Towards Scalable Topological Regularizers | Paper Citation Record

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This paper cites write newline.

Towards Scalable Topological Regularizers write newline

Reference 1

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This paper cites Wasserstein generative adversarial networks.

Towards Scalable Topological Regularizers Wasserstein generative adversarial networks

Reference 2

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This paper cites Aronszajn.

Towards Scalable Topological Regularizers Aronszajn

Reference 3

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This paper cites On the Expressivity of Persistent Homology in Graph Learning , June 2024.

Towards Scalable Topological Regularizers On the Expressivity of Persistent Homology in Graph Learning , June 2024

Reference 4

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This paper cites Manifold topology divergence: A framework for comparing data manifolds.

Towards Scalable Topological Regularizers Manifold topology divergence: A framework for comparing data manifolds

Reference 5

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This paper cites Ripser: Efficient computation of Vietoris -- Rips persistence barcodes.

Towards Scalable Topological Regularizers Ripser: Efficient computation of Vietoris -- Rips persistence barcodes

Reference 6

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This paper cites The Cramer Distance as a Solution to Biased Wasserstein Gradients.

Towards Scalable Topological Regularizers The Cramer Distance as a Solution to Biased Wasserstein Gradients

Reference 7

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This paper cites Stabilizing the unstable output of persistent homology computations.

Towards Scalable Topological Regularizers Stabilizing the unstable output of persistent homology computations

Reference 8

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This paper cites A closer look at the optimization landscapes of generative adversarial networks.

Towards Scalable Topological Regularizers A closer look at the optimization landscapes of generative adversarial networks

Reference 9

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This paper cites Blumberg, Itamar Gal, Michael A.

Towards Scalable Topological Regularizers Blumberg, Itamar Gal, Michael A

Reference 10

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This paper cites Verifying the Union of Manifolds Hypothesis for Image Data.

Towards Scalable Topological Regularizers Verifying the Union of Manifolds Hypothesis for Image Data

Reference 11

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This paper cites Virtual persistence diagrams, signed measures, Wasserstein distances, and Banach spaces.

Towards Scalable Topological Regularizers Virtual persistence diagrams, signed measures, Wasserstein distances, and Banach spaces

Reference 12

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Observation cdf77ab2-09a6-4dd4-8ae9-f54300c6044c · outbound

This paper cites Approximating Persistent Homology for Large Datasets , May 2022.

Towards Scalable Topological Regularizers Approximating Persistent Homology for Large Datasets , May 2022

Reference 13

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This paper cites Optimizing persistent homology based functions.

Towards Scalable Topological Regularizers Optimizing persistent homology based functions

Reference 14

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Observation 66f3a985-ccfe-4106-8d80-ec0fd078af36 · outbound

This paper cites PHom-GeM : Persistent homology for generative models.

Towards Scalable Topological Regularizers PHom-GeM : Persistent homology for generative models

Reference 15

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This paper cites Subsampling methods for persistent homology.

Towards Scalable Topological Regularizers Subsampling methods for persistent homology

Reference 16

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This paper cites Anime face dataset, 2019.

Towards Scalable Topological Regularizers Anime face dataset, 2019

Reference 17

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This paper cites Deep Learning for Classical Japanese Literature.

Towards Scalable Topological Regularizers Deep Learning for Classical Japanese Literature

Reference 18

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Towards Scalable Topological Regularizers Unresolved cited work

Reference 19

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This paper cites Sinkhorn distances: Lightspeed computation of optimal transport.

Towards Scalable Topological Regularizers Sinkhorn distances: Lightspeed computation of optimal transport

Reference 20

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This paper cites Nas-sgan: a semi-supervised generative adversarial network model for atypia scoring of breast cancer histopathological images.

Towards Scalable Topological Regularizers Nas-sgan: a semi-supervised generative adversarial network model for atypia scoring of breast cancer histopathological images

Reference 21

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This paper cites Semi-supervised generative adversarial networks for the segmentation of the left ventricle in pediatric mri.

Towards Scalable Topological Regularizers Semi-supervised generative adversarial networks for the segmentation of the left ventricle in pediatric mri

Reference 22

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This paper cites Computational Topology for Data Analysis.

Towards Scalable Topological Regularizers Computational Topology for Data Analysis

Reference 23

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This paper cites Dieudonne.

