Search Results for author: Fei Lu

Found 11 papers, 1 papers with code

Data adaptive RKHS Tikhonov regularization for learning kernels in operators

no code implementations8 Mar 2022 Fei Lu, Quanjun Lang, Qingci An

We present DARTR: a Data Adaptive RKHS Tikhonov Regularization method for the linear inverse problem of nonparametric learning of function parameters in operators.

Identifiability of interaction kernels in mean-field equations of interacting particles

no code implementations10 Jun 2021 Quanjun Lang, Fei Lu

We study the identifiability of the interaction kernels in mean-field equations for intreacting particle systems.

Domain Adaptive Monocular Depth Estimation With Semantic Information

no code implementations12 Apr 2021 Fei Lu, Hyeonwoo Yu, Jean Oh

The advent of deep learning has brought an impressive advance to monocular depth estimation, e. g., supervised monocular depth estimation has been thoroughly investigated.

Image Classification Monocular Depth Estimation

ISALT: Inference-based schemes adaptive to large time-stepping for locally Lipschitz ergodic systems

no code implementations25 Feb 2021 Xingjie Li, Fei Lu, Felix X. -F. Ye

However, locally Lipschitz SDEs often require special treatments such as implicit schemes with small time-steps to accurately simulate the ergodic measure.

On the coercivity condition in the learning of interacting particle systems

no code implementations20 Nov 2020 Zhongyang Li, Fei Lu

In the learning of systems of interacting particles or agents, coercivity condition ensures identifiability of the interaction functions, providing the foundation of learning by nonparametric regression.

Learning interaction kernels in mean-field equations of 1st-order systems of interacting particles

no code implementations29 Oct 2020 Quanjun Lang, Fei Lu

We introduce a nonparametric algorithm to learn interaction kernels of mean-field equations for 1st-order systems of interacting particles.

Learning interaction kernels in stochastic systems of interacting particles from multiple trajectories

no code implementations30 Jul 2020 Fei Lu, Mauro Maggioni, Sui Tang

Finally, we exhibit an efficient parallel algorithm to construct the estimator from data, and we demonstrate the effectiveness of our algorithm with numerical tests on prototype systems including stochastic opinion dynamics and a Lennard-Jones model.

Learning interaction kernels in heterogeneous systems of agents from multiple trajectories

no code implementations10 Oct 2019 Fei Lu, Mauro Maggioni, Sui Tang

These simulations also suggest that our estimators are robust to noise in the observations, and produce accurate predictions of dynamics in relative large time intervals, even when they are learned from data collected in short time intervals.

Data-driven model reduction, Wiener projections, and the Koopman-Mori-Zwanzig formalism

no code implementations21 Aug 2019 Kevin K. Lin, Fei Lu

Model reduction methods aim to describe complex dynamic phenomena using only relevant dynamical variables, decreasing computational cost, and potentially highlighting key dynamical mechanisms.

Time Series Time Series Analysis

The Unconstrained Ear Recognition Challenge 2019 - ArXiv Version With Appendix

no code implementations11 Mar 2019 Žiga Emeršič, Aruna Kumar S. V., B. S. Harish, Weronika Gutfeter, Jalil Nourmohammadi Khiarak, Andrzej Pacut, Earnest Hansley, Mauricio Pamplona Segundo, Sudeep Sarkar, Hyeonjung Park, Gi Pyo Nam, Ig-Jae Kim, Sagar G. Sangodkar, Ümit Kaçar, Murvet Kirci, Li Yuan, Jishou Yuan, Haonan Zhao, Fei Lu, Junying Mao, Xiaoshuang Zhang, Dogucan Yaman, Fevziye Irem Eyiokur, Kadir Bulut Özler, Hazim Kemal Ekenel, Debbrota Paul Chowdhury, Sambit Bakshi, Pankaj K. Sa, Banshidhar Majhi, Peter Peer, Vitomir Štruc

The goal of the challenge is to assess the performance of existing ear recognition techniques on a challenging large-scale ear dataset and to analyze performance of the technology from various viewpoints, such as generalization abilities to unseen data characteristics, sensitivity to rotations, occlusions and image resolution and performance bias on sub-groups of subjects, selected based on demographic criteria, i. e. gender and ethnicity.

Person Recognition

Nonparametric inference of interaction laws in systems of agents from trajectory data

1 code implementation14 Dec 2018 Fei Lu, Mauro Maggioni, Sui Tang, Ming Zhong

Inferring the laws of interaction between particles and agents in complex dynamical systems from observational data is a fundamental challenge in a wide variety of disciplines.

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