arXiv research

The Indian IT sector is strongly linked to global markets, while CG sector reflects domestic growth.

Any regular Gaussian probability distribution that can be represented by an AMP chain graph (CG) can be expressed as a system of linear equations with correlated errors whose structure depends on the CG. However, the CG represents the errors implicitly, as no nodes in the CG correspond to the errors. We propose in this…

Improved CG force-field learning from all-atom data.

PF-LaCG removes the need for knowing smoothness and strong convexity parameters for locally accelerated CG.

New algorithms improve direction finding using prior signal knowledge.

In this paper we introduce a parameter dependent class of Krylov-based methods, namely CD, for the solution of symmetric linear systems. We give evidence that in our proposal we generate sequences of conjugate directions, extending some properties of the standard Conjugate Gradient (CG) method, in order to preserve the…

New CGMD model predicts non-equilibrium processes better than existing methods.

In this paper, we deal with the problem of marginalization over and conditioning on two disjoint subsets of the node set of chain graphs (CGs) with the LWF Markov property. For this purpose, we define the class of chain mixed graphs (CMGs) with three types of edges and, for this class, provide a separation criterion un…

Novel CG-EGNNs learn equivariant functions from Clifford algebras.

New stochastic CG algorithm with variance reduction converges faster and is more efficient.

The paper analyzes and mitigates biases in scalable Gaussian Process methods.

The conjugate gradient (CG) method is an efficient iterative method for solving large-scale strongly convex quadratic programming (QP). In this paper we propose some generalized CG (GCG) methods for solving the $\ell_1$-regularized (possibly not strongly) convex QP that terminate at an optimal solution in a finite numb…

Defines connections on parahoric torsors for curves and proves their equivalence to polystability.

Graph neural network predicts optimal coarse-grained mapping operators.

A new machine-learned CG model predicts protein structures efficiently.

Advances data-driven coarse-graining for complex systems.

In this work, we consider Corporate Governance (CG) ties among companies from a multiple network perspective. Such a structure naturally arises from the close interrelation between the Shareholding Network (SH) and the Board of Directors network (BD). In order to capture the simultaneous effects of both networks on CG,…

Paper proposes a new method to find approximate SOSP for nonconvex constrained optimization problems.

Proposes a method to use causal graph knowledge for better predictive modeling.

CG-BGs combine flow-based models with PMFs to sample large systems efficiently.

CFA improves model's ability to generalize across unseen domain-class combinations.

We introduce a novel approach for estimating Latent Dirichlet Allocation (LDA) parameters from collapsed Gibbs samples (CGS), by leveraging the full conditional distributions over the latent variable assignments to efficiently average over multiple samples, for little more computational cost than drawing a single addit…

Robust CG methods avoid data corruption and solve structured statistical estimation problems.

Garside groupoids, as recently introduced by Krammer, generalise Garside groups. A weak Garside group is a group that is equivalent as a category to a Garside groupoid. We show that any periodic loop in a Garside groupoid $\CG$ may be viewed as a Garside element for a certain Garside structure on another Garside groupo…

For any triple $(W,L,ρ)$, where W is a closed connected and oriented 3-manifold, L is a link in W and $ρ$ is a flat principal B-bundle over W (B is the Borel subgroup of $SL(2,\mc)$), one constructs a $\Dd$-scissors congruence class $\cG_{\Dd}(W,L,ρ)$ which belongs to a (pre)-Bloch group $\Pp (\Dd)$. The class $\cG_{\D…

We discuss two sorts of generalization of Lie groupoids. One is Lie $n$-groupoids defined as simplicial manifolds with trivial $π_{k\geq n+1}$. The other is the stacky Lie groupoid $\cG\rra M$ with $\cG$ a differentiable stack. We build 1-1 correspondence between Lie 2-groupoids and stacky Lie groupoids up to a certain…

CG-EnKF and NS-EnKF outperform deep learning-based SF in data assimilation.

New method uses normalizing flows to improve force fields for coarse-grained molecular dynamics.

Proposes a new CG interpretation of neural networks for better theoretical analysis.

Efficient methods for Lévy models using SINH-regular processes.

We show how to construct measures on Banach manifolds associated to supersymmetric quantum field theories. These measures are mathematically well-defined objects inspired by the formal path integrals appearing in the physics literature on quantum field theory. We give three concrete examples of our construction. The fi…

The paper examines curvature and stability in quasi-geostrophic motions using spherical harmonics.

We discuss two generalizations of Lie groupoids. One consists of Lie $n$-groupoids defined as simplicial manifolds with trivial $π_{k\geq n+1}$. The other consists of stacky Lie groupoids $\cG\rra M$ with $\cG$ a differentiable stack. We build a 1-1 correspondence between Lie 2-groupoids and stacky Lie groupoids up to …

A new framework optimizes model transfer across domains with labeled data.

A new iterative K-FAC algorithm reduces training time and memory usage.

Streaming variational Bayes (SVB) is successful in learning LDA models in an online manner. However previous attempts toward developing online Monte-Carlo methods for LDA have little success, often by having much worse perplexity than their batch counterparts. We present a streaming Gibbs sampling (SGS) method, an onli…

Paper proposes a method to find approximate SOSP for nonconvex conic optimization problems.

New constructions from non-separating planar graphs improve understanding of graph linkability and knotability.

The paper analyzes Newton-like and inexact Newton methods for stochastic optimization problems.

New algorithm discovers causal relationships in complex data.

Conjugate gradient methods improve efficiency for high-dimensional GLMMs.

New method speeds up Gaussian process training and inference for large datasets.

Bayesian CG reduces PDEs with random coefficients to cheaper models.

We discuss the construction of finite noncommutative geometries on Hopf algebras and finite groups in the `quantum groups approach'. We apply the author's previous classification theorem, implying that calculi in the factorisable case correspond to blocks in the dual, to classify differential calculi on the quantum cod…

Researchers develop CG for deep learning constraints, improving model efficiency and accuracy.

New recommendations improve Gaussian process accuracy and stability.

Paper proposes a method to find approximate SOSP for nonconvex conic optimization problems.

Study finds maximal linklessly embeddable graphs up to 11 vertices and their complements.