Towards Scalable Topological Regularizers Dieudonne

Reference 24

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This paper cites Understanding the topology and the geometry of the space of persistence diagrams via optimal partial transport.

Towards Scalable Topological Regularizers Understanding the topology and the geometry of the space of persistence diagrams via optimal partial transport

Reference 25

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This paper cites On the choice of weight functions for linear representations of persistence diagrams.

Towards Scalable Topological Regularizers On the choice of weight functions for linear representations of persistence diagrams

Reference 26

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Towards Scalable Topological Regularizers Unresolved cited work

Reference 27

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This paper cites Signatures, lipschitz-free spaces, and paths of persistence diagrams.

Towards Scalable Topological Regularizers Signatures, lipschitz-free spaces, and paths of persistence diagrams

Reference 28

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This paper cites Curvature Sets Over Persistence Diagrams.

Towards Scalable Topological Regularizers Curvature Sets Over Persistence Diagrams

Reference 29

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This paper cites Generative adversarial nets.

Towards Scalable Topological Regularizers Generative adversarial nets

Reference 30

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This paper cites Borgwardt, Malte J.

Towards Scalable Topological Regularizers Borgwardt, Malte J

Reference 31

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This paper cites Improved training of wasserstein gans.

Towards Scalable Topological Regularizers Improved training of wasserstein gans

Reference 32

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This paper cites A Survey of Topological Machine Learning Methods.

Towards Scalable Topological Regularizers A Survey of Topological Machine Learning Methods

Reference 33

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This paper cites Gans trained by a two time-scale update rule converge to a local nash equilibrium.

Towards Scalable Topological Regularizers Gans trained by a two time-scale update rule converge to a local nash equilibrium

Reference 34

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This paper cites Topology Distance : A Topology-Based Approach for Evaluating Generative Adversarial Networks.

Towards Scalable Topological Regularizers Topology Distance : A Topology-Based Approach for Evaluating Generative Adversarial Networks

Reference 35

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This paper cites Topological Graph Neural Networks.

Towards Scalable Topological Regularizers Topological Graph Neural Networks

Reference 36

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This paper cites Topology- Preserving Deep Image Segmentation.

Towards Scalable Topological Regularizers Topology- Preserving Deep Image Segmentation

Reference 37

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This paper cites Rethinking fid: Towards a better evaluation metric for image generation.

Towards Scalable Topological Regularizers Rethinking fid: Towards a better evaluation metric for image generation

Reference 38

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This paper cites Revisiting latent space of gan inversion for robust real image editing.

Towards Scalable Topological Regularizers Revisiting latent space of gan inversion for robust real image editing

Reference 39

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This paper cites Geometry score: A method for comparing generative adversarial networks.

Towards Scalable Topological Regularizers Geometry score: A method for comparing generative adversarial networks

Reference 40

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This paper cites Persistence weighted Gaussian kernel for topological data analysis.

Towards Scalable Topological Regularizers Persistence weighted Gaussian kernel for topological data analysis

Reference 41

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This paper cites Pytorch-topological: A topological machine learning framework for pytorch.

Towards Scalable Topological Regularizers Pytorch-topological: A topological machine learning framework for pytorch

Reference 42

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This paper cites Large scale computation of means and clusters for persistence diagrams using optimal transport.

Towards Scalable Topological Regularizers Large scale computation of means and clusters for persistence diagrams using optimal transport

Reference 43

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This paper cites A Framework for Differential Calculus on Persistence Barcodes.

Towards Scalable Topological Regularizers A Framework for Differential Calculus on Persistence Barcodes

Reference 44

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This paper cites Interaction matters: A note on non-asymptotic local convergence of generative adversarial networks.

Towards Scalable Topological Regularizers Interaction matters: A note on non-asymptotic local convergence of generative adversarial networks

Reference 45

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This paper cites Dual manifold adversarial robustness: Defense against lp and non-lp adversarial attacks.

Towards Scalable Topological Regularizers Dual manifold adversarial robustness: Defense against lp and non-lp adversarial attacks

Reference 46

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This paper cites Deep learning face attributes in the wild.

Towards Scalable Topological Regularizers Deep learning face attributes in the wild

Reference 47

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This paper cites Deep transfer learning with joint adaptation networks.

