LDReg addresses local dimensional collapse in self-supervised learning.
problem Local dimensional collapse in self-supervised learning representations.
method Local dimensionality regularization based on Fisher-Rao metric.
result LDReg improves representation quality and regularizes local and global dimensions.
Localized diffusion models reduce training complexity by exploiting low-dimensional structure.
problem Training diffusion models is computationally expensive due to the curse of dimensionality.
method Localized neural networks and localized score matching loss to estimate low-dimensional score functions.
result Localized diffusion models can circumvent the curse of dimensionality with reduced sample complexity.
LASE improves local network structure visualization by targeting locally low-dimensional regions.
problem Global spectral embedding fails to capture local geometric features in sparse, transitive networks.
method Local Adjacency Spectral Embedding (LASE) using weighted spectral decomposition.
result LASE reveals locally low-dimensional structure, improving local reconstruction and visualization.
dtSNE preserves local densities in low-dimensional embeddings.
problem Local density differences are not accurately preserved in tSNE and UMAP.
method dtSNE, which approximately conserves local densities.
result dtSNE provides more accurate local density depictions.
SDSPCAAN combines supervised and local data structures for better dimensionality reduction.
problem Preserving both global and local data structures for noisy high-dimensional data.
method Supervised discriminative sparse PCA with adaptive neighbors (SDSPCAAN).
result SDSPCAAN improves classification accuracy on high-dimensional datasets.
Proves local maximizers for higher Ekeland-Hofer capacities in 4D star-shaped domains.
problem Finding local maximizers for higher Ekeland-Hofer capacities in specific domains.
method Analogous to 4D local Viterbo conjecture, proving maximizers for rational ellipsoids.
result Local maximizers of the k-th Ekeland-Hofer capacities are symplectomorphic to rational ellipsoids.
We obtain structure results for locally conformally symplectic Lie algebras. We classify locally conformally symplectic structures on four-dimensional Lie algebras and construct locally conformally symplectic structures on compact quotients of all four-dimensional connected and simply connected solvable Lie groups.
Due to Janet-Cartan's theorem, any analytic Riemannian manifolds can be locally isometrically embedded into a sufficiently high dimensional Euclidean space. However, for an individual Riemannian manifold (M,g), it is in general hard to determine the least dimensional Euclidean space into which (M,g) can be locally isom…
Adaptive framework improves nonparametric dimensionality reduction.
problem Optimal hyper-parameter tuning for nonparametric dimensionality reduction.
method Adaptive framework using intrinsic dimension estimator and optimal local neighbourhood sizes.
result Significant improvements in various learning tasks through better low-dimensional visualizations.
New method controls error in low-dimensional marginals of spatial models.
problem Inaccurate approximation of low-dimensional marginals in spatial models.
method Stein's method with δ-locality condition for spatial models.
result Uniform error bound for marginals of approximate distributions.
Study local properties of homogeneous ANR-spaces, proving dimension full-valuedness.
problem Understand local structure of homogeneous ANR-spaces.
method Describe local structure and use it to prove dimension full-valuedness.
result Every finite-dimensional homogeneous metric ANR-compactum is dimensionally full-valued.
A manifold is locally \emph{k-fold symmetric}, if for any point and any k-dimensional vector subspace tangent to this point there exists a local isometry such that this point is a fixed point and the differential of the isometry restricted to that k-dimensional vector subspace is minus the identity. We show that …
New local ID estimators based on data separability.
problem Estimating intrinsic dimensionality locally in multi-dimensional data.
method Local estimators based on concentration of measure.
result Empirical comparison with other ID estimators.
We show several relations between local moves on 1-dimensional knots and those on high dimensional knots related by products of knots.
Survey of recent results on homogeneous finite-dimensional spaces.
problem Understanding properties of homogeneous finite-dimensional spaces.
method Discussion of recent results and unsolved problems.
result Discussion of recent results and unsolved problems.
Local Bayesian optimization shows strong performance and converges well, contrary to folklore.
problem Understanding the behavior and convergence of local Bayesian optimization methods.
method Studied the behavior of local optimization strategies and rigorously analyzed a specific algorithm.
result Local Bayesian optimization algorithms converge well and perform strongly, contrary to the folklore.
