CellCLAT: Preserving Topology and Trimming Redundancy in Self-Supervised Cellular Contrastive Learning | Paper Citation Record

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CellCLAT: Preserving Topology and Trimming Redundancy in Self-Supervised Cellular Contrastive Learning Unresolved cited work

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This paper cites an unresolved cited work.

CellCLAT: Preserving Topology and Trimming Redundancy in Self-Supervised Cellular Contrastive Learning Unresolved cited work

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Observation 72082f49-0d0f-43ac-8296-2afca9be5f86 · outbound

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

CellCLAT: Preserving Topology and Trimming Redundancy in Self-Supervised Cellular Contrastive Learning On the Expressivity of Persistent Homology in Graph Learning

Reference 3

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This paper cites E(n) Equivariant Topological Neural Networks.

CellCLAT: Preserving Topology and Trimming Redundancy in Self-Supervised Cellular Contrastive Learning E(n) Equivariant Topological Neural Networks

Reference 4

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CellCLAT: Preserving Topology and Trimming Redundancy in Self-Supervised Cellular Contrastive Learning Unresolved cited work

Reference 5

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This paper cites an unresolved cited work.

CellCLAT: Preserving Topology and Trimming Redundancy in Self-Supervised Cellular Contrastive Learning Unresolved cited work

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This paper cites an unresolved cited work.

CellCLAT: Preserving Topology and Trimming Redundancy in Self-Supervised Cellular Contrastive Learning Unresolved cited work

Reference 7

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This paper cites CliquePH: Higher-Order Information for Graph Neural Networks through Persistent Homology on Clique Graphs.

CellCLAT: Preserving Topology and Trimming Redundancy in Self-Supervised Cellular Contrastive Learning CliquePH: Higher-Order Information for Graph Neural Networks through Persistent Homology on Clique Graphs

Reference 8

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This paper cites an unresolved cited work.

CellCLAT: Preserving Topology and Trimming Redundancy in Self-Supervised Cellular Contrastive Learning Unresolved cited work

Reference 9

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This paper cites an unresolved cited work.

CellCLAT: Preserving Topology and Trimming Redundancy in Self-Supervised Cellular Contrastive Learning Unresolved cited work

Reference 10

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Observation 79d57fcf-7f67-4565-9685-cf2a2ff8c313 · outbound

This paper cites Simplicial Neural Networks.

CellCLAT: Preserving Topology and Trimming Redundancy in Self-Supervised Cellular Contrastive Learning Simplicial Neural Networks

Reference 11

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CellCLAT: Preserving Topology and Trimming Redundancy in Self-Supervised Cellular Contrastive Learning Unresolved cited work

Reference 12

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This paper cites Topological Blindspots: Understanding and Extending Topological Deep Learning Through the Lens of Expressivity.

CellCLAT: Preserving Topology and Trimming Redundancy in Self-Supervised Cellular Contrastive Learning Topological Blindspots: Understanding and Extending Topological Deep Learning Through the Lens of Expressivity

Reference 13

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This paper cites an unresolved cited work.

CellCLAT: Preserving Topology and Trimming Redundancy in Self-Supervised Cellular Contrastive Learning Unresolved cited work

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CellCLAT: Preserving Topology and Trimming Redundancy in Self-Supervised Cellular Contrastive Learning Unresolved cited work

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This paper cites an unresolved cited work.

CellCLAT: Preserving Topology and Trimming Redundancy in Self-Supervised Cellular Contrastive Learning Unresolved cited work

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This paper cites Simplicial Attention Neural Networks.

CellCLAT: Preserving Topology and Trimming Redundancy in Self-Supervised Cellular Contrastive Learning Simplicial Attention Neural Networks

Reference 17

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This paper cites an unresolved cited work.

CellCLAT: Preserving Topology and Trimming Redundancy in Self-Supervised Cellular Contrastive Learning Unresolved cited work

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This paper cites CIN++: Enhancing Topological Message Passing.

CellCLAT: Preserving Topology and Trimming Redundancy in Self-Supervised Cellular Contrastive Learning CIN++: Enhancing Topological Message Passing

Reference 19

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This paper cites Simplicial Attention Networks.

CellCLAT: Preserving Topology and Trimming Redundancy in Self-Supervised Cellular Contrastive Learning Simplicial Attention Networks

Reference 20

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CellCLAT: Preserving Topology and Trimming Redundancy in Self-Supervised Cellular Contrastive Learning Unresolved cited work

Reference 21

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This paper cites Cell Complex Neural Networks.

CellCLAT: Preserving Topology and Trimming Redundancy in Self-Supervised Cellular Contrastive Learning Cell Complex Neural Networks

Reference 22

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This paper cites Topological Deep Learning: Going Beyond Graph Data.

