arXiv research

Deep RL estimates muscle excitations in biomechanical simulations.

Developed Taylor series for muscle-finger system analysis.

We present a comprehensive study of the use of generative modeling approaches for Multiple-Instance Learning (MIL) problems. In MIL a learner receives training instances grouped together into bags with labels for the bags only (which might not be correct for the comprised instances). Our work was motivated by the task …

Machine learning speeds up quantum chemical calculations of excited states.

New self-exciting random evolutions (SEREs) for modeling traffic and transport processes.

The excited states of polyatomic systems are rather complex, and often exhibit meta-stable dynamical behaviors. Static analysis of reaction pathway often fails to sufficiently characterize excited state motions due to their highly non-equilibrium nature. Here, we proposed a time series guided clustering algorithm to ge…

Machine learning aids excited-state molecular dynamics studies.

Optimal reinsurance strategy analyzed for dynamic risk model with self- and externally-excited jumps.

In Levin-Wen (LW) models, a wide class of exactly solvable discrete models, for two dimensional topological phases, it is relatively easy to describe only single fluxon excitations, but not the charge and dyonic as well as many-fluxon excitations. To incorporate charged and dyonic excitations in (doubled) topological p…

Paper presents a method for estimating Hawkes process parameters.

CSI-Net learns WiFi signals for body characterization and pose recognition.

3D ConvNets improved with Project & Excite for medical imaging segmentation.

Deep-learning method improves sEMG-based gesture recognition accuracy.

Paper explores ML for UV spectra, showing transferability in chemical space.

Deep QMC method accurately computes electronic excited states.

Study shows self-exciting shocks increase systemic risk in interbank networks.

Modeling aggressive market order arrivals using Hawkes factor models.

Develops machine learning models for excited states of CH2NH2+.

A new model predicts discrete events with flexible, nonparametric baseline and excitation.

Persistency of excitation ensures correct parameter estimation in neural networks.

Lower bounds and upper bounds on sample complexity for identifying linear dynamical systems.

Paper analyzes coexisting hidden and self-excited attractors in an economic system.

Researchers use Hawkes processes to analyze credit trades, revealing self-excitement and volume impacts.

Optimal noise excitation for linear system identification reduces sample complexity.

New algorithm learns LQR with $O(\sqrt{T})$ regret using Langevin dynamics and excitation.

Paper forecasts financial trading durations using a new point process model.

The Hawkes process is a simple point process, whose intensity function depends on the entire past history and is self-exciting and has the clustering property. The Hawkes process is in general non-Markovian. The linear Hawkes process has immigration-birth representation. Based on that, Fierro et al. recently introduced…

Study on stochastic volatility models with external shocks triggering jump cascades.

Develops a goodness-of-fit test for self-exciting processes.

Artificial neural networks reduce computational costs for predicting excitation energy transfer properties in light-harvesting systems.

This study improves text-to-speech synthesis using GANs for glottal excitation.

Study state-dependent Hawkes processes for limit order book modeling.

Multiplex Network Hawkes model for systemic risk measurement

Safe control of systems with unknown dynamics using persistent excitation.

Study optimal dividend and capital injection in insurance portfolios with self-exciting claim arrivals.

Paper introduces MSPD for multivariate risk processes with dependencies.

Paper detects communities from graph signals using low-rank excitation modeling.

Estimates network structure from spike train data using point process models.

This study defines a multivariate Self--Exciting Threshold Autoregressive with eXogenous input (MSETARX) models and present an estimation procedure for the parameters. The conditions for stationarity of the nonlinear MSETARX models is provided. In particular, the efficiency of an adaptive parameter estimation algorithm…

Proposes a speaker-independent GlotNet vocoder using WaveNet for speech generation.

GAttNHP predicts future events in temporal knowledge graphs by encoding long-range dependencies and handling mutual excitation.

We propose an extension to Hawkes processes by treating the levels of self-excitation as a stochastic differential equation. Our new point process allows better approximation in application domains where events and intensities accelerate each other with correlated levels of contagion. We generalize a recent algorithm f…

Tiled Squeeze-and-Excite improves channel attention with local spatial context.

New method interprets recurrent neural networks via excitable network attractors.

Many real-valued stochastic time-series are locally linear (Gassian), but globally non-linear. For example, the trajectory of a human hand gesture can be viewed as a linear dynamic system driven by a nonlinear dynamic system that represents muscle actions. We present a mixed-state dynamic graphical model in which a hid…

MEG models for dynamic networks estimate dependencies and shared latent space relationships.

In this paper we studied about the wavelet identification of the thresholds and time delay for more general case without the constraint that the time delay is smaller than the order of the model. Here we composed an empirical wavelet from the SETAR (Self-Exciting Threshold Autoregressive) model and identified the thres…

New method improves neural network robustness to adversarial attacks.