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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.

168,932 papers · 148 categories

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48 results for model bridging

Paper extends Brownian bridge with random length and pinning point for financial modeling.

problem Modeling financial information flow with uncertainty in pinning point.
method Introduced an extension of Brownian bridge with random length and pinning point, derived formulae for conditional expectations.
result The extended Brownian bridge fails to be Markovian if pinning point distribution is absolutely continuous.

Neural network approximates diffusion bridges for efficiency and robustness.

problem Efficient simulation of conditioned diffusion processes, especially rare events and multimodal distributions.
method Trains a neural network to approximate bridge dynamics, eliminating MCMC and score modeling.
result Efficient sampling of conditioned diffusion bridges at comparable cost to unconditioned process.

Paper introduces a new generative learning model using Schrödinger bridge diffusion in latent space.

problem Learning distributions from divergent data distributions.
method Pre-training with large-scale models, Schrödinger bridge diffusion model in latent space.
result Effective control of second-order Wasserstein distance between generated and target distributions.

In order to alleviate data sparsity and overfitting problems in maximum likelihood estimation (MLE) for sequence prediction tasks, we propose the Generative Bridging Network (GBN), in which a novel bridge module is introduced to assist the training of the sequence prediction model (the generator network). Unlike MLE di…

2017-06-28abs ↗pdf ↗

FDBM models use fractional Brownian motion to model complex stochastic processes.

problem Capturing memory effects and long-range dependencies in stochastic processes.
method Developed a generative diffusion bridge framework using a Markovian approximation of fractional Brownian motion.
result FDBM outperforms standard models in predicting future states and unpaired data translation.

This paper tackles infinite-dimensional diffusion bridge simulation using operator learning.

problem Challenges in simulating diffusion bridges for modeling natural data due to intractable drift terms and continuous data representations.
method Merges score matching techniques with operator learning to directly learn infinite-dimensional bridges.
result Demonstrates high efficacy in simulating diffusion bridges for various applications, including real-world biological data.

A new sampler for FLMs improves token-level decoding controls.

problem Sampling from FLMs using standard methods collapses marginals and produces invalid sequences.
method Samples clean one-hot endpoints from FLM token marginals and uses Ornstein-Uhlenbeck bridges conditioned on these endpoints.
result The method preserves token-wise posterior-predictive marginals and improves quality-diversity tradeoff.

The paper provides examples of keen weakly reducible bridge spheres for links in b-bridge position.

problem Characterizing and finding examples of keen weakly reducible bridge spheres.
method Analyzing bridge spheres and their properties in terms of compressing disks and width complex.
result Infinitely many examples of keen weakly reducible bridge spheres for links in b-bridge position.

Extends diffusion-based Schrödinger bridge models to handle time-dependent potentials.

problem Approximating optimal transport dynamics between two boundary distributions with a twisted Brownian motion reference.
method Introduces Twisted Schrödinger Bridge Matching (TSBM) using the Iterative Markovian Fitting (IMF) paradigm, incorporating a gradient-dependent bridge-matching loss.
result Improves trajectory inference across high-dimensional settings, including crowd navigation and single-cell data.

Suppose a knot in a 33-manifold is in nn-bridge position. We consider a reduction of the knot along a bridge disk DD and show that the result is an (n1)(n-1)-bridge position if and only if there is a bridge disk EE such that (D,E)(D, E) is a cancelling pair. We apply this to an unknot KK, in nn-bridge position with re…

2016-06-23abs ↗pdf ↗

New framework models non-conservative stochastic processes without energy conservation constraints.

problem Existing Schrödinger Bridge methods are limited by energy-conservation assumptions.
method Introduces non-conservative generalized Schrödinger bridge (NCGSB) based on contact Hamiltonian mechanics.
result Contact Wasserstein geodesic (CWG) provides a broader class of real-world stochastic processes.

UNSB uses neural Schrödinger Bridge to solve unpaired image-to-image translation.

problem Difficulties in unpaired image-to-image translation with diffusion models.
method Expresses SB problem as adversarial learning problems, incorporating advanced discriminators and regularization.
result Successfully solves various unpaired image-to-image translation tasks.

We show that if KK is a knot in S3S^3 and ΣΣ is a bridge sphere for KK with high distance and 2n2n punctures, the number of perturbations of KK required to interchange the two balls bounded by ΣΣ via an isotopy is nn. We also construct a knot with two different bridge spheres with 2n2n and 2n12n-1 bridges respecti…

2009-08-25abs ↗pdf ↗

Paper explores link and plat presentations, showing equivalence under bridge isotopy.

problem Relationship between links in bridge position and plat presentations.
method Established equivalence between Hilden double coset classes of plat presentations and bridge positions up to isotopy.
result Revealed equivalence of Hilden double coset classes for n-bridge unknot and torus knots in plat position.

The paper diagnoses factor-model pricing errors using characteristic axes and bridge-alpha curves.

problem Tackles systematic sign reversals and overcorrections in factor-model pricing errors.
method Extends cap-axis integral diagnostic to characteristic axes, measures pricing errors as bridge-alpha curves, and uses a predetermined characteristic order to generate zero-curve restrictions.
result Axis-level pricing errors are nearly orthogonal to maximum-Sharpe gains, showing significant sign reversals and overcorrections.