07-09T01:05:49.979789Z

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Observation a55902ae-6c11-42b8-bb98-c2ce53e21d28 · outbound

This paper cites Group equivariant morphological networks,.

Lattice theory and algebraic models for deep convolutional learning based on mathematical morphology Group equivariant morphological networks,

Reference 38

Resolution: verified exact; doi, observed 2026-06-30T13:44:40.397290Z

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Observation d5eaa344-92ed-4501-ae53-c5475f732d28 · outbound

This paper cites Neural networks with hybrid morphological/rank/linear nodes: a unifying framework with applications to handwritten character recognition,.

Lattice theory and algebraic models for deep convolutional learning based on mathematical morphology Neural networks with hybrid morphological/rank/linear nodes: a unifying framework with applications to handwritten character recognition,

Reference 39

Resolution: verified fuzzy; raw_fallback, observed 2026-07-09T01:05:49.970838Z

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Observation 6d9a514d-2a73-4525-8f0c-aed378955068 · outbound

This paper cites On the spectral bias of neural networks,.

Lattice theory and algebraic models for deep convolutional learning based on mathematical morphology On the spectral bias of neural networks,

Reference 40

Resolution: verified fuzzy; raw_fallback, observed 2026-07-09T01:05:49.967172Z

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Observation 3237e791-6c89-4bb8-86c1-2222800ae103 · outbound

This paper cites An introduction to morphological neural networks,.

Lattice theory and algebraic models for deep convolutional learning based on mathematical morphology An introduction to morphological neural networks,

Reference 41

Resolution: verified fuzzy; raw_fallback, observed 2026-07-09T01:05:49.969024Z

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Observation 5c7d606e-414e-4293-95d4-9d62efe39f07 · outbound

This paper cites U-Net: convolutional networks for biomedical image segmentation,.

Lattice theory and algebraic models for deep convolutional learning based on mathematical morphology U-Net: convolutional networks for biomedical image segmentation,

Reference 42

Resolution: verified fuzzy; raw_fallback, observed 2026-07-09T01:05:49.963824Z

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Observation f3218782-d53f-405c-848a-5b8639c04117 · outbound

This paper cites Scale equivariant neural net- works with morphological scale-spaces,.

Lattice theory and algebraic models for deep convolutional learning based on mathematical morphology Scale equivariant neural net- works with morphological scale-spaces,

Reference 43

Resolution: verified fuzzy; raw_fallback, observed 2026-07-09T01:05:49.956798Z

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Observation 33ccffec-43d1-43ad-badf-a0bb1424db03 · outbound

This paper cites Serra,Image Analysis and Mathematical Morphology, Academic Press, London, 1982.

Lattice theory and algebraic models for deep convolutional learning based on mathematical morphology Serra,Image Analysis and Mathematical Morphology, Academic Press, London, 1982

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Resolution: verified fuzzy; raw_fallback, observed 2026-07-09T01:05:49.951507Z

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Observation 84a41356-dd2e-4a15-a7df-970c26612219 · outbound

This paper cites Attention is all you need,.

Lattice theory and algebraic models for deep convolutional learning based on mathematical morphology Attention is all you need,

Reference 45

Resolution: verified fuzzy; raw_fallback, observed 2026-07-09T01:05:49.953179Z

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Observation 75cee1ee-a993-4953-aa6f-14e16468e522 · outbound

This paper cites Learnable empirical mode decomposition based on mathematical morphology,.

Lattice theory and algebraic models for deep convolutional learning based on mathematical morphology Learnable empirical mode decomposition based on mathematical morphology,

Reference 46

Resolution: verified exact; doi, observed 2026-06-30T13:44:40.399975Z

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Observation 1abac055-23e1-4afc-a4f0-586f144dbe89 · outbound

This paper cites Fixed point layers for geodesic morphologi- cal operations,.

Lattice theory and algebraic models for deep convolutional learning based on mathematical morphology Fixed point layers for geodesic morphologi- cal operations,

Reference 47

Resolution: verified fuzzy; raw_fallback, observed 2026-07-09T01:05:49.954984Z

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Observation 8cbfeb8a-51d7-4c15-8d40-dc61d3e23547 · outbound

This paper cites MorphoActivation: generalizing ReLU activation func- tion by mathematical morphology,.

Lattice theory and algebraic models for deep convolutional learning based on mathematical morphology MorphoActivation: generalizing ReLU activation func- tion by mathematical morphology,

Reference 48

Resolution: verified fuzzy; raw_fallback, observed 2026-07-09T01:05:49.958576Z

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Observation 819c2996-3631-4958-8c64-f656fc483297 · outbound

This paper cites Tropical Geometry of Deep Neural Networks.

Lattice theory and algebraic models for deep convolutional learning based on mathematical morphology Tropical Geometry of Deep Neural Networks

Reference 49

Resolution: verified exact; local_arxiv, observed 2026-06-30T13:44:40.613451Z

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