Towards Scalable Topological Regularizers Deep transfer learning with joint adaptation networks

Reference 48

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This paper cites SGDR: Stochastic Gradient Descent with Warm Restarts.

Towards Scalable Topological Regularizers SGDR: Stochastic Gradient Descent with Warm Restarts

Reference 49

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Towards Scalable Topological Regularizers Unresolved cited work

Reference 50

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This paper cites Adversarial neural pruning with latent vulnerability suppression.

Towards Scalable Topological Regularizers Adversarial neural pruning with latent vulnerability suppression

Reference 51

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This paper cites Few-shot cross-domain image generation via inference-time latent-code learning.

Towards Scalable Topological Regularizers Few-shot cross-domain image generation via inference-time latent-code learning

Reference 52

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This paper cites Kernel Mean Embedding of Distributions: A Review and Beyond.

Towards Scalable Topological Regularizers Kernel Mean Embedding of Distributions: A Review and Beyond

Reference 53

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This paper cites Topological Optimization with Big Steps.

Towards Scalable Topological Regularizers Topological Optimization with Big Steps

Reference 54

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This paper cites Manifold regularization and semi-supervised learning: Some theoretical analyses.

Towards Scalable Topological Regularizers Manifold regularization and semi-supervised learning: Some theoretical analyses

Reference 55

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This paper cites Dinov2: Learning robust visual features without supervision.

Towards Scalable Topological Regularizers Dinov2: Learning robust visual features without supervision

Reference 56

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This paper cites Porter, Ulrike Tillmann, Peter Grindrod, and Heather A.

Towards Scalable Topological Regularizers Porter, Ulrike Tillmann, Peter Grindrod, and Heather A

Reference 57

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This paper cites Bronstein, Gunnar E.

Towards Scalable Topological Regularizers Bronstein, Gunnar E

Reference 58

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This paper cites Giotto-ph: A Python Library for High-Performance Computation of Persistent Homology of Vietoris-Rips Filtrations , August 2021.

Towards Scalable Topological Regularizers Giotto-ph: A Python Library for High-Performance Computation of Persistent Homology of Vietoris-Rips Filtrations , August 2021

Reference 59

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Observation 6afd189d-d563-4703-a859-1dc51bb4c6f0 · outbound

This paper cites Learning transferable visual models from natural language supervision.

Towards Scalable Topological Regularizers Learning transferable visual models from natural language supervision

Reference 60

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This paper cites Generalized zero-and few-shot learning via aligned variational autoencoders.

Towards Scalable Topological Regularizers Generalized zero-and few-shot learning via aligned variational autoencoders

Reference 61

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Observation bbc23ff9-8102-4f0f-8d43-1216a17eb9f8 · outbound

This paper cites Differentiability and Optimization of Multiparameter Persistent Homology.

Towards Scalable Topological Regularizers Differentiability and Optimization of Multiparameter Persistent Homology

Reference 62

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Observation 7ccdb1af-cef5-4e96-ab0a-681d181a8f87 · outbound

This paper cites Paetzold, Anjany Sekuboyina, Ivan Ezhov, Alexander Unger, Andrey Zhylka, Josien P.

Towards Scalable Topological Regularizers Paetzold, Anjany Sekuboyina, Ivan Ezhov, Alexander Unger, Andrey Zhylka, Josien P

Reference 63

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Observation f304c13c-7294-4f02-8f5f-b8e4079e1fc2 · outbound

This paper cites Kernel distribution embeddings: Universal kernels, characteristic kernels and kernel metrics on distributions.

Towards Scalable Topological Regularizers Kernel distribution embeddings: Universal kernels, characteristic kernels and kernel metrics on distributions

Reference 64

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Observation d9587c8c-dae1-490a-a041-e8483414a5b2 · outbound

This paper cites Metrizing Weak Convergence with Maximum Mean Discrepancies.

Towards Scalable Topological Regularizers Metrizing Weak Convergence with Maximum Mean Discrepancies

Reference 65

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Observation 00f21ced-d643-4754-b900-774be873d3a1 · outbound

This paper cites From geometry to topology: Inverse theorems for distributed persistence.

