Survey explores translation surfaces from geometric and topological perspectives.
problem Underutilized aspects of translation surfaces in dynamical viewpoint.
method Equivalent definitions, moduli spaces, period map, holonomy map.
result Highlights foundational, yet underrated, aspects of translation surfaces theory.
Three perspectives on quantizing magnetic Poisson structures are explored.
problem Quantization of magnetic Poisson structures with non-associativity.
method Deformation quantization, symplectic realization, geometric quantization using bundle gerbes.
result Comparison and contrast of different quantization approaches.
Differential geometry applied to Mukai duality on K3 surfaces.
problem Understanding Mukai duality on K3 surfaces.
method Differential geometric approach.
result New insights into Mukai duality on K3 surfaces.
Geometric perspective on unique solution in matrix completion with a deterministic pattern.
problem Identifying unique solutions in matrix completion with a specific pattern of observed entries.
method Geometric and algebraic analysis, focusing on the well-posedness condition and local stability.
result A sufficient condition for local uniqueness of matrix completion solutions, called the well-posedness condition.
Researchers use LLMs to judge other LLMs, but this study provides a new geometric perspective to understand when it works.
problem The challenge of evaluating LLMs using other LLMs as judges, considering both aleatoric and epistemic uncertainties.
method A geometric perspective on ranking LLM candidates using probability simplices, analyzing conditions for identifiable rankings and designing Bayesian priors.
result Geometric analysis reveals that rankings based on LLM judges are robust in many but not all datasets, emphasizing the importance of modeling epistemic uncertainty.
Expanding on previous work, this note generalizes geometric structures results.
problem Generalizing geometric structures results.
method Generalization to a class of geometric structures including integrable almost-complex structures.
result Main results generalized to a broader class of geometric structures.
Geometric approach improves functional outlier detection.
problem Detecting outliers in functional data sets.
method Developed a geometric perspective on functional manifold for outlier detection.
result Improved understanding and differentiation of outliers.
Unified model for image-to-image translation explained with new geometrical perspective.
problem Lack of solid theoretical interpretations for image-to-image translation models.
method Reformulated adversarial learning model from a geometrical perspective and extended generalization definition.
result Derived a condition to control the generalization capability of the model.
The paper analyzes diffusion condensation for data geometry and topology.
problem Understanding the geometry and topology of high-dimensional data.
method Time-inhomogeneous diffusion process with geometric, spectral, and topological analysis.
result The condensation process defines intrinsic condensation homology and ambient persistent homology.
Geometrically reformulates wave equation solving method.
problem Solving tensorial wave equations in spacetimes.
method Geometric formulation of the method of descent.
result Representation formula for tensorial wave equation.
Geometric approach improves reinforcement learning representation.
problem Improving reinforcement learning representation learning.
method Formal evidence through geometric properties of value functions.
result Optimizing value functions reduces to predicting adversarial value functions (AVFs).
We formalize geometrically the idea that the (de Donder) Hamiltonian formulation of a higher derivative Lagrangian field theory can be constructed understanding the latter as a first derivative theory subjected to constraints.
Survey explores geometric aspects of policy optimization in control systems.
problem Understanding the geometric relationships between control design and optimization.
method Geometric perspective on policy optimization, focusing on parameterization and topology.
result Implications of policy geometry on stability and performance of local search algorithms.
Combines topological and geometric approaches to data analysis.
problem Understanding when and how geometric objects intersect.
method Connects topological and geometric concepts of curvature.
result Reconceptualizes curvature and links it to hyperconvexity.
Two new algorithms solve high-dimensional optimization problems without gradients.
problem Optimizing complex, high-dimensional functions without gradient information.
method GradientLess Descent (GLD) algorithms that use evaluations at adaptively chosen inputs.
result Converges within an ε-ball of the optimum with a number of evaluations that is poly-logarithmic in dimensionality.
This paper uses a geometric approach to understand how normalization layers affect neural network optimization.
problem Understanding the effect of normalization layers on optimization in neural networks.
method Introduces a spherical framework to study optimization dynamics of neural networks with normalization layers from a geometric perspective.
result Derives the first effective learning rate expression of Adam and shows that SGD with NLs is equivalent to a constrained variant of Adam.
