{"total":13,"items":[{"citing_arxiv_id":"2606.07151","ref_index":18,"ref_count":1,"confidence":0.9,"is_internal_anchor":false,"paper_title":"Geodesics of Dynamic Graphs for Regime Change Detection","primary_cat":"cs.LG","submitted_at":"2026-06-05T11:08:22+00:00","verdict":"UNVERDICTED","verdict_confidence":"LOW","novelty_score":7.0,"formal_verification":"none","one_line_summary":"Models regimes in temporal graphs as geodesic trajectories and detects changes as drifts from estimated geodesics, outperforming baselines on synthetic data and showing better alignment with external events on COVID mobility data.","context_count":0,"top_context_role":null,"top_context_polarity":null,"context_text":null},{"citing_arxiv_id":"2606.06329","ref_index":6,"ref_count":1,"confidence":0.9,"is_internal_anchor":false,"paper_title":"Efficient Mean Curvature Computation on High-Dimensional Data Manifolds","primary_cat":"cs.LG","submitted_at":"2026-06-04T16:04:31+00:00","verdict":"UNVERDICTED","verdict_confidence":"LOW","novelty_score":7.0,"formal_verification":"none","one_line_summary":"An exact algebraic identity plus low-rank SVD and Haar-measure null-space approximation reduce per-point mean curvature cost from O(m^4) to O(k^2 m + k m p^2) with 50-300x speedups and negligible accuracy loss.","context_count":0,"top_context_role":null,"top_context_polarity":null,"context_text":null},{"citing_arxiv_id":"2605.23540","ref_index":50,"ref_count":1,"confidence":0.9,"is_internal_anchor":false,"paper_title":"When One Point Is Not Enough: Addressing Ambiguous Instances in Dimensionality Reduction by Splitting","primary_cat":"cs.LG","submitted_at":"2026-05-22T12:01:54+00:00","verdict":"UNVERDICTED","verdict_confidence":"LOW","novelty_score":6.0,"formal_verification":"none","one_line_summary":"A graph-based technique splits ambiguous instances into multiple points in DR projections to reduce partial neighborhood embedding and reveal hidden memberships.","context_count":0,"top_context_role":null,"top_context_polarity":null,"context_text":null},{"citing_arxiv_id":"2605.12116","ref_index":28,"ref_count":1,"confidence":0.9,"is_internal_anchor":false,"paper_title":"MPEX AI Digital Twins Milestone Report","primary_cat":"physics.plasm-ph","submitted_at":"2026-05-12T13:31:43+00:00","verdict":"UNVERDICTED","verdict_confidence":"LOW","novelty_score":1.0,"formal_verification":"none","one_line_summary":"The MPEX AI Digital Twins project reports that its two phase-I AI milestones for hot-spot control and damage assessment are on track for June 2026 demonstration.","context_count":1,"top_context_role":"background","top_context_polarity":"support","context_text":"patches with similar surface morphology, such as crack patterns or texture features, to be mapped close to each other, even if their raw feature vectors differ significantly. From a broader perspective, this procedure can be interpreted as a form ofmanifold learning, where the goal is to uncover a low-dimensional representation that captures the intrinsic geometry of the data [28, 1, 18]. Unlike purely linear methods, nonlinear manifold learning techniques are able to represent complex variations in data that arise from underlying physical processes, such as crack formation and surface evolution. This is particularly important in the present setting, where the relationship between observed morphology and underlying material or exposure conditions is highly nonlinear."