arXiv research

Persistency of excitation ensures correct parameter estimation in neural networks.

Develops machine learning models for excited states of CH2NH2+.

Deep QMC method accurately computes electronic excited states.

New method improves neural network robustness to adversarial attacks.

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

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

This work synthesizes realistic data from neural excitation patterns to anonymize private data.

Motor control is a set of time-varying muscle excitations which generate desired motions for a biomechanical system. Muscle excitations cannot be directly measured from live subjects. An alternative approach is to estimate muscle activations using inverse motion-driven simulation. In this article, we propose a deep rei…

Neural waveform models such as the WaveNet are used in many recent text-to-speech systems, but the original WaveNet is quite slow in waveform generation because of its autoregressive (AR) structure. Although faster non-AR models were recently reported, they may be prohibitively complicated due to the use of a distillin…

Introduction: Machine learning provides fundamental tools both for scientific research and for the development of technologies with significant impact on society. It provides methods that facilitate the discovery of regularities in data and that give predictions without explicit knowledge of the rules governing a syste…

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

Machine learning speeds up quantum chemical calculations of excited states.

Understanding the relationship between the structure of light-harvesting systems and their excitation energy transfer properties is of fundamental importance in many applications including the development of next generation photovoltaics. Natural light harvesting in photosynthesis shows remarkable excitation energy tra…

NNNH uses neural networks to model complex event patterns.

Neural networks are based on a simplified model of the brain. In this project, we wanted to relax the simplifying assumptions of a traditional neural network by making a model that more closely emulates the low level interactions of neurons. Like in an RNN, our model has a state that persists between time steps, so tha…

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

Paper proposes a neural network for non-parametric Hawkes process kernel estimation.

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…

Consider observing a collection of discrete events within a network that reflect how network nodes influence one another. Such data are common in spike trains recorded from biological neural networks, interactions within a social network, and a variety of other settings. Data of this form may be modeled as self-excitin…

Machine learning aids excited-state molecular dynamics studies.

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

QCNNs avoid barren plateaus, making them trainable.

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.

This paper proposes a method for generating speech from filterbank mel frequency cepstral coefficients (MFCC), which are widely used in speech applications, such as ASR, but are generally considered unusable for speech synthesis. First, we predict fundamental frequency and voicing information from MFCCs with an autoreg…

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

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

Attention mechanism is a hot spot in deep learning field. Using channel attention model is an effective method for improving the performance of the convolutional neural network. Squeeze-and-Excitation block takes advantage of the channel dependence, selectively emphasizing the important channels and compressing the rel…

The state-of-the-art in text-to-speech synthesis has recently improved considerably due to novel neural waveform generation methods, such as WaveNet. However, these methods suffer from their slow sequential inference process, while their parallel versions are difficult to train and even more expensive computationally. …

Price changes are induced by aggressive market orders in stock market. We introduce a bivariate marked Hawkes process to model aggressive market order arrivals at the microstructural level. The order arrival intensity is marked by an exogenous part and two endogenous processes reflecting the self-excitation and cross-e…

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.

Optimal noise excitation for linear system identification reduces sample complexity.

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

A machine learning framework predicts self-induced stochastic resonance in neurons.

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.

Multiplex Network Hawkes model for systemic risk measurement

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

Paper introduces MSPD for multivariate risk processes with dependencies.

In this paper we consider a mean-field model of interacting diffusions for the monetary reserves in which the reserves are subjected to a self- and cross-exciting shock. This is motivated by the financial acceleration and fire sales observed in the market. We derive a mean-field limit using a weak convergence analysis …

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…

New insights into training deep networks with low rank layers.

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.

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…

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