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Many techniques have been developed, such as model compression, to make Deep Neural Networks (DNNs) inference more efficiently. Nevertheless, DNNs still lack excellent run-time dynamic inference capability to enable users trade-off accuracy…

Computer Vision and Pattern Recognition · Computer Science 2020-09-15 Li Yang , Zhezhi He , Yu Cao , Deliang Fan

Unconstrained optimization problems become more common in scientific computing and engineering applications with the rapid development of artificial intelligence, and numerical methods for solving them more quickly and efficiently have been…

Optimization and Control · Mathematics 2025-04-17 Lin Li , Pengcheng Xie , Li Zhang

This paper proposes a novel proximal-gradient algorithm for a decentralized optimization problem with a composite objective containing smooth and non-smooth terms. Specifically, the smooth and nonsmooth terms are dealt with by gradient and…

Optimization and Control · Mathematics 2021-02-02 Zhi Li , Wei Shi , Ming Yan

We introduce a new approach for decoupling trends (drift) and changepoints (shifts) in time series. Our locally adaptive model-based approach for robustly decoupling combines Bayesian trend filtering and machine learning based…

Methodology · Statistics 2024-01-09 Haoxuan Wu , Toryn L. J. Schafer , Sean Ryan , David S. Matteson

In high-dimensional statistical inference in which the number of parameters to be estimated is larger than that of the holding data, regularized linear estimation techniques are widely used. These techniques have, however, some drawbacks.…

Methodology · Statistics 2025-08-06 Takashi Takahashi , Yoshiyuki Kabashima

The emergence of 3D Gaussian Splatting (3DGS) has recently ignited a renewed wave of research in dense visual SLAM. However, existing approaches encounter challenges, including sensitivity to artifacts and noise, suboptimal selection of…

Robotics · Computer Science 2025-08-26 Wancai Zheng , Xinyi Yu , Jintao Rong , Linlin Ou , Yan Wei , Libo Zhou

This paper describes an extension of the BFGS and L-BFGS methods for the minimization of a nonlinear function subject to errors. This work is motivated by applications that contain computational noise, employ low-precision arithmetic, or…

Optimization and Control · Mathematics 2021-09-10 Hao-Jun Michael Shi , Yuchen Xie , Richard Byrd , Jorge Nocedal

The propensity score (PS) is often used to control for large numbers of covariates in high-dimensional healthcare database studies. The least absolute shrinkage and selection operator (LASSO) has become the most widely used tool for fitting…

Methodology · Statistics 2025-12-17 Richard Wyss , Ben B. Hansen , Georg Hahn , Lars van der Laan , Kueiyu Joshua Lin

Implicit inverse problems, in which noisy observations of a physical quantity are used to infer a nonlinear functional applied to an associated function, are inherently ill posed and often exhibit non uniqueness of solutions. Such problems…

Numerical Analysis · Mathematics 2025-05-27 Davide Parodi , Federico Benvenuto , Sara Garbarino , Michele Piana

While many real-world data streams imply that they change frequently in a nonstationary way, most of deep learning methods optimize neural networks on training data, and this leads to severe performance degradation when dataset shift…

Machine Learning · Computer Science 2021-07-02 Wonju Lee , Seok-Yong Byun , Jooeun Kim , Minje Park , Kirill Chechil

Generative models such as denoising diffusion models are quickly advancing their ability to approximate highly complex data distributions. They are also increasingly leveraged in scientific machine learning, where samples from the implied…

Machine Learning · Computer Science 2025-03-14 Jan-Hendrik Bastek , WaiChing Sun , Dennis M. Kochmann

Localized features such as singularities, sharp gradients, discontinuities, and moving sources require adaptive finite element discretizations. Conventional refinement strategies introduce significant computational overhead through…

Computational Engineering, Finance, and Science · Computer Science 2026-04-29 Jan Niklas Schmäke , Martin Ruess

In this paper we address the convergence of stochastic approximation when the functions to be minimized are not convex and nonsmooth. We show that the "mean-limit" approach to the convergence which leads, for smooth problems, to the ODE…

Optimization and Control · Mathematics 2018-05-08 Szymon Majewski , Błażej Miasojedow , Eric Moulines

Precision motion systems are at the core of various manufacturing equipment. The rapidly increasing demand for higher productivity necessitates higher control bandwidth in the motion systems to effectively reject disturbances while…

Systems and Control · Electrical Eng. & Systems 2023-11-14 Jingjie Wu , Lei Zhou

This work focuses on a compositional data-driven approach to verify incremental global asymptotic stability (delta-GAS) over interconnected homogeneous networks of degree one with unknown mathematical dynamics. Our proposed approach…

Systems and Control · Electrical Eng. & Systems 2026-04-29 Mahdieh Zaker , David Angeli , Abolfazl Lavaei

In a variational denoising model, weight in the data fidelity term plays the role of enhancing the noise-removal capability. It is profoundly correlated with noise information, while also balancing the data fidelity and regularization…

Image and Video Processing · Electrical Eng. & Systems 2026-04-14 Xiangyu Rui , Xiangyong Cao , Xile Zhao , Deyu Meng , Michael K. NG

Conventional compressed sensing theory assumes signals have sparse representations in a known, finite dictionary. Nevertheless, in many practical applications such as direction-of-arrival (DOA) estimation and line spectral estimation, the…

Information Theory · Computer Science 2014-12-19 Jun Fang , Huiping Duan , Jing Li , Hongbin Li , Rick S. Blum

We present a novel view of nonlinear manifold learning using derivative-free optimization techniques. Specifically, we propose an extension of the classical multi-dimensional scaling (MDS) method, where instead of performing gradient…

Can stochastic gradient methods track a moving target? We study the problem of tracking multidimensional time-varying parameters under noisy observations and possible model misspecification. Gradient-based filters update the time-varying…

Methodology · Statistics 2026-05-05 Simon Donker van Heel , Rutger-Jan Lange , Bram van Os , Dick van Dijk

This paper introduces a novel anomaly detection framework that combines the robust statistical principles of density-estimation-based anomaly detection methods with the representation-learning capabilities of deep learning models. The…

Machine Learning · Computer Science 2024-08-15 Joseph Gallego-Mejia , Oscar Bustos-Brinez , Fabio A. González