},{"citing_arxiv_id":"2605.08753","ref_index":58,"ref_count":1,"confidence":0.9,"is_internal_anchor":false,"paper_title":"Simultaneous Monitoring of Shape and Surface Color via 4D Point Clouds: A Registration-free Approach","primary_cat":"cs.CV","submitted_at":"2026-05-09T07:30:03+00:00","verdict":"UNVERDICTED","verdict_confidence":"LOW","novelty_score":6.0,"formal_verification":"none","one_line_summary":"SMAC detects shape deformations and color anomalies in 4D point clouds using Laplace-Beltrami spectral properties without registration or mesh reconstruction.","context_count":0,"top_context_role":null,"top_context_polarity":null,"context_text":null},{"citing_arxiv_id":"2604.09419","ref_index":21,"ref_count":1,"confidence":0.9,"is_internal_anchor":false,"paper_title":"NOMAD: Generating Embeddings for Massive Distributed Graphs","primary_cat":"cs.LG","submitted_at":"2026-04-10T15:30:43+00:00","verdict":"UNVERDICTED","verdict_confidence":"LOW","novelty_score":5.0,"formal_verification":"none","one_line_summary":"NOMAD delivers an MPI-based distributed implementation of graph embedding models achieving 10-100x median speedups over multi-threaded baselines and 35-76x over prior distributed systems on large clusters.","context_count":1,"top_context_role":"background","top_context_polarity":"background","context_text":"geometric framework for nonlinear dimensionality reduction,\"science, vol. 290, no. 5500, pp. 2319-2323, 2000. [Online]. Available: https://doi.org/10.1126/science.290.5500.2319 [20] M. Belkin and P. Niyogi, \"Laplacian eigenmaps for dimensionality reduction and data representation,\"Neural Comput., vol. 15, no. 6, pp. 1373-1396, 2003. [Online]. Available: https://doi.org/10.1162/089976 603321780317 [21] M. A. Cox and T. F. Cox, \"Multidimensional scaling,\" inHandbook of data visualization. Springer, 2008, pp. 315-347. [Online]. Available: https://doi.org/10.1007/978-3-540-33037-0 14 [22] N. Pezzotti, T. H ¨ollt, B. Lelieveldt, E. Eisemann, and A. Vilanova, \"Hierarchical stochastic neighbor embedding,\" inComputer graphics forum, vol. 35, no. 3. Wiley Online Library, 2016, pp."},{"citing_arxiv_id":"2604.03456","ref_index":65,"ref_count":1,"confidence":0.9,"is_internal_anchor":false,"paper_title":"Earth Embeddings Reveal Diverse Urban Signals from Space","primary_cat":"cs.LG","submitted_at":"2026-04-03T20:58:37+00:00","verdict":"UNVERDICTED","verdict_confidence":"LOW","novelty_score":5.0,"formal_verification":"none","one_line_summary":"Earth embeddings from satellite images predict neighborhood-level urban indicators with higher accuracy for built-environment outcomes than for behavior-driven ones, showing city-specific variation but year-to-year stability.","context_count":1,"top_context_role":"method","top_context_polarity":"use_method","context_text":"Kernel PCA (KPCA). A non-linear extension of PCA that uses kernel functions (specifically the Radial Basis Function) to project data into a higher-dimensional space where it is linearly separable before reduction[64]. Isomap. A manifold learning technique that preserves the intrinsic geometric structure of the data by maintaining geodesic distances between points[65]. Random Projection. A computationally efficient method based on the Johnson-Lindenstrauss lemma, which preserves pairwise distances by projecting the data onto a lower-dimensional subspace using a random Gaussian matrix[66]. All reduction methods were implemented using the scikit-learn framework in Python, with parameters fitted globally across all metropolitan areas to ensure consistent feature representation"},{"citing_arxiv_id":"2604.02535","ref_index":67,"ref_count":1,"confidence":0.9,"is_internal_anchor":false,"paper_title":"A Spectral Framework for Multi-Scale Nonlinear Dimensionality Reduction","primary_cat":"cs.LG","submitted_at":"2026-04-02T21:39:52+00:00","verdict":"UNVERDICTED","verdict_confidence":"LOW","novelty_score":6.0,"formal_verification":"none","one_line_summary":"A spectral framework for nonlinear DR uses spectral bases plus cross-entropy optimization to create multi-scale embeddings that preserve both global manifold geometry and local neighborhoods while supporting graph-frequency analysis.","context_count":1,"top_context_role":"background","top_context_polarity":"background","context_text":"https://cole-trapnell-lab. github.io/monocle3/docs/trajectories/, 2022. Accessed: 2025-09-07. 