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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.

168,742 papers · 148 categories

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48 results for infilling sphere

InSphereNet uses infilling spheres for 3D object classification, improving accuracy with fewer parameters.

problem 3D object classification using points, voxels, or images.
method Constructs infilling spheres from signed distance field (SDF) for classification.
result InSphereNet achieves superior accuracy with fewer inputs and parameters.

Physical systems are modelled and investigated within simulation software in an increasing range of applications. In reality an investigation of the system is often performed by empirical test scenarios which are related to typical situations. Our aim is to derive a method which generates diverse test scenarios each re…

2018-10-04abs ↗pdf ↗

ICP improves text infilling and POS tagging with valid confidence sets.

problem Statistical reliability of machine learning predictions.
method Inductive conformal prediction algorithms for text infilling and POS tagging.
result Valid set-valued predictions with small size for real-world applications.

Recent years have seen remarkable progress of text generation in different contexts, such as the most common setting of generating text from scratch, and the emerging paradigm of retrieval-and-rewriting. Text infilling, which fills missing text portions of a sentence or paragraph, is also of numerous use in real life, …

2019-01-01abs ↗pdf ↗

Model infers mineral locations from geospatial data, improving predictions with auxiliary data.

problem Challenges in characterizing hidden mineral deposits underground.
method Generative modeling approach using masked and infilled geospatial maps.
result Models achieve Dice coefficients of 0.31 and recalls of 0.22 at 1×1 mi² resolution.

Unified perspective unites Bayesian optimization and active learning for efficient goal-oriented optimization.

problem Efficiently optimize expensive engineering and scientific problems with limited data.
method Unified framework linking Bayesian infill criteria and active learning criteria.
result Unified approach formalizes Bayesian infill criteria and active learning criteria.

A new method for high-dimensional Bayesian optimization.

problem Challenges in extending BO to high dimensions.
method Expected Coordinate Improvement (ECI) criterion for high-dimensional Bayesian optimization.
result Significantly better results than standard BO and competitive results with state-of-the-art methods.

Study improves accuracy of weather data for real-time building simulations.

problem Anomalous and missing weather data affect real-time building energy simulations.
method Introduces a framework for quality control of measured weather data using anomaly detection and neural network infilling.
result Neural Networks enhance the accuracy of data imputation compared to traditional methods.

We use SMC with twist functions to improve probabilistic inference in LLMs.

problem Improving probabilistic inference in large language models.
method We use Sequential Monte Carlo with learned twist functions to estimate expected future values and focus inference on promising sequences.
result Twisted SMC improves the accuracy of language model inference and evaluation.

Paper introduces TtT, market-implied transition time, from greenium term structure.

problem Estimating market-implied transition time to a low-carbon economy.
method Develops inference theory for TtT, introduces two stochastic models.
result Combines two-layer analysis for consistent estimation of diffusion parameters.

Bayesian Optimization tackles hidden constraints in architecture optimization.

problem Optimizing system architectures with hidden constraints using expensive physics-based simulations.
method Surrogate-based optimization with Gaussian Process models, including strategies for handling failed evaluations.
result Best performance achieved with a mixed-discrete GP predicting Probability of Viability (PoV) and minimum PoV threshold selection.

Scalarizing functions have been widely used to convert a multiobjective optimization problem into a single objective optimization problem. However, their use in solving (computationally) expensive multi- and many-objective optimization problems in Bayesian multiobjective optimization is scarce. Scalarizing functions ca…

2019-04-11abs ↗pdf ↗

Diffusion models generate music sequences without autoregressive loops.

problem Generating music sequences from symbolic data using diffusion models.
method Parameterize discrete symbolic data in continuous latent space, train diffusion model, generate sequences through reverse process.
result Strong unconditional generation and post-hoc conditional infilling compared to autoregressive models.

Sparse Gaussian process quantile regression tackles computational challenges in Bayesian quantile regression.

problem Nonconjugacy and computational cost in Gaussian process quantile regression.
method Sparse Gaussian process framework with Laplace approximation, adaptive inducing-input placement, and sequential data acquisition.
result Accuracy of Laplace approximation and effectiveness of adaptive mechanisms in reducing predictive uncertainty.

