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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.

168,695 papers · 148 categories

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64128192256 · May 202619922001200920172026
48 results for smooth trajectory

Smooth Schrödinger Bridges improve trajectory inference by smoothing Gaussian processes.

problem Improving trajectory inference in applications like particle tracking.
method Generalizes Schrödinger Bridge problem to smooth Gaussian processes, solving the problem on phase space.
result The method outperforms existing methods on real datasets.

We consider billiard trajectories in a smooth convex body in Rd\mathbb R^d and estimate the number of distinct periodic trajectories that make exactly pp reflections per period at the boundary of the body. In the case of prime pp we obtain the lower bound (d2)(p1)+2(d-2)(p-1)+2, which is much better than the previous estimat…

2009-05-12abs ↗pdf ↗

We give lower bound on the number of periodic billiard trajectories inside a generic smooth strictly convex closed surface in 3-space: for odd n, there are at least 2(n-1) such trajectories. We apply a topological approach based on the calculation of cohomology of certain configuration spaces.

2001-06-07abs ↗pdf ↗

FLUID uses flows to unify filtering and smoothing for complex systems.

problem Bayesian filtering and smoothing for high-dimensional nonlinear systems.
method FLUID encodes observation histories into a fixed summary statistic, using flows for filtering and smoothing.
result FLUID provides accurate approximations of filtering and smoothing distributions.

The paper proposes a new method for clustering survival data using smoothed log-hazard trajectories.

problem Clustering survival data based on instantaneous risk dynamics.
method Functional Principal Component Analysis applied to B-spline smoothed log-hazard trajectories.
result The proposed method provides an interpretable representation of relative temporal risk dynamics.

The paper investigates how neural network weights evolve to monitor training progress.

problem Monitoring the training progress of neural networks in a cost-effective manner.
method Investigates the evolution of neural network weights in weight space.
result DNN models evolve on unique, smooth trajectories in weight space that can be used to track training progress.

Stochastic gradient descent's long-term fluctuations are described by a diffusion limit.

problem Long-term behavior of stochastic gradient descent in non-smooth settings.
method Functional central limit theorem applied to rescaled trajectory of SGD.
result Characterization of long-term fluctuations around the minimizer.

In this paper we use the Ekeland-Hofer-Zehnder symplectic capacity to provide several bounds and inequalities for the length of the shortest periodic billiard trajectory in a smooth convex body in Rn{\mathbb R}^{n}. Our results hold both for classical billiards, as well as for the more general case of Minkowski billiar…

2011-11-09abs ↗pdf ↗

Framework for continuous-time network data representation learning.

problem Learning reliable representations of dynamic network interactions.
method Three-stage process: intensity estimation, projection learning, evolving node representation construction.
result Trajectories satisfy structural and temporal coherence, providing robust inference.

Develops anytime-valid stopping rules for SGD based on observed trajectory.

problem Stopping stochastic gradient descent (SGD) based on observed trajectory.
method Develops anytime-valid confidence sequences for stochastic gradient methods.
result Statistically valid, time-uniform stopping rules for SGD across convex and nonconvex settings.

NESS improves neighbor embedding for smooth cell-state transitions in single-cell data.

problem Challenges in extracting smooth, low-dimensional representations from noisy single-cell data.
method Builds on PCS framework to develop NESS, a stable machine learning approach.
result NESS consistently yields useful biological insights across diverse single-cell datasets.

A comprehensive methodology is provided for smoothing noisy, irregularly sampled data with non-Gaussian noise using smoothing splines. We demonstrate how the spline order and tension parameter can be chosen a priori from physical reasoning. We also show how to allow for non-Gaussian noise and outliers which are typical…

2019-04-26abs ↗pdf ↗

Billiard trajectories and geodesics are closely related geometrically.

problem Understanding the relationship between billiard trajectories and geodesics on surfaces.
method Establishing mutual approximation results for billiard trajectories and geodesic segments on surfaces.
result For Riemannian billiard tables, there are families of fold-type surfaces such that every sequence of geodesic segments on these surfaces has a subsequence that converges to a billiard trajectory.

We provide lower bounds on the number of periodic Finsler billiard trajectories inside a quadratically convex smooth closed hypersurface MM in a dd-dimensional Finsler space with possibly irreversible Finsler metric. An example of such a system is a billiard in a sufficiently weak magnetic field. The rr-periodic Fin…

2017-12-21abs ↗pdf ↗

PhysVarMix predicts diverse urban trajectories with physics constraints.

problem Predicting complex urban agent trajectories with multiple plausible scenarios.
method Physics-informed variational mixture model combining learning and physics constraints.
result Superior performance compared to existing methods on benchmark datasets.

We prove that every compact plane billiard, bounded by a smooth curve, is insecure: there exist pairs of points A,BA,B such that no finite set of points can block all billiard trajectories from AA to BB.

2007-05-23abs ↗pdf ↗

This paper proposes a new algorithm for learning guidance rewards in RL.

problem Long-term temporal credit assignment in sparse or delayed reward environments.
method Surrogate RL objective with trajectory-space smoothing to learn guidance rewards.
result Guidance rewards can be learned without additional neural networks and have intuitive interpretation.

