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.
Improves neural relational inference for dynamic multi-agent trajectories.
DGRCL integrates dynamic and static graph relations for financial market prediction.
MDGNN predicts stock prices by capturing multifaceted relations over time.
Paper connects dynamics of mechanical systems to Reeb dynamics.
The paper generalizes relations between dynamical series and resolvents of vector fields.
Proposes a graph dynamics prior for more accurate relational inference.
Model for dynamic relational data with regime changes.
DOODL learns shared spectral dynamics across related dynamical systems.
Relational data-like graphs, networks, and matrices-is often dynamic, where the relational structure evolves over time. A fundamental problem in the analysis of time-varying network data is to extract a summary of the common structure and the dynamics of the underlying relations between the entities. Here we build on t…
We introduce dynamic asymptotic dimension, a notion of dimension for actions of discrete groups on locally compact spaces, and more generally for locally compact étale groupoids. We study our notion for minimal actions of the integer group, its relation with conditions used by Bartels, Lück, and Reich in the context of…
To each dynamic equivalence of two control systems is associated an infinite permutation matrix. We investigate how such matrices are related to the existence of dynamic equivalences.
Study regularization in deep networks, uncovering performance relations and proposing a training schedule.
Most previous contributions to BSDEs, and the related theories of nonlinear expectation and dynamic risk measures, have been in the framework of continuous time diffusions or jump diffusions. Using solutions of BSDEs on spaces related to finite state, continuous time Markov chains, we develop a theory of nonlinear expe…
Sharkovskii proved that the existence of a periodic orbit in a one-dimensional dynamical system implies existence of infinitely many periodic orbits. We obtain an analog of Sharkovskii's theorem for periodic orbits of shear homeomorphisms of the torus. This is done by obtaining a dynamical order relation on the set of …
Paper introduces dynamic strategies for multi-period investment models.
Symmetric binary matrices representing relations among entities are commonly collected in many areas. Our focus is on dynamically evolving binary relational matrices, with interest being in inference on the relationship structure and prediction. We propose a nonparametric Bayesian dynamic model, which reduces dimension…
As a metric to measure the performance of an online method, dynamic regret with switching cost has drawn much attention for online decision making problems. Although the sublinear regret has been provided in many previous researches, we still have little knowledge about the relation between the dynamic regret and the s…
The Dirichlet Belief Network~(DirBN) has been recently proposed as a promising approach in learning interpretable deep latent representations for objects. In this work, we leverage its interpretable modelling architecture and propose a deep dynamic probabilistic framework -- the Recurrent Dirichlet Belief Network~(Recu…
Graph-based approach predicts stock trends using dynamic multi-relational graphs.
Study the geometry and dynamics of skew evolutes and involutes, related to bicycle kinematics.
DREAM model improves computational efficiency for non-linear effects in relational event models.
Temporal networks are ubiquitous and evolve over time by the addition, deletion, and changing of links, nodes, and attributes. Although many relational datasets contain temporal information, the majority of existing techniques in relational learning focus on static snapshots and ignore the temporal dynamics. We propose…
Model captures neural activity related to behavior while separating internal computations.
Bayesian meta-learning on relation graphs improves few-shot relation extraction.
Meta-Dynamic models learn shared neural dynamics across tasks.
Employing data on the assessed value of land in 1983 -- 2005 Japan, we investigate the dynamical behavior in the high scale region of non-equilibrium systems. From the detailed quasi-balance and Gibrat's law, we derive a relation between the change of Pareto index and a symmetry in the detailed quasi-balance. The relat…
Amortized Causal Discovery learns to infer causal graphs from time-series data, improving performance.
New benchmarks improve model performance by accounting for isomorphism classes in multi-relational datasets.
In this paper, we construct invariants of braids, knots and links by studying dynamics of points in and applying the Ptolemy relation .
Proposes a THGNN for dynamic financial time series prediction.
Paper introduces OMD for ordered state transitions in SSMs.
Real-world dynamical systems often consist of multiple stochastic subsystems that interact with each other. Modeling and forecasting the behavior of such dynamics are generally not easy, due to the inherent hardness in understanding the complicated interactions and evolutions of their constituents. This paper introduce…
Proposes a new model for more accurate demand forecasting considering dynamic contextual information.
Paper shows equivalence between MM and PH for n-D Morse functions.
In this paper we relate the study of actions of discrete groups over connected manifolds to that of their orbit spaces seen as differentiable stacks. We show that the orbit stack of a discrete dynamical system on a simply connected manifold encodes the dynamics up to conjugation and inversion. We also prove a generaliz…
In this paper, we consider the pricing of derivative products that involve dynamic hedging strategies and payments within the planning horizon. Equity-indexed annuities (EIAs), Guaranteed investment certificate (GIC), American and Barrier options are typical examples of these products. Our exploration involves evaluati…
Fast algorithm for analyzing huge social networks.
DArtNet predicts time series data using graph structure and dynamic attributes.
Rapid mixing of Langevin dynamics on Riemannian manifolds
Interacting systems are prevalent in nature, from dynamical systems in physics to complex societal dynamics. The interplay of components can give rise to complex behavior, which can often be explained using a simple model of the system's constituent parts. In this work, we introduce the neural relational inference (NRI…
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…
dLDS models neural dynamics as sparse combinations of simpler components.
Extends optimal transport to dynamic and martingale settings.
We study utility maximization problem for general utility functions using dynamic programming approach. We consider an incomplete financial market model, where the dynamics of asset prices are described by an -valued continuous semimartingale. Under some regularity assumptions we derive backward stochastic partial…
In the present work we introduce a novel multi-agent model with the aim to reproduce the dynamics of a double auction market at microscopic time scale through a faithful simulation of the matching mechanics in the limit order book. The agents follow a noise decision making process where their actions are related to a s…
Graphs are essential representations of many real-world data such as social networks. Recent years have witnessed the increasing efforts made to extend the neural network models to graph-structured data. These methods, which are usually known as the graph neural networks, have been applied to advance many graphs relate…
New relation found between ADM mass and generalized Komar energy for dynamical spacetimes.
Variational auto-encoder frameworks have demonstrated success in reducing complex nonlinear dynamics in molecular simulation to a single non-linear embedding. In this work, we illustrate how this non-linear latent embedding can be used as a collective variable for enhanced sampling, and present a simple modification th…