Unified theory for adaptive image convolutions using metric perspectives.
problem Fixed kernels in convolutions limit adaptability in image processing.
method Metric perspective on images as 2D manifolds with local distances, proposing metric convolutions.
result Metric convolutions provide better generalisation and competitive performance.
Survey on moduli spaces of differentials from algebraic geometry perspective.
problem Understanding the topology of moduli spaces of differentials remains limited.
method Algebraic geometry perspective, connections to various fields.
result Many open problems and connections to other fields.
Machine learning models predict which ideas will be innovated based on subjective perspectives.
problem Predicting high-impact innovation based on subjective perspectives and interpersonal innovation opportunities.
method Quantifying subjective perspectives and their interaction based on innovator positions within a geometric space of concepts.
result Subjective perspectives predict which ideas individuals and groups will creatively attend to and successfully combine in the future.
Unified geometric perspectives on PDEs, torsion invariants, and moduli theory.
problem Index theory and analytic torsion of nonlinear PDEs.
method Microlocal sheaf theory, factorization algebras, Spencer hypercohomology.
result Unified geometric perspectives on PDEs, torsion invariants, and moduli theory.
New examples of Schoenflies balls are produced using a 5D approach.
problem Identifying Schoenflies balls that are not standard.
method Using a 5-dimensional perspective, algebraic and geometric handle cancellation.
result New examples of Schoenflies balls not known to be standard are produced.
Alternative finance models from physics for non-equilibrium systems.
problem Inequities of classical finance models in physics-based perspective.
method Physics-based insights for non-equilibrium finance models.
result Alternative models for non-equilibrium finance systems.
The paper defines and analyzes geometrically continuous splines for polygonal surfaces.
problem Defining and analyzing geometrically continuous splines for polygonal surfaces.
method General definition and analysis of geometrically continuous polygonal surfaces and spline functions.
result A comprehensive example and dimension formula for spline spaces of bounded degree on polygonal surfaces.
These are lecture notes from a series of lectures at the SMF summer school on "Geometric and Quantum Topology in Dimension 3", June 2014. The focus is on Heegaard Floer homology from the perspective of sutured Floer homology.
Certain solutions of a sextic sigma-model Lagrangian reminiscent of Skyrme model correspond to perfect fluids with stiff matter equation of state. We analyse from a differential geometric perspective this correspondence extended to general barotropic fluids.
This work revisits, from a geometric perspective, the notion of discrete connection on a principal bundle, introduced by M. Leok, J. Marsden and A. Weinstein. It provides precise definitions of discrete connection, discrete connection form and discrete horizontal lift and studies some of their basic properties and rela…
The paper connects complex normalizing flows to Kähler-Ricci flows using geometric and statistical perspectives.
problem Understanding the relationship between complex normalizing flows and Kähler-Ricci flows.
method Develops connections between complex normalizing flows and Kähler-Ricci flows by relating the log determinant to Ricci curvature and using a Bayesian perspective.
result Reconciles the complex normalizing flow and Kähler-Ricci flow, showing they are related under certain conditions.
We discuss from a geometric point of view the connection between the renormalization group flow for non--linear sigma models and the Ricci flow. This offers new perspectives in providing a geometrical landscape for 2D quantum field theories. In particular we argue that the structure of Ricci flow singularities suggests…
We review some recent results on the mean curvature flows of Lagrangian submanifolds from the perspective of geometric partial differential equations. These include global existence and convergence results, characterizations of first-time singularities, and constructions of self-similar solutions.
Study on directed graphs using Ricci curvature, extending previous undirected graph results.
problem Generalization of Ricci curvature for directed graphs.
method Introducing a new Ricci curvature for directed graphs using mean transition probability kernel.
result Several geometric and spectral properties of directed graphs under a lower Ricci curvature bound.
A new model for graph clustering using curvature spaces.
problem Graph clustering from a geometric perspective.
method Introducing a heterogeneous curvature space and a contrastive learning approach.
result CONGREGATE model outperforms state-of-the-art competitors.
New method proves length spectrum rigidity in various geometric settings.
problem Length spectrum rigidity in geometric settings.
method Combination of dynamical systems and geometric group theory.
result Provides concise proofs and extends classical results.