Towards Scalable Topological Regularizers From geometry to topology: Inverse theorems for distributed persistence

Reference 66

Resolution: verified exact; doi, observed 2026-08-10T15:01:44.024244Z

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Observation 5e4493aa-89b2-49c1-9b04-f01e8ab414f8 · outbound

This paper cites A Fast and Robust Method for Global Topological Functional Optimization.

Towards Scalable Topological Regularizers A Fast and Robust Method for Global Topological Functional Optimization

Reference 67

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T15:01:44.821429Z

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Observation c752a679-816f-4ceb-a996-699cab656cb6 · outbound

This paper cites On the optimal estimation of probability measures in weak and strong topologies.

Towards Scalable Topological Regularizers On the optimal estimation of probability measures in weak and strong topologies

Reference 68

Resolution: verified exact; doi, observed 2026-08-10T15:01:44.009296Z

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Observation 3366099f-1a40-4ebb-a205-d7b08d427b42 · outbound

This paper cites Exposing flaws of generative model evaluation metrics and their unfair treatment of diffusion models.

Towards Scalable Topological Regularizers Exposing flaws of generative model evaluation metrics and their unfair treatment of diffusion models

Reference 69

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T15:01:44.810375Z

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Observation 3819a98e-381d-4c10-b62b-dfb10e347409 · outbound

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Towards Scalable Topological Regularizers Unresolved cited work

Reference 70

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Observation 343d2403-0a84-497e-aae9-9ec7b7e35c0d · outbound

This paper cites Deep coral: Correlation alignment for deep domain adaptation.

Towards Scalable Topological Regularizers Deep coral: Correlation alignment for deep domain adaptation

Reference 71

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Observation e1b36528-6a2c-491f-9047-caf815f22f05 · outbound

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Towards Scalable Topological Regularizers Return of frustratingly easy domain adaptation

Reference 72

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T15:01:44.776507Z

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Observation c579ea1d-101f-46d9-ba30-5e0219f59878 · outbound

This paper cites Distributing Persistent Homology via Spectral Sequences.

Towards Scalable Topological Regularizers Distributing Persistent Homology via Spectral Sequences

Reference 73

Resolution: verified exact; doi, observed 2026-08-10T15:01:43.997686Z

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Observation 09bd6718-9a8b-45f1-9171-7cabcc0c989a · outbound

This paper cites Semi-supervised seizure prediction with generative adversarial networks.

Towards Scalable Topological Regularizers Semi-supervised seizure prediction with generative adversarial networks

Reference 74

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T15:01:44.765917Z

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Observation 305c74f9-5bbd-46d9-96e7-eba4e139da5e · outbound

This paper cites Optimal Transport : Old and New.

Towards Scalable Topological Regularizers Optimal Transport : Old and New

Reference 75

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Observation fd99199e-eda0-4d33-812b-2a4dc3ee9d54 · outbound

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Towards Scalable Topological Regularizers Unresolved cited work

Reference 76

Resolution: verified exact; doi, observed 2026-08-10T15:01:43.977006Z

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Observation 78233cdd-ab41-45d0-9877-614122b22f81 · outbound

This paper cites Sganrda: semi-supervised generative adversarial networks for predicting circrna--disease associations.

Towards Scalable Topological Regularizers Sganrda: semi-supervised generative adversarial networks for predicting circrna--disease associations

Reference 77

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T15:01:44.755128Z

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Observation a2e0cb77-555a-4467-ab40-d9945b979165 · outbound

This paper cites Learning to diversify for single domain generalization.

Towards Scalable Topological Regularizers Learning to diversify for single domain generalization

Reference 78

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T15:01:44.743430Z

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Observation 6b4833f9-5e8f-4d7e-b5ad-d0508a692c26 · outbound

This paper cites Stabilizing Generative Adversarial Networks: A Survey.

Towards Scalable Topological Regularizers Stabilizing Generative Adversarial Networks: A Survey

Reference 79

Resolution: unresolved; no resolver link, observed 2026-08-10T15:01:43.888791Z

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Observation 71092963-0079-4930-a043-b37c6c300940 · outbound

This paper cites Fashion-MNIST: a Novel Image Dataset for Benchmarking Machine Learning Algorithms.

