Derives Fredholm criteria for isotypical components from a Simonenko principle.
problem Finding Fredholm conditions for isotypical components of invariant pseudodifferential operators.
method General Simonenko's local principle and equivariant local principle for restriction to isotypical components.
result Full proof of equivariant local principle and extension of results.
Proves bijection between smooth conformal immersions and immersions.
problem Finding conformal immersions of closed Riemannian surfaces.
method Reformulated using h-principle and proved bijection on path connected components. result Induces a bijection between smooth conformal immersions and immersions.
This research simplifies PCA model selection using MDL principle.
problem Choosing the right number of principal components in PCA.
method Reduces NML problems to lower-dimension problems and bounds PCA NML.
result Bound the NML of PCA by terms of the NML of linear regression.
This paper uses PCA for stock price prediction, reducing complexity and improving accuracy.
problem Predicting stock prices from past data using PCA.
method Dimensionality reduction through PCA to project noisy data onto a principle subspace.
result The method improves prediction accuracy and reduces risk.
Deeper neural networks learn lower frequency functions faster, according to a new principle.
problem Understanding why deeper learning is faster.
method Fourier analysis and filtering method to separate and analyze the frequency distribution of neural network outputs.
result Deeper hidden layers in neural networks bias towards lower frequency functions during training.
DNNs initially capture low-frequency components before high-frequency ones, a phenomenon called F-Principle.
problem Understanding why DNNs generalize well despite overfitting.
method Empirical study on real and synthetic datasets, focusing on frequency components captured by DNNs.
result DNNs capture dominant low-frequency components first, then high-frequency ones, a phenomenon called F-Principle.
Extends variational principle to tensor Banach spaces.
problem High-dimensional partial differential equations and minimization problems.
method Describes tensor product as disjoint connected components, each modeled as a Banach manifold.
result Extension of Dirac-Frenkel variational principle to topological tensor spaces.
Symplectic coordinates found on a Hitchin component for a hyperbolic surface.
problem Parametrizing the PSL3(R)-Hitchin component with canonical coordinates. method Proved global Darboux coordinates with half canonical Goldman coordinates.
result Global Darboux coordinates exist for the PSL3(R)-Hitchin component. Study the averaging principle for non-autonomous slow-fast systems and apply it to financial local stochastic volatility models.
problem Understanding the behavior of non-autonomous slow-fast systems of stochastic differential equations.
method Prove the averaging principle under specific conditions and apply it to a financial model.
result Prices of derivatives converge to those calculated using the limit model under a risk-neutral measure.
We propose a framework for training GANs on composed data, improving model modularity and interpretability.
problem Training GANs on complex, composed data.
method Composition/decomposition framework for adversarially training GANs on composed data.
result Improves modularity, extensibility, and interpretability of GANs.
We investigate the validity of the equivalence principle along paths in gravitational theories based on derivations of the tensor algebra over a differentiable manifold. We prove the existence of local bases, called normal, in which the components of the derivations vanish along arbitrary paths. All such bases are expl…
Study harmonic surfaces in 3D space, proving superposition principle.
problem Understanding harmonic surfaces in R3. method Using harmonic Enneper immersions and superposition principle.
result Minimal and maximal surfaces can be decomposed into harmonic components.
Research examines how Islamic banking principles spread among managers and scholars.
problem Diffusion of Islamic banking principles among managers and scholars.
method Literature review focusing on knowledge diffusion and Islamic banking governance principles.
result Emergence of common Islamic banking governance principles from diverse knowledge streams.
Proves Riemannian positive mass theorem with singularities.
problem Proves Riemannian positive mass theorem for specific types of singular manifolds.
method Uses initial data sets with a second fundamental form to transfer convexity between different singularity components.
result Proves the theorem for manifolds with some mean-concave components and others mean-convex.
Unified proof of Teichmüller components using robust submanifolds.
problem Existence of higher Teichmüller components in representation spaces.
method Introducing robust families of submanifolds for a linear Lie group.
result Unified short proof of Teichmüller components for specific groups.
Two derivations of PCA for distributional data.
problem PCA for datasets of distributions.
method Two derivations: variance maximization and reconstruction error minimization.
result Closed-form solution for distributional PCA.