CellCLAT: Preserving Topology and Trimming Redundancy in Self-Supervised Cellular Contrastive Learning Topological Deep Learning: Going Beyond Graph Data

Reference 24

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CellCLAT: Preserving Topology and Trimming Redundancy in Self-Supervised Cellular Contrastive Learning Unresolved cited work

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This paper cites Topological Graph Neural Networks.

CellCLAT: Preserving Topology and Trimming Redundancy in Self-Supervised Cellular Contrastive Learning Topological Graph Neural Networks

Reference 26

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This paper cites Categorical Reparameterization with Gumbel-Softmax.

CellCLAT: Preserving Topology and Trimming Redundancy in Self-Supervised Cellular Contrastive Learning Categorical Reparameterization with Gumbel-Softmax

Reference 27

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CellCLAT: Preserving Topology and Trimming Redundancy in Self-Supervised Cellular Contrastive Learning Unresolved cited work

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This paper cites SGAT: Simplicial Graph Attention Network.

CellCLAT: Preserving Topology and Trimming Redundancy in Self-Supervised Cellular Contrastive Learning SGAT: Simplicial Graph Attention Network

Reference 29

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CellCLAT: Preserving Topology and Trimming Redundancy in Self-Supervised Cellular Contrastive Learning Unresolved cited work

Reference 30

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This paper cites In Proceedings of the AAAI Conference on Artificial Intelligence, Vol.

CellCLAT: Preserving Topology and Trimming Redundancy in Self-Supervised Cellular Contrastive Learning In Proceedings of the AAAI Conference on Artificial Intelligence, Vol

Reference 31

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CellCLAT: Preserving Topology and Trimming Redundancy in Self-Supervised Cellular Contrastive Learning DARTS: Differentiable Architecture Search

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CellCLAT: Preserving Topology and Trimming Redundancy in Self-Supervised Cellular Contrastive Learning Unresolved cited work

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CellCLAT: Preserving Topology and Trimming Redundancy in Self-Supervised Cellular Contrastive Learning Unresolved cited work

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CellCLAT: Preserving Topology and Trimming Redundancy in Self-Supervised Cellular Contrastive Learning Unresolved cited work

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CellCLAT: Preserving Topology and Trimming Redundancy in Self-Supervised Cellular Contrastive Learning Unresolved cited work

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This paper cites The Concrete Distribution: A Continuous Relaxation of Discrete Random Variables.

CellCLAT: Preserving Topology and Trimming Redundancy in Self-Supervised Cellular Contrastive Learning The Concrete Distribution: A Continuous Relaxation of Discrete Random Variables

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CellCLAT: Preserving Topology and Trimming Redundancy in Self-Supervised Cellular Contrastive Learning graph2vec: Learning Distributed Representations of Graphs

Reference 38

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CellCLAT: Preserving Topology and Trimming Redundancy in Self-Supervised Cellular Contrastive Learning Unresolved cited work

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This paper cites TUDataset: A collection of benchmark datasets for learning with graphs.

CellCLAT: Preserving Topology and Trimming Redundancy in Self-Supervised Cellular Contrastive Learning TUDataset: A collection of benchmark datasets for learning with graphs

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CellCLAT: Preserving Topology and Trimming Redundancy in Self-Supervised Cellular Contrastive Learning Architectures of Topological Deep Learning: A Survey of Message-Passing Topological Neural Networks

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CellCLAT: Preserving Topology and Trimming Redundancy in Self-Supervised Cellular Contrastive Learning Unresolved cited work

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CellCLAT: Preserving Topology and Trimming Redundancy in Self-Supervised Cellular Contrastive Learning Unresolved cited work

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This paper cites arXiv preprint arXiv:2410.06530 (2024).

CellCLAT: Preserving Topology and Trimming Redundancy in Self-Supervised Cellular Contrastive Learning arXiv preprint arXiv:2410.06530 (2024)

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CellCLAT: Preserving Topology and Trimming Redundancy in Self-Supervised Cellular Contrastive Learning Unresolved cited work

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CellCLAT: Preserving Topology and Trimming Redundancy in Self-Supervised Cellular Contrastive Learning Unresolved cited work

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CellCLAT: Preserving Topology and Trimming Redundancy in Self-Supervised Cellular Contrastive Learning Unresolved cited work

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CellCLAT: Preserving Topology and Trimming Redundancy in Self-Supervised Cellular Contrastive Learning Unresolved cited work

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CellCLAT: Preserving Topology and Trimming Redundancy in Self-Supervised Cellular Contrastive Learning Topological Neural Networks go Persistent, Equivariant, and Continuous