5, 8 [66] C. Trapnell, D. Cacchiarelli, J. Grimsby, P. Pokharel, S. Li, M. Morse et al. The dynamics and regulators of cell fate decisions are revealed by pseudotemporal ordering of single cells.Nat. Biotechnol., 32(4):381-386, 2014. doi: 10.1038/nbt.2859 6 [67] A. Tsitsulin, D. Mottin, P. Karras, A. Bronstein, and E. Müller. NetLSD: Hearing the shape of a graph. InProc. KDD, pp. 2347-2356, 2018. doi: 10.1145/3219819.3219991 3 [68] L. van der Maaten and G. Hinton. Visualizing data using t-SNE.J. Mach. Learn. Res., 9(11), 2008. https://www.jmlr.org/papers/volume9/ vandermaaten08a/vandermaaten08a.pdf. 1, 2"},{"citing_arxiv_id":"2601.20173","ref_index":76,"ref_count":1,"confidence":0.9,"is_internal_anchor":false,"paper_title":"MAPLE: Self-Supervised Learning-Enhanced Nonlinear Dimensionality Reduction for Visual Analysis","primary_cat":"cs.LG","submitted_at":"2026-01-28T02:14:17+00:00","verdict":"UNVERDICTED","verdict_confidence":"LOW","novelty_score":6.0,"formal_verification":"none","one_line_summary":"MAPLE enhances UMAP via self-supervised MMCRs to untangle complex manifolds, yielding clearer clusters and finer subclusters than standard UMAP at similar cost.","context_count":0,"top_context_role":null,"top_context_polarity":null,"context_text":null},{"citing_arxiv_id":"2511.12845","ref_index":4,"ref_count":1,"confidence":0.9,"is_internal_anchor":false,"paper_title":"Manifold Learning for Source Separation in Confusion-Limited Gravitational-Wave Data","primary_cat":"physics.gen-ph","submitted_at":"2025-11-17T00:27:42+00:00","verdict":null,"verdict_confidence":null,"novelty_score":null,"formal_verification":null,"one_line_summary":null,"context_count":0,"top_context_role":null,"top_context_polarity":null,"context_text":null},{"citing_arxiv_id":"2506.20928","ref_index":21,"ref_count":1,"confidence":0.9,"is_internal_anchor":false,"paper_title":"Active Learning for Manifold Gaussian Process Regression","primary_cat":"stat.ML","submitted_at":"2025-06-26T01:25:39+00:00","verdict":"UNVERDICTED","verdict_confidence":"LOW","novelty_score":4.0,"formal_verification":"none","one_line_summary":"A joint optimization of neural manifold learning and active-learning-guided Gaussian process regression in latent space outperforms random sampling on synthetic data for complex functions.","context_count":0,"top_context_role":null,"top_context_polarity":null,"context_text":null},{"citing_arxiv_id":"2407.12208","ref_index":77,"ref_count":1,"confidence":0.9,"is_internal_anchor":false,"paper_title":"Computing k-means in mixed precision","primary_cat":"math.NA","submitted_at":"2024-07-16T22:48:35+00:00","verdict":null,"verdict_confidence":null,"novelty_score":null,"formal_verification":null,"one_line_summary":null,"context_count":0,"top_context_role":null,"top_context_polarity":null,"context_text":null},{"citing_arxiv_id":"2403.14922","ref_index":28,"ref_count":1,"confidence":0.9,"is_internal_anchor":false,"paper_title":"CODA: A Continuous Online Evolve Framework for Deploying HAR Sensing Systems","primary_cat":"cs.LG","submitted_at":"2024-03-22T02:50:42+00:00","verdict":"UNVERDICTED","verdict_confidence":"LOW","novelty_score":6.0,"formal_verification":"none","one_line_summary":"CODA enables continuous online adaptation for HAR sensing by cache-based selective assimilation of informative instances and adaptive temporal retention to forget obsolete data under non-stationary drift.","context_count":0,"top_context_role":null,"top_context_polarity":null,"context_text":null}],"limit":50,"offset":0}