On-device research index

arXiv research

A locally-built, LLM-digested index of recent arXiv papers in quant finance, geometry/topology, and statistical ML — keyword search served straight from SQLite on this machine.

168,695 papers · 148 categories

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13 results for “Discrete-Level”

Ordinality in Discrete-level Question Difficulty Estimation: Introducing Balanced DRPS and OrderedLogitNNcs.LG

This study introduces balanced DRPS and OrderedLogitNN for better QDE of discrete-level questions.

problem Lack of ordinal regression methods and fair evaluation metrics for discrete-level QDE.

method Introduces balanced DRPS and OrderedLogitNN, fine-tunes BERT on RACE++ and ARC datasets.

result OrderedLogitNN outperforms other models on complex QDE tasks.

Question Difficulty Estimation Ordinal Regression Discrete-Level Balanced DRPS OrderedLogitNN

2025-07-01abs ↗pdf ↗

Multilevel Geometric Optimization for Regularised Constrained Linear Inverse Problemsmath.OC

A multilevel optimization method for constrained problems.

problem Regularized constrained linear inverse problems with box constraints.

method Geometric multilevel optimization with varying discretization levels.

result Preserves feasibility of updates while speeding up computations.

multilevel optimization constrained problems box constraints multigrid methods Riemannian structure

2022-07-11abs ↗pdf ↗

Resurgence in complex Chern-Simons theory at generic levelshep-th

Study on Chern-Simons theory at generic levels, revealing universal resurgent structure.

problem Analyzing Chern-Simons theory at generic levels with small boundary holonomy.

method Examined resurgent structure of state integral models on knot complements with generic discrete level.

result Resurgent structure is universal, independent of the level

k

Chern-Simons theory resurgence generic levels knot complements Borel plane singularities

2022-08-30abs ↗pdf ↗

Discrete asymptotic nets and W-congruences in Plucker line geometrynlin.SI

The asymptotic lattices and their transformations are studied within the line geometry approach. It is shown that the discrete asymptotic nets are represented by isotropic congruences in the Plucker quadric. On the basis of the Lelieuvre-type representation of asymptotic lattices and of the discrete analog of the Mouta…

1999-09-16abs ↗pdf ↗

Stochastic Deep Learning in Memristive Networksstat.ML

We study the performance of stochastically trained deep neural networks (DNNs) whose synaptic weights are implemented using emerging memristive devices that exhibit limited dynamic range, resolution, and variability in their programming characteristics. We show that a key device parameter to optimize the learning effic…

2017-11-09abs ↗pdf ↗

Mimetic framework on curvilinear quadrilaterals of arbitrary ordermath.NA

In this paper higher order mimetic discretizations are introduced which are firmly rooted in the geometry in which the variables are defined. The paper shows how basic constructs in differential geometry have a discrete counterpart in algebraic topology. Generic maps which switch between the continuous differential for…

2011-11-18abs ↗pdf ↗

Ninomiya-Victoir scheme: strong convergence, antithetic version and application to multilevel estimatorsq-fin.CP

In this paper, we are interested in the strong convergence properties of the Ninomiya-Victoir scheme which is known to exhibit weak convergence with order 2. We prove strong convergence with order $1/2$ . This study is aimed at analysing the use of this scheme either at each level or only at the finest level of a multil…

2015-08-26abs ↗pdf ↗

Discrete Lorentz surfaces and s-embeddings I: isothermic surfacesmath.DG

This paper answers a question about discrete embeddings to maximal surfaces.

problem The question of whether discrete embeddings lift to maximal surfaces.

method Introduced a correspondence between s-embeddings and congruences of touching Lorentz spheres, identified isothermic s-embeddings that lift to S-isothermic surfaces.

result Isothermic s-embeddings lift to S-isothermic surfaces, which are key for obtaining discrete maximal surfaces.

s-embeddings discrete maximal surfaces congruences of spheres isothermic surfaces Ising model

2024-10-15abs ↗pdf ↗

Discrete Lorentz surfaces and s-embeddings II: maximal surfacesmath.DG

Discrete maximal surfaces identified from s-embeddings.

problem Understanding the conformal invariance of the Ising model.

method Introduced a special class of isothermic s-embeddings that correspond to discrete S-maximal surfaces.

result Each S-maximal surface comes with a 1-parameter family of associated surfaces that are isometric.

Ising model s-embeddings maximal surfaces discrete geometry conformal invariance

2024-11-28abs ↗pdf ↗

Unbiased Kinetic Langevin Monte Carlo with Inexact Gradientsstat.CO

Unbiased method for Bayesian posterior means using kinetic Langevin dynamics.

problem Estimating Bayesian posterior means efficiently and accurately.

method Combines advanced splitting methods with enhanced gradient approximations in a multilevel Monte Carlo approach.

result The method achieves unbiased estimates with finite variance and central limit theorem properties.

Bayesian inference Monte Carlo methods Langevin dynamics gradient approximations multilevel Monte Carlo

2023-11-08abs ↗pdf ↗

Deep Visual Reasoning: Learning to Predict Action Sequences for Task and Motion Planning from an Initial Scene Imagecs.LG

Deep network predicts action sequences for complex tasks from a scene image.

problem Scalable task and motion planning from initial scene images.

method Deep convolutional recurrent neural network that predicts action sequences.

result Predicts promising action sequences, reducing motion planning problems.

deep learning task planning motion planning neural networks action sequences

2020-06-09abs ↗pdf ↗

Energy-Dissipative Evolutionary Deep Operator Neural Networksstat.ML

DeepONet learns operators for PDEs with varying parameters and initial conditions.

problem Learning operators for partial differential equations with different parameters or initial conditions.

method DeepONet uses a Branch net and Trunk net to minimize error between evaluated and expected outputs, incorporating a scalar auxiliary variable approach for energy dissipation.

result DeepONet can accurately approximate operators for PDEs with varying parameters or initial conditions.

DeepONet Operator Learning Partial Differential Equations Energy Dissipation Scalar Auxiliary Variable

2023-06-09abs ↗pdf ↗

An index formula for simple graphsmath.DG

Gauss-Bonnet for simple graphs G assures that the sum of curvatures K(x) over the vertex set V of G is the Euler characteristic X(G). Poincare-Hopf tells that for any injective function f on V the sum of i(f,x) is X(G). We also know that averaging the indices E[i(f,x)] over all functions gives curvature K(x). We explor…

2012-05-02abs ↗pdf ↗