Search Results for author: Guo Yu

Found 14 papers, 3 papers with code

Implementation of Kalman Filter Approach for Active Noise Control by Using MATLAB: Dynamic Noise Cancellation

1 code implementation10 Feb 2024 Guo Yu

Hence, this study suggests employing the Kalman filter in the active noise control (ANC) system to enhance the efficacy of noise reduction for dynamic noise.

Management

One-Step Forward and Backtrack: Overcoming Zig-Zagging in Loss-Aware Quantization Training

1 code implementation30 Jan 2024 Lianbo Ma, Yuee Zhou, Jianlun Ma, Guo Yu, Qing Li

During the gradient descent learning, a one-step forward search is designed to find the trial gradient of the next-step, which is adopted to adjust the gradient of current step towards the direction of fast convergence.

Quantization

Knowledge-Assisted Dual-Stage Evolutionary Optimization of Large-Scale Crude Oil Scheduling

no code implementations9 Jan 2024 Wanting Zhang, Wei Du, Guo Yu, Renchu He, Wenli Du, Yaochu Jin

On the basis of the proposed model, a dual-stage evolutionary algorithm driven by heuristic rules (denoted by DSEA/HR) is developed, where the dual-stage search mechanism consists of global search and local refinement.

Scheduling

Analysis on the Influence of Synchronization Error on Fixed-filter Active Noise Control

no code implementations6 Oct 2023 Guo Yu

The efficacy of active noise control technology in mitigating urban noise, particularly in relation to low-frequency components, has been well-established.

An Interpretable Hybrid Predictive Model of COVID-19 Cases using Autoregressive Model and LSTM

1 code implementation14 Nov 2022 Yangyi Zhang, Sui Tang, Guo Yu

The Coronavirus Disease 2019 (COVID-19) has a profound impact on global health and economy, making it crucial to build accurate and interpretable data-driven predictive models for COVID-19 cases to improve policy making.

Survey on Evolutionary Deep Learning: Principles, Algorithms, Applications and Open Issues

no code implementations23 Aug 2022 Nan Li, Lianbo Ma, Guo Yu, Bing Xue, Mengjie Zhang, Yaochu Jin

Specifically, we firstly illuminate EDL from machine learning and EC and regard EDL as an optimization problem.

AutoML Feature Engineering

Towards Fairness-Aware Multi-Objective Optimization

no code implementations22 Jul 2022 Guo Yu, Lianbo Ma, Wei Du, Wenli Du, Yaochu Jin

Recent years have seen the rapid development of fairness-aware machine learning in mitigating unfairness or discrimination in decision-making in a wide range of applications.

BIG-bench Machine Learning Decision Making +2

Pareto-wise Ranking Classifier for Multi-objective Evolutionary Neural Architecture Search

no code implementations14 Sep 2021 Lianbo Ma, Nan Li, Guo Yu, Xiaoyu Geng, Min Huang, Xingwei Wang

In the deployment of deep neural models, how to effectively and automatically find feasible deep models under diverse design objectives is fundamental.

Neural Architecture Search

Tiny Adversarial Mulit-Objective Oneshot Neural Architecture Search

no code implementations28 Feb 2021 Guoyang Xie, Jinbao Wang, Guo Yu, Feng Zheng, Yaochu Jin

Our work focuses on how to improve the robustness of tiny neural networks without seriously deteriorating of clean accuracy under mobile-level resources.

Neural Architecture Search

Landau Quantization and Highly Mobile Fermions in an Insulator

no code implementations12 Oct 2020 Pengjie Wang, Guo Yu, Yanyu Jia, Michael Onyszczak, F. Alexandre Cevallos, Shiming Lei, Sebastian Klemenz, Kenji Watanabe, Takashi Taniguchi, Robert J. Cava, Leslie M. Schoop, Sanfeng Wu

Using a detection scheme that avoids edge contributions, we uncover strikingly large quantum oscillations in the monolayer insulator's magnetoresistance, with an onset field as small as ~ 0. 5 tesla.

Mesoscale and Nanoscale Physics Materials Science Strongly Correlated Electrons

Consistency-based Semi-supervised Active Learning: Towards Minimizing Labeling Cost

no code implementations ECCV 2020 Mingfei Gao, Zizhao Zhang, Guo Yu, Sercan O. Arik, Larry S. Davis, Tomas Pfister

Active learning (AL) combines data labeling and model training to minimize the labeling cost by prioritizing the selection of high value data that can best improve model performance.

Active Learning Image Classification +1

Consistency-Based Semi-Supervised Active Learning: Towards Minimizing Labeling Budget

no code implementations25 Sep 2019 Mingfei Gao, Zizhao Zhang, Guo Yu, Sercan O. Arik, Larry S. Davis, Tomas Pfister

Active learning (AL) aims to integrate data labeling and model training in a unified way, and to minimize the labeling budget by prioritizing the selection of high value data that can best improve model performance.

Active Learning Representation Learning

Estimating the error variance in a high-dimensional linear model

no code implementations6 Dec 2017 Guo Yu, Jacob Bien

In this paper, we propose the natural lasso estimator for the error variance, which maximizes a penalized likelihood objective.

Vocal Bursts Intensity Prediction

Learning Local Dependence In Ordered Data

no code implementations25 Apr 2016 Guo Yu, Jacob Bien

Penalized maximum likelihood estimation of this matrix yields a simple regression interpretation for local dependence in which variables are predicted by their neighbors.

regression

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