New geometric perspective for optimal learning on hexagonal structures.
problem Optimal learning process on hexagonal structures.
method Local trivial fibrations and Ceva's theorem.
result Learning can be defined on hexagonal structures.
A framework for computing holonomy groups of hybrid systems to achieve forward motion.
problem Achieving forward motion from periodic leg motion.
method Developing a framework for computing holonomy groups of hybrid systems.
result Computing holonomy groups of hybrid systems to achieve non-zero net motion.
New perspective on KCC theory for dynamical systems.
problem Geometric description of dynamical systems.
method Introducing a non-linear and Berwald type connection to describe dynamical systems geometrically.
result Established the relationship between linear and Jacobi stability for two-dimensional autonomous systems.
Surveying recent progress on flows of G2-structures on 7-manifolds.
problem Preserving metrics while modifying G2-structures on 7-manifolds. method Heat flows and other approaches in terms of 3-forms, octonions, vector fields, and geometric structures. result Comparison of different perspectives on G2-structure flows. Survey of algorithms for PCA and subspace tracking with missing data.
problem Handling missing data in streaming Principal Component Analysis and subspace tracking.
method Review of classical and recent algorithms with low computational and memory complexities.
result Algorithms need careful adjustment for missing data.
Paper proposes LCP for structural encodings, outperforming existing methods.
problem Improving Graph Neural Networks performance through effective structural encodings.
method Geometric perspective, Local Curvature Profiles (LCP) for structural encodings, combining with global positional encodings, comparing with rewiring techniques.
result LCP significantly outperforms existing structural encodings and combining LCP with global positional encodings improves performance.
Neural networks can model chaos efficiently by becoming geometrically chaotic.
problem Lack of theoretical understanding of how neural networks learn chaos.
method Employed a geometric perspective to show neural networks can model chaotic dynamics.
result Neural networks can reconstruct strange attractors and accurately predict local divergence rates.
Study explores unstable 3-forms on Calabi-Yau 3-folds.
problem Understanding degenerations of Calabi-Yau 3-folds via 3-forms.
method Investigates geometries of 3-forms on symplectic 6-manifolds.
result Unstable 3-forms reveal rich geometric properties related to SYZ conjecture.
Gradient clipping helps private SGD converge despite potential bias.
problem Gradient clipping in private SGD can bias convergence.
method Theoretical analysis and empirical evaluation of gradient clipping effects.
result Gradient clipping can prevent convergence to stationary points and introduces bias.
Geometrically proves WKB solutions of Schrödinger equations are resurgent.
problem Understanding resurgent behavior of WKB solutions on Riemann surfaces.
method Purely geometric approach using holomorphic Lie groupoids and spectral curves.
result Formal WKB solutions are Borel summable in almost all directions.
Investigates connections in Lie group bundles, focusing on geometric reduction.
problem Geometric reduction of gauge field theories.
method Definition and analysis of equivariant connections in Lie group bundles.
result Provides conditions for the existence and properties of equivariant connections.
Geometric framework explains deep learning performance.
problem Understanding why deep learning works well across various tasks.
method Comparing deep learning to quantum computations and diffeomorphic template matching.
result Geometric structures of different deep learning systems.
Bregman perspective on CART provides a unified framework for impurity measures.
problem Unifying impurity measures in CART
method Bregman divergence approach
result Unified framework for impurity measures
We give a different perspective on the (by now) classic Basmajian identity, and point out some related results, both in the setting of hyperbolic manifolds, and in the polyhedral setting \emph{without} any group acting. In the new version we give more geometric and combinatorial applications of the main ideas.
The paper explores complex geometries of 3-forms on symplectic 6-manifolds.
problem Understanding the geometry of 3-forms on symplectic 6-manifolds.
method Investigation of geometries associated with 3-forms of various orbital types.
result Rich geometric structures attached to unstable 3-forms from Calabi-Yau degeneration.
This work uses tropical geometry to understand neural network decision boundaries.
problem Characterizing neural network decision boundaries with piecewise linear activations.
method Tropical geometry applied to a simple neural network model.
result Decision boundaries are a subset of a tropical hypersurface related to a polytope formed by zonotopes.