A relation between interest rates and inflation is presented using a two component economic model and a simple general principle. Preliminary results indicate a remarkable similarity to classical economic theories, in particular that of Wicksell.
Machine learning combines data, model, and loss components.
problem Transforming human lives through ML applications.
method Combines data, model, and loss components in efficient implementations.
result Understanding ML as three components helps navigate its growing applications.
The paper analyzes cyclic Higgs bundles using elliptic systems and immersion properties.
problem Analyzing cyclic Higgs bundles and their associated immersions.
method Derive a maximum principle for elliptic systems and apply it to the Hitchin equation.
result Obtain bounds on extrinsic curvature and complete the picture for specific representations.
This paper reviews nonlinear ICA for disentangled representations in unsupervised learning.
problem Finding useful disentangled representations in unsupervised deep learning.
method Review of nonlinear ICA theory and algorithms for disentanglement.
result Nonlinear ICA can be shown to estimate useful disentangled representations.
The study classifies prolongations up to Engel homotopy based on their formal data.
problem Classifying prolongations up to Engel homotopy.
method Reduction to formal data and study of homotopy type.
result The classification problem reduces to formal data when the turning number is large enough.
Study geometric properties of log Calabi-Yau manifolds, focusing on Fano manifolds with smooth or two proportional components.
problem Geometric properties of log Calabi-Yau manifolds in two specific cases.
method Analysis of various geometric properties, focusing on Bochner principle, local triviality, polystability, and compactifiability of universal cover.
result The universal cover of X∖D is a Calabi-Yau manifold of infinite topological type when D has two components. Paper reformulates Kernel PCA as a convex optimization problem for semi-supervised learning.
problem Improving semi-supervised learning with limited labeled data.
method Reinterprets Kernel PCA as a convex optimization problem and proposes a new convex optimization problem for semi-supervised classification.
result Proposed convex optimization problem for semi-supervised learning performs well with few labeled data.
IMA addresses non-identifiability in nonlinear ICA by assuming orthogonal Jacobian columns.
problem Non-identifiability in nonlinear ICA.
method IMA assumes orthogonal Jacobian columns and extends to manifold settings.
result IMA circumvents non-identifiability issues and can be beneficial for higher-dimensional observations.
A coordinate-free proof of the Maximum Principle is provided in the specific case of an optimal control problem with fixed time. Our treatment heavily relies on a special notion of variation of curves that consist of a concatenation of integral curves of time-dependent vector fields with unit time component, and on the…
Method decomposes streaming data into sparse and low-rank components from compressive measurements.
problem Online decomposing compressive streaming data efficiently.
method Solves n-ℓ1 cluster-weighted minimization to decompose sparse and low-rank components. result Outperforms existing methods for numerical and video data.
We prove that the space of gauge equivalence classes of U(1)-invariant connections on some SU(2)-principle bundles over the 4-sphere S^4 is weakly homotopy equivalent to a component of the second loop space of the 2-sphere S^2.
Generative model identifies temporal count data components with regime-dependent contributions.
problem Modeling temporal count data with regime-dependent dynamics.
method Generative framework combining regime-adaptive dynamics with Poisson log-normal emissions.
result Established identifiability of the model and revealed co-variation patterns and regime shifts.
Independent component analysis (ICA) has become a standard data analysis technique applied to an array of problems in signal processing and machine learning. This tutorial provides an introduction to ICA based on linear algebra formulating an intuition for ICA from first principles. The goal of this tutorial is to prov…
New approach tackles nonidentifiability in nonlinear blind source separation.
problem Nonidentifiability in nonlinear blind source separation.
method Independent mechanism analysis, incorporating causal assumptions.
result Empirical and theoretical evidence shows improved identifiability.
A treatment in a neighborhood and at a point of the equivalence principle on the basis of derivations of the tensor algebra over a manifold is given. Necessary and sufficient conditions are given for the existence of local bases, called normal frames, in which the components of derivations vanish in a neighborhood or a…
The paper tackles efficient dimensionality reduction for time series data using stochastic optimization.
problem Estimating the principle component of stationary time series data with nonconvex and dependent data points.
method Proposes a variant of Oja's algorithm combined with downsampling to control bias in stochastic gradient.
result Proves asymptotic rate of convergence and near optimal sample complexity for the proposed algorithm.
