arXiv Machine Learning

Measuring Distortion in the Empty Regions of Dimensionality Reduction Scatterplots with the Gap Index

arXiv:2607. 28324v1 Announce Type: new Abstract: Quality metrics play a crucial role in the proper use of dimensionality reduction projections for visual analysis of high-dimensional data.

arXiv Machine Learning

Jul 30

When One Point Is Not Enough: Addressing Ambiguous Instances in Dimensionality Reduction by Splitting

arXiv:2605. 23540v2 Announce Type: replace Abstract: Dimensionality Reduction (DR) methods are widely used to visualize high-dimensional data.

By Diede P. M. van der Hoorn, Alessio Arleo, Fernando V. Paulovich

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arXiv Machine Learning

Jul 1

Visualizing High-Dimensional Graph Embeddings via Informed Multi-View Projections

arXiv:2606. 31119v1 Announce Type: new Abstract: Graphs are commonly visualized in 2D, where humans readily interpret spatial relationships, yet such layouts often distort higher-dimensional structure.

By Ya Ji (Khoury College of Computer Sciences, Northeastern University, Seattle), Xuefeng Li (Khoury College of Computer Sciences, Northeastern University, Seattle), Timo Brand (School of Computation, Information and Technology, Technical University of Munich, Heilbronn, Germany), Jacob Miller (School of Computation, Information and Technology, Technical University of Munich, Heilbronn, Germany), Peng Zhang (Khoury College of Computer Sciences, Northeastern University, Seattle), Stephen Kobourov (School of Computation, Information and Technology, Technical University of Munich, Heilbronn, Germany), Yifan Hu (Khoury College of Computer Sciences, Northeastern University, Seattle)

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arXiv Machine Learning

Jul 13

Cluster and then Embed: A Modular Approach for Visualization

arXiv:2509. 03373v2 Announce Type: replace Abstract: Dimensionality reduction methods such as t-SNE and UMAP are popular methods for visualizing data with a potential (latent) clustered structure.

By Elizabeth Coda, Ery Arias-Castro, Gal Mishne

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arXiv Machine Learning

Jul 17

cGAP: Generalized Association Plots with HOMALS-Guided Heatmaps for Visualization of High-Dimensional Categorical Data

arXiv:2607. 15018v1 Announce Type: cross Abstract: High-dimensional categorical data arise in genetics, biomedicine, and the social sciences, yet visualization tools for such data remain far less developed than those for continuous variables.

By Chun-houh Chen, Shun-Chuan Chang, Chiun-How Kao, Yi-Ju Lee, Shang-Ying Shiu, Yin-Jing Tien, ShengLi Tzeng, Han-Ming Wu

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Hugging Face Trending Papers

Aug 10

Illusion or Integrity? Geometrical Consistency Metric for AIGC Video Quality Evaluation

Recently, AI-driven video generation has attracted considerable attention. This surge increases the demand for reliable video quality assessment (VQA) metrics to evaluate AI-generated content (AIGC) videos and guide model optimization.

diffusion benchmarks safety

arXiv AI

Jul 10

Dimensionality Reduction Meets Network Science: Sensemaking on UMAP's kNN Graph

arXiv:2607. 08746v1 Announce Type: cross Abstract: While UMAP is widely used for exploring high-dimensional data, typical workflows focus on its lower-dimensional embedding, largely overlooking the rich k-nearest-neighbor (kNN) graph that UMAP constructs internally.

By Duen Horng Chau, Donghao Ren, Fred Hohman, Dominik Moritz

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arXiv Machine Learning

Jun 30

Your Data Manifold is Secretly a Reward Model: Shell-LCC for Text-to-Video Generation

arXiv:2606. 30248v1 Announce Type: cross Abstract: Recent text-to-video (T2V) diffusion models rely heavily on auxiliary reward signals (e.

By Shihao Zhang, Yuguang Yan, Junzhe Zhang, Wei Zhao, Bohan Wang, Hanwang Zhang

diffusion reinforcement-learning fine-tuning

arXiv Machine Learning

Jun 9

MS-COOT: Comparing Morse-Smale Complexes with Co-Optimal Transport

arXiv:2606. 08258v1 Announce Type: cross Abstract: Understanding and comparing structures in scalar fields is a central challenge in scientific visualization, with applications ranging from feature analysis to temporal and structural comparison.

By Guangyu Meng, Mingzhe Li, Erin Wolf Chambers

arXiv AI

Jun 30

Automated Quality Assessment of Geospatial Vector Data: A GeoAI Approach using Spatial Representation Learning

arXiv:2606. 28390v1 Announce Type: cross Abstract: Geospatial vector data quality is a foundational research topic in GIS, yet classic rule-based quality assessment algorithms often struggle with diverse urban morphologies and massive data volumes.

By Hao Li, Chen Chu, Filip Biljecki, Cyrus Shahabi, Wenwen Li

agents diffusion benchmarks data-engineering

arXiv AI

Aug 11

Illusion or Integrity? Geometrical Consistency Metric for AIGC Video Quality Evaluation

arXiv:2608. 09594v1 Announce Type: cross Abstract: Recently, AI-driven video generation has attracted considerable attention.

By Yifei Xue, Yuanchen Fei, Hao Zhang, Chenzhi Nie, Tie ji, Yizhen Lao

diffusion benchmarks safety

arXiv AI

2d ago

HiCo-GS: Hierarchical Context Aggregation and Geometric Consistency for Octree Gaussian Splatting

arXiv:2608. 14136v1 Announce Type: cross Abstract: Octree-based anchor Gaussian Splatting has emerged as a scalable representation for city-scale novel view synthesis, where multi-level anchors adaptively capture scene content from coarse building structures to fine architectural details.

By Wei Zhang, Shengkai Yu, Shiqiang Gong, Qi Zhang, Qiang Li, Qi Wang

benchmarks

arXiv Machine Learning

Jul 1

Drop-In Perceptual Optimization for 3D Gaussian Splatting

arXiv:2603. 23297v2 Announce Type: replace-cross Abstract: Despite their output being ultimately consumed by human viewers, 3D Gaussian Splatting (3DGS) methods often rely on ad-hoc combinations of pixel-level losses, resulting in blurry renderings.

By Ezgi Ozyilkan, Zhiqi Chen, Oren Rippel, Jona Ball\'e, Kedar Tatwawadi

benchmarks

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