This paper describes the construction of a canonical compactification of the space of trajectories and of the unstable/stable sets of a generic gradient like vector field on a closed manifold as well as a canonical structure of a smooth manifold with corners of these spaces. As an application we discuss the geometric c…

2011-01-04abs ↗pdf ↗

Diffusion models' sampling paths lie in a low-dimensional subspace, resembling boomerangs.

problem Understanding the geometric structure of diffusion-based generative models.
method Characterization of deterministic sampling trajectories using low-dimensional subspace and kernel-estimated data modeling.
result Sampling trajectories in diffusion models are confined to a low-dimensional subspace and exhibit a boomerang shape.

The paper is an informal report on joint work with Stefan Haller on Dynamics in relation with Topology and Spectral Geometry. By dynamics one means a smooth vector field on a closed smooth manifold; the elements of dynamics of concern are the rest points, instantons and closed trajectories. One discusses their counting…

2010-12-28abs ↗pdf ↗

KOMET identifies Koopman operators from model parameter trajectories to adapt to evolving data distributions.

problem Adaptation of parametric models to non-stationary environments.
method Data-driven framework using Koopman operator identification and Extended Dynamic Mode Decomposition (EDMD).
result KOMET achieves high autonomous-rollout accuracies of 0.981 to 1.000 over 100 time steps on various drifting datasets.

This study models target trajectories using stochastic processes for efficient tracking.

problem Efficiently modeling and predicting target trajectories in continuous time.
method Decomposes trajectory modeling into deterministic and stochastic components using Gaussian or Student's-tt processes.
result Demonstrates superior performance in tracking maneuvering targets compared to existing methods.

Dan Reznik found, by computer experimentation, a number of conserved quantities associated with periodic billiard trajectories in ellipses. We prove some of his observations using a non-standard generating function for the billiard ball map. In this way, we also obtain some identities valid for all smooth convex billia…

2020-01-23abs ↗pdf ↗

Bayesian Neural ODEs improve vessel trajectory prediction with better uncertainty estimates.

problem Challenges in predicting vessel trajectories from irregular AIS data.
method Adopted a Gaussian process (GP) kernel-based prior on the vector field evaluated at measurement points, combined with probabilistic multiple shooting for long trajectories.
result Improved accuracy and uncertainty quantification in vessel trajectory predictions.

3MSBM learns smooth trajectories from multiple snapshots.

problem Capturing long-range temporal dependencies in complex systems.
method Lifts dynamics to phase space, generalizes stochastic bridges to multi-marginal conditional problems, learns transport maps preserving intermediate marginals.
result Significantly improves convergence and scalability in capturing complex dynamics.

We give topological lower bounds on the number of periodic and closed trajectories in strictly convex smooth billiards. We use variational reduction admitting a finite group of symmetries and apply topological approach based on equivariant Morse and Lusternik - Schnirelman theories. The paper continues results publishe…

2000-06-12abs ↗pdf ↗

Hamiltonian dynamics-based algorithms achieve deterministic and accelerated convergence for convex optimization.

problem Accelerating convex optimization
method Hamiltonian dynamics
result Hamiltonian dynamics-based algorithms achieve deterministic and accelerated convergence for convex optimization.

The paper develops a new theory to understand deep learning optimization.

problem Understanding the dynamics of optimization in deep learning, especially in the edge of stability regime.
method Developed a central flow differential equation to describe the time-averaged trajectory of oscillatory optimizers.
result Central flows can predict long-term optimization trajectories with high numerical accuracy.

Novel method for SDE calibration from sparse data using neural flows.

problem Calibrating SDEs from sparse, noisy observations.
method Characterization of posterior SDE using neural networks trained to solve a PDE with multiplicative updates.
result Significant improvement in scalability and accuracy compared to classical methods.

When the Poincaré map associated with a periodic orbit of a hybrid dynamical system has constant-rank iterates, we demonstrate the existence of a constant-dimensional invariant subsystem near the orbit which attracts all nearby trajectories in finite time. This result shows that the long-term behavior of a hybrid model…

2011-09-08abs ↗pdf ↗

We present a probabilistic language model for time-stamped text data which tracks the semantic evolution of individual words over time. The model represents words and contexts by latent trajectories in an embedding space. At each moment in time, the embedding vectors are inferred from a probabilistic version of word2ve…

2017-02-27abs ↗pdf ↗

A new method for generating samples without training, using smoothed score matching.

problem Generating samples efficiently and without training.
method Moment-matched score-smoothed overdamped Langevin dynamics (MM-SOLD).
result The method enables fast, robust, training-free sampling with competitive sample fidelity and diversity.

Let TRm+1T\subset \R^{m+1} be a strictly convex domain bounded by a smooth hypersurface X=TX=\partial T. In this paper we find lower bounds on the number of billiard trajectories in TT which have a prescribed intial point AXA\in X, a prescribed final point BXB\in X and make a prescribed number nn of reflections at the bo…

2000-06-07abs ↗pdf ↗

We study the problem of discriminative sub-trajectory mining. Given two groups of trajectories, the goal of this problem is to extract moving patterns in the form of sub-trajectories which are more similar to sub-trajectories of one group and less similar to those of the other. We propose a new method called Statistica…

2019-05-06abs ↗pdf ↗