Topological manifolds can be embedded flatly in high-dimensional Euclidean space and are locally retracts.
problem Embedding and retraction of topological manifolds in Euclidean spaces.
method Locally flat embedding and retraction of manifolds in high-dimensional Euclidean space.
result Every topological n-manifold can be embedded locally flatly in R2n+1 and is a retract of some neighborhood in R2n+1. This study proves the local existence of a symplectic gradient flow on a flat torus.
problem Proving the local existence of a symplectic gradient flow on a flat torus.
method Using a moment map and a DeTurck trick to make the flow strictly parabolic and showing local existence and regularity.
result The group of symplectomorphisms of the real four-dimensional torus is locally contractible.
New method estimates intrinsic dimensionality using angles, not distances.
problem Estimating local intrinsic dimensionality accurately.
method Introduces a new estimator using the distribution of angles between neighbor points.
result New estimator behaves similarly but complementarily to existing measures of intrinsic dimensionality.
Gaussian process regression loses locality in high dimensions, affecting molecular energy surface fitting.
problem Loss of locality in high-dimensional Gaussian process regression.
method Analysis of Matern family kernels and multi-zeta basis functions.
result The property of locality disappears in high dimensions, impacting regression quality.
The purpose of the present paper is to study the globally and locally φ-T-symmetric (ε)-para Sasakian manifold in dimension 3. The globally φ-T-symmetric 3-dimensional (ε)-para Sasakian manifold is either Einstein manifold or h…
Geodesic completeness proven for all compact locally symmetric Lorentz manifolds.
problem Geodesic completeness of compact locally symmetric Lorentz manifolds.
method Proof in all remaining cases using completeness result.
result All compact, locally symmetric Lorentz manifolds are geodesically complete.
Local PBO methods improve preferential BO in high-dimensional problems.
problem Efficiently optimizing preferential BO in high-dimensional settings.
method Adapting high-dimensional BO techniques to preferential feedback.
result Local PBO methods reduce cumulative regret compared to global baselines.
Previous work (Pradines, 1966, Aof and Brown, 1992) has given a setting for a holonomy Lie groupoid of a locally Lie groupoid. Here we develop analogous 2-dimensional notions starting from a locally Lie crossed module of groupoids. This involves replacing the Ehresmann notion of a local smooth coadmissible section of a…
Electrostatic systems with specific tensors are locally conformally flat.
problem Understanding the geometry of electrostatic systems with special tensors.
method Proving local conformal flatness for electrostatic manifolds with divergence-free Bach tensor.
result Three-dimensional electrostatic manifolds with divergence-free Bach tensor are locally conformally flat.
FSL-Net detects and localizes feature shifts in large, high-dimensional datasets.
problem Feature shifts between data sources lead to erroneous features in various applications.
method FSL-Net is a neural network trained on multiple datasets to localize feature shifts.
result FSL-Net accurately localizes feature shifts from unseen datasets without re-training.
Proves non-properness set of 3D polynomial homeos can't be a line.
problem Non-properness set of 3D polynomial homeomorphisms.
method Proof by contradiction, using topological properties.
result Non-properness set cannot be homeomorphic to the real line.
We present local discriminative Gaussian (LDG) dimensionality reduction, a supervised dimensionality reduction technique for classification. The LDG objective function is an approximation to the leave-one-out training error of a local quadratic discriminant analysis classifier, and thus acts locally to each training po…
We obtain a topological and equivariant classification of closed, connected three-dimensional Alexandrov spaces admitting a local isometric circle action. We show, in particular, that such spaces are homeomorphic to connected sums of some closed 3-manifold with a local circle action and finitely many copies of the susp…
Study classifies LC Kahler structures on 4D solv Lie alg, with applications.
problem Classifying LC Kahler structures on 4D solvable Lie algebras.
method Investigation through linear equivalence and geometric interpretation.
result Produces many examples, including lcK structures on Oeljeklaus-Toma manifolds.
DeepGLO forecasts high-dimensional time series by combining global and local models.
problem Forecasting high-dimensional time series with global patterns and local calibration.
method Hybrid model combining global matrix factorization and local temporal networks.
result DeepGLO outperforms state-of-the-art approaches by more than 25% in WAPE.
Local gluing connects flow lines in finite time intervals.
problem Connecting flow lines in finite time intervals.
method Functional analytic approach to define local gluing map.
result Explicit construction of local gluing map in Euclidean case; intricate construction in non-Euclidean case.