Towards Scalable Topological Regularizers Fashion-MNIST: a Novel Image Dataset for Benchmarking Machine Learning Algorithms

Reference 80

Resolution: unresolved; no resolver link, observed 2026-08-10T15:01:43.892658Z

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Observation 435487c5-2163-4d32-a21a-53ed1ab2a67e · outbound

This paper cites A multitask latent feature augmentation method for few-shot learning.

Towards Scalable Topological Regularizers A multitask latent feature augmentation method for few-shot learning

Reference 81

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T15:01:44.732780Z

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Observation 4efae462-dd25-4bf2-ad6e-e2f9f1841569 · outbound

This paper cites Larger norm more transferable: An adaptive feature norm approach for unsupervised domain adaptation.

Towards Scalable Topological Regularizers Larger norm more transferable: An adaptive feature norm approach for unsupervised domain adaptation

Reference 82

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T15:01:44.720274Z

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Observation 3eb1b4fb-a51c-4ddf-b43b-7f3fd397baef · outbound

This paper cites A survey on deep semi-supervised learning.

Towards Scalable Topological Regularizers A survey on deep semi-supervised learning

Reference 83

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T15:01:44.707991Z

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Observation 4ff3fe5c-67eb-447b-9e0e-17a7f2121bfb · outbound

This paper cites Persistence by Parts: Multiscale Feature Detection via Distributed Persistent Homology.

Towards Scalable Topological Regularizers Persistence by Parts: Multiscale Feature Detection via Distributed Persistent Homology

Reference 84

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Observation 3377359b-8d97-4d94-be32-a30e2ae35002 · outbound

This paper cites LSUN: Construction of a Large-scale Image Dataset using Deep Learning with Humans in the Loop.

Towards Scalable Topological Regularizers LSUN: Construction of a Large-scale Image Dataset using Deep Learning with Humans in the Loop

Reference 85

Resolution: unresolved; no resolver link, observed 2026-08-10T15:01:43.911098Z

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Observation c9828c67-e93d-4415-87a3-3fa88cae45e5 · outbound

This paper cites Lafeat: Piercing through adversarial defenses with latent features.

Towards Scalable Topological Regularizers Lafeat: Piercing through adversarial defenses with latent features

Reference 86

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T15:01:44.696549Z

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Observation abc2c4a8-b05d-45df-aa99-9d43a10a9503 · outbound

This paper cites GPU-Accelerated Computation of Vietoris-Rips Persistence Barcodes.

Towards Scalable Topological Regularizers GPU-Accelerated Computation of Vietoris-Rips Persistence Barcodes

Reference 87

Resolution: unresolved; no resolver link, observed 2026-08-10T15:01:43.917265Z

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Observation a7c5956d-bbea-4378-bbd0-d20d3f2bdbce · outbound

This paper cites Learning to generate novel domains for domain generalization.

Towards Scalable Topological Regularizers Learning to generate novel domains for domain generalization

Reference 88

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T15:01:44.684574Z

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Observation 19d40475-8ca9-4058-b5cd-cc8519181357 · outbound

This paper cites Ng, Gunnar E.

Towards Scalable Topological Regularizers Ng, Gunnar E

Reference 89

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T15:01:44.673648Z

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Observation d303aef8-8cbe-45c5-b1a9-e0bbfb1632ef · outbound

This paper cites Exploring Sparse MoE in GANs for Text-conditioned Image Synthesis.

Towards Scalable Topological Regularizers Exploring Sparse MoE in GANs for Text-conditioned Image Synthesis

Reference 90

Resolution: verified exact; local_arxiv, observed 2026-08-10T15:01:44.161287Z

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Observation 6675fdda-03c0-4031-b994-3e17e1f5d5dd · outbound

This paper cites @esa (Ref.

Towards Scalable Topological Regularizers @esa (Ref

Reference 91

Resolution: unresolved; no resolver link, observed 2026-08-10T15:01:43.929874Z

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Observation 73f54406-a90f-41e1-bc83-7368d1939c95 · outbound

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Towards Scalable Topological Regularizers Unresolved cited work

Reference 92

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Observation 22e4fed4-fa51-42a2-8c30-6d2b73e1c73d · outbound

This paper cites an unresolved cited work.

Towards Scalable Topological Regularizers Unresolved cited work

Reference 93

Resolution: unresolved; no resolver link, observed 2026-08-10T15:01:43.936659Z

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