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Topology

41 papers

Papers per year

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Papers

TOPOGRAPH: Topology-Preserving Graph Reduction with Adaptive Structure for Persistent Homology AAAI 2026
IsUMap: Manifold Learning and Data Visualization Leveraging Vietoris-Rips Filtrations AAAI 2025
CuMPerLay: Learning Cubical Multiparameter Persistence Vectorizations ICCV 2025
Diffeomorphic interpolation for efficient persistence-based topological optimization NIPS 2024
Neural Persistence Dynamics NIPS 2024
Wasserstein convergence of Cech persistence diagrams for samplings of submanifolds NIPS 2024
Graphcode: Learning from multiparameter persistent homology using graph neural networks NIPS 2024
On the Limitations of Fractal Dimension as a Measure of Generalization NIPS 2024
Persistence Homology Distillation for Semi-supervised Continual Learning NIPS 2024
Homology Consistency Constrained Efficient Tuning for Vision-Language Models NIPS 2024
Learning Persistent Community Structures in Dynamic Networks via Topological Data Analysis AAAI 2024
Dynamic Tangled Derivative Logic of Metric Spaces AAAI 2024
Time-Aware Knowledge Representations of Dynamic Objects with Multidimensional Persistence AAAI 2024
The Shape of Word Embeddings: Quantifying Non-Isometry with Topological Data Analysis EMNLP 2024
Stable and Consistent Density-Based Clustering via Multiparameter Persistence JMLR 2024
A Framework for Fast and Stable Representations of Multiparameter Persistent Homology Decompositions NIPS 2023
Convolutional Persistence as a Remedy to Neural Model Analysis AISTATS 2023
Topological Data Analysis for Speech Processing INTERSPEECH 2023
Outlier-Robust Subsampling Techniques for Persistent Homology JMLR 2023
Limits of Dense Simplicial Complexes JMLR 2023
Filtration-Enhanced Graph Transformation IJCAI 2022
Topologically penalized regression on manifolds JMLR 2022
A Computationally Efficient Framework for Vector Representation of Persistence Diagrams JMLR 2022
The decomposition of the higher-order homology embedding constructed from the $k$-Laplacian NIPS 2021
Topology Distance: A Topology-Based Approach for Evaluating Generative Adversarial Networks AAAI 2021