We prove a local graphical theorem for two-dimensional self-shrinkers away from the origin. As applications, we study the asymptotic behavior of noncompact self-shrinkers with finite genus. Also, we show uniform boundedness on the second fundamental form of two-dimensional noncompact self-shrinkers with bounded mean cu…
In this paper, we use the powerful tool Milnor bases to classify all the 3−dimensional connected and locally symmetric Riemannian Lie Groups by solving system of polynomial equations of structure constants of each Lie algebra . Moreover, we showed that E0(2), is the only Lie group with locally symmetric left invar…
We show that the classical example X of a 3-dimensional generalized manifold constructed by van Kampen is another example of not homologically locally connected (i.e. not HLC) space. This space X is not locally homeomorphic to any of the compact metrizable 3-dimensional manifolds constructed in our earlier paper wh…
Yamabe flow proves compactness of certain locally conformally flat manifolds with positive Ricci curvature.
problem Proving compactness of locally conformally flat manifolds with positive Ricci curvature.
method Using the Yamabe flow to prove compactness.
result Locally conformally flat manifolds with positive pinched Ricci curvature are compact.
New method detects drift in high-dimensional data.
problem Understanding and localizing concept drift in learning systems.
method Conformal predictions for drift localization.
result Our approach outperforms existing methods on image datasets.
Book on infinite-dimensional Lie groups, covering basics and various classes.
problem Understanding Lie groups in infinite-dimensional spaces.
method Develops smooth manifolds and Lie groups in locally convex spaces, discussing various classes.
result Detailed exploration of infinite-dimensional Lie groups and their properties.
Unified treatment of gauge theories and Yang-Mills theory duality.
problem Unified treatment of gauge theories and Yang-Mills theory duality.
method Cohomological localization techniques and Atiyah-Singer index theorem.
result Unified framework and simplified derivations of localization formulas.
Deep neural networks can reduce high-dimensional data to lower dimensions.
problem Understanding the performance of deep neural networks in high-dimensional data.
method Applying deep neural networks to high-dimensional data and analyzing the least squares regression estimates.
result The least squares regression estimates using deep neural networks achieve dimensionality reduction when the regression function has locally low dimensionality, circumventing the curse of dimensionality.
FibeRed reduces complex data dimensions while preserving topology.
problem Hard embedding of topologically complex datasets in low-dimensional Euclidean space.
method Modeling datasets with vector bundles, reducing fibers while preserving topology.
result FibeRed learns topologically faithful embeddings in lower dimensions than existing methods.
Mercat preserves angles to create accurate low-dimensional embeddings.
problem Reconstructing global relationships in low-dimensional embeddings.
method Reconstructing angles between data points to preserve both local and global structures.
result Mercat yields good reconstruction across various experiments and metrics.
New parametrizations for minimal timelike surfaces discovered.
problem Finding parametrizations for minimal timelike surfaces in specific spaces.
method Derived representation formulas for null curves leading to parametrizations of minimal timelike surfaces.
result Examples of minimal timelike surfaces constructed.
Proposes a boundary detection method inspired by LLE for high-dimensional data.
problem Identifying boundary points from data on an embedded manifold.
method Inspired by locally linear embedding, uses nearest neighbor search schemes and spectral properties of local covariance matrix.
result Enhanced boundary detection in noisy data.
Bayesian optimisation tackles high-dimensional categorical and mixed search spaces.
problem Bayesian optimisation on high-dimensional categorical and mixed search spaces is challenging.
method Combining local optimisation with a tailored kernel design.
result Empirically outperforms current baselines in performance and computational costs.
An answer to the question: Can, in general, the adoption of a given symmetry induce a further symmetry, which might be hidden at a first level? has been attempted in the context of differential geometry of locally homogeneous spaces. Based on E. Cartan's theory of moving frames, a methodology for finding all symmetries…
New approach combines PCA and t-sne for better data analysis.
problem Multiscale complexity in high-dimensional data.
method Multiscale joint characterization using PCA and t-sne.
result Joint characterization detects signals not seen by PCA or t-sne alone.
Unique solutions found for diffusive martingale problems.
problem Finding unique solutions to Cauchy problems for diffusive real-valued strict local martingales.
method Provided sets of smooth functions under local Hölder and Engelbert-Schmidt conditions for unique classical and weak solutions.
result Unique solutions found for specific martingale models.