Reference 49

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CellCLAT: Preserving Topology and Trimming Redundancy in Self-Supervised Cellular Contrastive Learning Unresolved cited work

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CellCLAT: Preserving Topology and Trimming Redundancy in Self-Supervised Cellular Contrastive Learning Unresolved cited work

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CellCLAT: Preserving Topology and Trimming Redundancy in Self-Supervised Cellular Contrastive Learning Unresolved cited work

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CellCLAT: Preserving Topology and Trimming Redundancy in Self-Supervised Cellular Contrastive Learning Unresolved cited work

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CellCLAT: Preserving Topology and Trimming Redundancy in Self-Supervised Cellular Contrastive Learning Unresolved cited work

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CellCLAT: Preserving Topology and Trimming Redundancy in Self-Supervised Cellular Contrastive Learning Unresolved cited work

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CellCLAT: Preserving Topology and Trimming Redundancy in Self-Supervised Cellular Contrastive Learning Unresolved cited work

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CellCLAT: Preserving Topology and Trimming Redundancy in Self-Supervised Cellular Contrastive Learning Convolutional Learning on Simplicial Complexes

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CellCLAT: Preserving Topology and Trimming Redundancy in Self-Supervised Cellular Contrastive Learning Unresolved cited work

Reference 58

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CellCLAT: Preserving Topology and Trimming Redundancy in Self-Supervised Cellular Contrastive Learning Unresolved cited work

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CellCLAT: Preserving Topology and Trimming Redundancy in Self-Supervised Cellular Contrastive Learning Unresolved cited work

Reference 60

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This paper cites an unresolved cited work.

CellCLAT: Preserving Topology and Trimming Redundancy in Self-Supervised Cellular Contrastive Learning Unresolved cited work

Reference 61

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CellCLAT: Preserving Topology and Trimming Redundancy in Self-Supervised Cellular Contrastive Learning Unresolved cited work

Reference 62

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CellCLAT: Preserving Topology and Trimming Redundancy in Self-Supervised Cellular Contrastive Learning Unresolved cited work

Reference 63

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This paper cites an unresolved cited work.

CellCLAT: Preserving Topology and Trimming Redundancy in Self-Supervised Cellular Contrastive Learning Unresolved cited work

Reference 64

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This paper cites an unresolved cited work.

CellCLAT: Preserving Topology and Trimming Redundancy in Self-Supervised Cellular Contrastive Learning Unresolved cited work

Reference 67

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Observation 61bede2c-366f-4751-be8c-5dc01f5936da · outbound

This paper cites At iteration𝑡 = 0, the initial features (or colours) of the 0-cells in CCNN are obtained directly from the node features of the graph.

CellCLAT: Preserving Topology and Trimming Redundancy in Self-Supervised Cellular Contrastive Learning At iteration𝑡 = 0, the initial features (or colours) of the 0-cells in CCNN are obtained directly from the node features of the graph

Reference 68

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Observation af6fee2d-59a1-4e3a-94ec-d59e7f234038 · outbound

This paper cites Assume that after𝑡 iterations the WL colour- ing a𝑡 is topological reduced to the CCNN colouring b𝑡 , i.e., a𝑡 ⪯ b𝑡.

CellCLAT: Preserving Topology and Trimming Redundancy in Self-Supervised Cellular Contrastive Learning Assume that after𝑡 iterations the WL colour- ing a𝑡 is topological reduced to the CCNN colouring b𝑡 , i.e., a𝑡 ⪯ b𝑡

Reference 69

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T13:46:00.596758Z

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Observation 28bc3010-1bf3-4cef-bb94-15a26a72eb8e · outbound

This paper cites Additionally, the variance remains relatively stable, suggesting the robustness of our approach across different training durations.

CellCLAT: Preserving Topology and Trimming Redundancy in Self-Supervised Cellular Contrastive Learning Additionally, the variance remains relatively stable, suggesting the robustness of our approach across different training durations

Reference 100

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T13:46:00.349668Z

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Observation 919a5af3-a906-4393-ae24-d700c912f5a7 · outbound

This paper cites In The world wide web conference.

CellCLAT: Preserving Topology and Trimming Redundancy in Self-Supervised Cellular Contrastive Learning In The world wide web conference

Reference 2019

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Observation a76e5697-ec04-41d6-a49a-4d97fd6d5bc0 · outbound

This paper cites In The Thirty-eighth An- nual Conference on Neural Information Processing Systems.

CellCLAT: Preserving Topology and Trimming Redundancy in Self-Supervised Cellular Contrastive Learning In The Thirty-eighth An- nual Conference on Neural Information Processing Systems

Reference 2024

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No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

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