A scalable portfolio approach speeds up Bayesian optimization for noisy functions.

problem Efficiently selecting multiple designs in parallel for noisy, expensive black-box optimization.
method A portfolio approach that balances exploration and exploitation, using a scalable allocation strategy.
result Significant speed improvements over existing methods, with similar or better performance.

New method boosts performance of diffusion models on discrete data like natural language.

problem Performance of diffusion models on discrete data like natural language is poor.
method Proposes score entropy, a novel loss that extends score matching to discrete spaces.
result Significantly boosts performance on language modeling tasks.

New CH covariance class improves spatial statistics by balancing differentiability and tail behavior.

problem Lack of control over mean-square differentiability and tail behavior in Matérn covariance functions.
method Developed a new Confluent Hypergeometric (CH) covariance class using a scale mixture of Matérn and polynomial covariances.
result The CH class offers improved theoretical properties and better performance in extrapolative settings.

Kervaire's sphere-link is equivalent to a ribbon sphere-link, simplifying complex 2-complexes.

problem Understanding the structure of 2-complexes and their asphericity.
method Using Kervaire's sphere-link and ribbon sphere-link equivalence, analyzing the compact complement of ribbon disk-links.
result Every connected subcomplex of a contractible finite 2-complex is aspherical.

The 3-sphere has either 2 minimal 2-spheres or an optimal foliation by 2-spheres.

problem Proving existence of minimal 2-spheres or optimal foliations in arbitrary Riemannian 3-spheres.
method Analyzing the properties of arbitrary Riemannian metrics on 3-spheres.
result The existence of at least two minimal 2-spheres or an optimal foliation in 3-spheres with arbitrary metrics.

The study shows how to construct dd-spheres from (d1)(d-1)-spheres and dd-balls without additional vertices.

problem Constructing dd-spheres from (d1)(d-1)-spheres and dd-balls without additional vertices.
method Examining specific types of spheres (flag, stacked, join of spheres) and dd-balls to determine if constructions can be made without extra vertices.
result Affirmative answers to constructing dd-spheres from (d1)(d-1)-spheres and dd-balls without additional vertices for certain types of spheres and dd-balls.

Soliton spheres are immersed 2-spheres in the conformal 4-sphere S^4=HP^1 that allow rational, conformal parametrizations f:CP^1->HP^1 obtained via twistor projection and dualization from rational curves in CP^{2n+1}. Soliton spheres can be characterized as the case of equality in the quaternionic Pluecker estimate. A …

2009-05-13abs ↗pdf ↗

Classification of constant curvature surfaces in Berger spheres.

problem Identifying complete rotationally invariant surfaces with constant Gauss curvature in Berger spheres.
method Complete classification through detailed analysis of Clifford tori and spheres.
result Rotationally invariant spheres with constant Gauss curvature are the only topological spheres in Berger spheres for K>KPK > K_P.

Reduces connectivity problem for genus-4 Heegaard surface in 3-sphere.

problem Connectivity problem in reducing sphere complex for genus-4 Heegaard surface.
method Presented a sufficient condition for a non-separating weak reducing pair to be separated by a reducing sphere.
result Reduced connectivity problem to showing disjointness of representative reducing spheres from a fixed disk.

Paper studies inscribed sphere and lines through centers of Apollonius spheres in n dimensions.

problem Tangency of spheres and lines through their centers.
method Lie sphere geometry and two-step construction of Apollonius spheres.
result Center of inscribed sphere coincides with point PXP_X.

In the 1-parameter family of Berger spheres S^3(a), a > 0 (S^3(1) is the round 3-sphere of radius 1) we classify the stable constant mean curvature spheres, showing that in some Berger spheres (a close to 0) there are unstable constant mean curvature spheres. Also, we classify the orientable compact stable constant mea…

2009-06-08abs ↗pdf ↗

We introduce and study a new class of homotopy spheres called Farrell-Jones spheres. Using Farrell-Jones sphere we construct examples of closed negatively curved manifolds M2nM^{2n}, where n=7n=7 or 88, which are homeomorphic but not diffeomorphic to complex hyperbolic manifolds, thereby giving a partial answer to a que…

2015-10-11abs ↗pdf ↗