Enhances motion data analysis using metric learning for DTW.
problem Improving classification accuracy in motion capture data analysis.
method Extends LMNN principle to DTW, treating component-wise dissimilarity values as features.
result Significantly enhances classification accuracy in motion capture data analysis.
CSD learns a common component for domain generalization, outperforming existing methods.
problem Training models to generalize across unseen domains.
method CSD decomposes the model into a common and specific component, discarding the latter.
result CSD outperforms state-of-the-art domain generalization methods.
Improves ANN performance by normalizing data with PCA and eigenvalue weighting.
problem Boosting ANN performance through data preprocessing.
method PCA followed by weighting principle components by eigenvalues.
result Significantly improves ANN performance in classification tasks.
We study singular stochastic control of a two dimensional stochastic differential equation, where the first component is linear with random and unbounded coefficients. We derive existence of an optimal relaxed control and necessary conditions for optimality in the form of a mixed relaxed-singular maximum principle in a…
Volatility dynamics of wavelet - filtered stock price time series is studied. Using the universal thresholding method of wavelet filtering and a principle of minimal linear autocorrelation of noise component we find that the quantitative characteristics of volatility dynamics of denoised series are noticeably different…
This research deconstructs GANs into formulation, generalization, and optimization components.
problem Improving the performance and stability of GANs.
method Proposes a perturbation view of GANs, introduces Cascade GANs, and develops principles for GAN generalization and optimization.
result Demonstrates a fundamental trade-off in GAN approximation and statistical errors, and proposes a new GAN architecture with zero minimax duality gap.
The existence of local bases in which the components of derivations of tensor algebras over a differentiable manifold vanish along paths is proved. The holonomicity of these bases is investigated. The obtained results are applied to the case of linear connections. Some relations with the equivalence principle are shown…
New framework for disentangling features from noisy data.
problem Disentangling identifiable features from noisy data.
method Structured Nonlinear Independent Component Analysis (SNICA).
result Identifiability holds even in the presence of noise of unknown distribution.
SDAMI enhances interpretable high-dimensional regression with sparse deep learning and footprint principle.
problem Personalized models for small samples and high-dimensional features with interpretability.
method Sparse Deep Additive Model with Interactions (SDAMI) combining sparsity-driven feature selection and deep subnetworks.
result SDAMI successfully identifies pure interactions with near-zero false positive rates.
New method for decomposing high-dimensional parametric domains using PCA and inverse projection.
problem Decomposing high-dimensional parametric domains efficiently.
method Iterative Principal Component Analysis (PCA) and inverse projection methods.
result The proposed method effectively reconstructs the original domain from lower-dimensional data.
This paper studies the combinatorial Yamabe flow on hyperbolic surfaces with boundary. It is proved by applying a variational principle that the length of boundary components is uniquely determined by the combinatorial conformal factor. The combinatorial Yamabe flow is a gradient flow of a concave function. The long ti…
Necessary and/or sufficient conditions are studied for the existence, uniqueness and holonomicity of bases in which on sufficiently general subsets of a differentiable manifold the components of derivations of the tensor algebra over it vanish. The linear connections and the equivalence principle are considered form th…
Combines OT and PCA for DR, preserving clusters.
problem Analyzing high-dimensional data with global dependencies.
method Optimal transport (OT) for minimizing reconstruction error, combined with PCA.
result Effective preservation of high-dimensional clusters in embeddings.
We present a unifying framework which reduces the construction of probabilistic component analysis techniques to a mere selection of the latent neighbourhood, thus providing an elegant and principled framework for creating novel component analysis models as well as constructing probabilistic equivalents of deterministi…
Universal geometry organizes heterotic moduli spaces.
problem Understanding the structure of heterotic compactifications.
method Fibering compactification data over moduli space and analyzing universal curvatures.
result Deformations are components of universal curvatures, incorporating α′2 corrections. Robust tensor ring completion improves tensor recovery accuracy and efficiency.
problem Tensor completion sensitivity to sparse components.
method Robust Tensor Ring Completion (RTRC) with weighted nuclear norms and l1 regularization.
result Exact recovery guarantees and superior performance in various tasks.