{"total":25,"rows":[{"id":"2111.08366","title":"Multi-Vector Models with Textual Guidance for Fine-Grained Scientific Document Similarity","created":"2021-11-16","cited_by_count":35,"area":null},{"id":"2111.15139","title":"Breaking the Linear Iteration Cost Barrier for Some Well-known Conditional Gradient Methods Using MaxIP Data-structures","created":"2021-11-30","cited_by_count":19,"area":null},{"id":"2102.12833","title":"Diffusion Earth Mover's Distance and Distribution Embeddings","created":"2021-02-25","cited_by_count":18,"area":null},{"id":"2304.04250","title":"Editable User Profiles for Controllable Text Recommendation","created":"2023-04-09","cited_by_count":16,"area":null},{"id":"2105.07623","title":"Sentence Similarity Based on Contexts","created":"2021-05-17","cited_by_count":12,"area":null},{"id":"2107.06876","title":"Scalable Optimal Transport in High Dimensions for Graph Distances, Embedding Alignment, and More","created":"2021-07-14","cited_by_count":11,"area":null},{"id":"2409.17687","title":"Graph Edit Distance with General Costs Using Neural Set Divergence","created":"2024-09-26","cited_by_count":11,"area":null},{"id":"2101.11520","title":"Supervised Tree-Wasserstein Distance","created":"2021-01-27","cited_by_count":10,"area":null},{"id":"2105.14403","title":"Re-evaluating Word Mover's Distance","created":"2021-05-30","cited_by_count":10,"area":null},{"id":"2109.03431","title":"Fixed Support Tree-Sliced Wasserstein Barycenter","created":"2021-09-08","cited_by_count":10,"area":null},{"id":"2206.12116","title":"Approximating 1-Wasserstein Distance with Trees","created":"2022-06-24","cited_by_count":9,"area":null},{"id":"2405.03664","title":"A New Robust Partial $p$-Wasserstein-Based Metric for Comparing Distributions","created":"2024-05-06","cited_by_count":7,"area":null},{"id":"2307.01770","title":"Fast Optimal Transport through Sliced Wasserstein Generalized Geodesics","created":"2023-07-04","cited_by_count":5,"area":null},{"id":"2410.21107","title":"Tree-Wasserstein Distance for High Dimensional Data with a Latent Feature Hierarchy","created":"2024-10-28","cited_by_count":5,"area":null},{"id":"2104.00925","title":"Landmarks Augmentation with Manifold-Barycentric Oversampling","created":"2021-04-02","cited_by_count":4,"area":null},{"id":"2107.12334","title":"Embedding Signals on Knowledge Graphs with Unbalanced Diffusion Earth Mover's Distance","created":"2021-07-26","cited_by_count":4,"area":null},{"id":"2308.00273","title":"Neural approximation of Wasserstein distance via a universal architecture for symmetric and factorwise group invariant functions","created":"2023-08-01","cited_by_count":4,"area":null},{"id":"2210.12288","title":"Learning Ultrametric Trees for Optimal Transport Regression","created":"2022-10-21","cited_by_count":3,"area":null},{"id":"2401.07378","title":"Efficient approximation of Earth Mover's Distance Based on Nearest Neighbor Search","created":"2024-01-14","cited_by_count":3,"area":null},{"id":"2410.03052","title":"Learning Structured Representations by Embedding Class Hierarchy with Fast Optimal Transport","created":"2024-10-04","cited_by_count":3,"area":null},{"id":"2210.13630","title":"Budget-Constrained Bounds for Mini-Batch Estimation of Optimal Transport","created":"2022-10-24","cited_by_count":1,"area":null},{"id":"2111.10452","title":"MURAL: An Unsupervised Random Forest-Based Embedding for Electronic Health Record Data","created":"2021-11-19","cited_by_count":0,"area":null},{"id":"2310.10143","title":"An Empirical Study of Self-supervised Learning with Wasserstein Distance","created":"2023-10-16","cited_by_count":0,"area":null},{"id":"2402.14489","title":"A Class of Topological Pseudodistances for Fast Comparison of Persistence Diagrams","created":"2024-02-22","cited_by_count":0,"area":null},{"id":"2609.01528","title":"Sierpi\\'nski--Knopp Wasserstein Distance for Persistence Diagrams and Applications to 2-Wasserstein Approximation","created":"2026-09-01","cited_by_count":0,"area":null}],"next_cursor":null}