Search Results for author: C. L. Philip Chen

Found 17 papers, 3 papers with code

Broad Recommender System: An Efficient Nonlinear Collaborative Filtering Approach

1 code implementation20 Apr 2022 Ling Huang, Can-Rong Guan, Zhen-Wei Huang, Yuefang Gao, Yingjie Kuang, Chang-Dong Wang, C. L. Philip Chen

Recently, Deep Neural Networks (DNNs) have been widely introduced into Collaborative Filtering (CF) to produce more accurate recommendation results due to their capability of capturing the complex nonlinear relationships between items and users. However, the DNNs-based models usually suffer from high computational complexity, i. e., consuming very long training time and storing huge amount of trainable parameters.

Collaborative Filtering Recommendation Systems

A Novel Multi-Task Learning Method for Symbolic Music Emotion Recognition

no code implementations15 Jan 2022 Jibao Qiu, C. L. Philip Chen, Tong Zhang

In this paper, we present a simple multi-task framework for SMER, which incorporates the emotion recognition task with other emotion-related auxiliary tasks derived from the intrinsic structure of the music.

Emotion Recognition Language Modelling +2

OneDConv: Generalized Convolution For Transform-Invariant Representation

no code implementations15 Jan 2022 Tong Zhang, Haohan Weng, Ke Yi, C. L. Philip Chen

Convolutional Neural Networks (CNNs) have exhibited their great power in a variety of vision tasks.

A Survey on Masked Facial Detection Methods and Datasets for Fighting Against COVID-19

no code implementations13 Jan 2022 Bingshu Wang, Jiangbin Zheng, C. L. Philip Chen

Representative algorithms are described in detail, coupled with some typical techniques that are described briefly.

Lesion Segmentation

Graph Representation Learning via Contrasting Cluster Assignments

no code implementations15 Dec 2021 ChunYang Zhang, Hongyu Yao, C. L. Philip Chen, Yuena Lin

With the rise of contrastive learning, unsupervised graph representation learning has been booming recently, even surpassing the supervised counterparts in some machine learning tasks.

Contrastive Learning Graph Representation Learning

Stacked BNAS: Rethinking Broad Convolutional Neural Network for Neural Architecture Search

no code implementations15 Nov 2021 Zixiang Ding, Yaran Chen, Nannan Li, Dongbin Zhao, C. L. Philip Chen

Moreover, multi-scale feature fusion and knowledge embedding are proposed to improve the performance of BCNN with shallow topology.

Neural Architecture Search

Siamese Labels Auxiliary Learning

no code implementations27 Feb 2021 Wenrui Gan, Zhulin Liu, C. L. Philip Chen, Tong Zhang

In general, the main work of this paper include: (1) propose SiLa Learning, which improves the performance of common models without increasing test parameters; (2) compares SiLa with DML and proves that SiLa can improve the generalization of the model; (3) SiLa is applied to Dynamic Neural Networks, and proved that SiLa can be used for various types of network structures.

Auxiliary Learning

Towards Uncovering the Intrinsic Data Structures for Unsupervised Domain Adaptation using Structurally Regularized Deep Clustering

2 code implementations8 Dec 2020 Hui Tang, Xiatian Zhu, Ke Chen, Kui Jia, C. L. Philip Chen

To address this issue, we are motivated by a UDA assumption of structural similarity across domains, and propose to directly uncover the intrinsic target discrimination via constrained clustering, where we constrain the clustering solutions using structural source regularization that hinges on the very same assumption.

Deep Clustering Image Classification +2

Modal Regression based Structured Low-rank Matrix Recovery for Multi-view Learning

no code implementations22 Mar 2020 Jiamiao Xu, Fangzhao Wang, Qinmu Peng, Xinge You, Shuo Wang, Xiao-Yuan Jing, C. L. Philip Chen

Furthermore, recent low-rank modeling provides a satisfactory solution to address data contaminated by predefined assumptions of noise distribution, such as Gaussian or Laplacian distribution.

MULTI-VIEW LEARNING Representation Learning

BNAS:An Efficient Neural Architecture Search Approach Using Broad Scalable Architecture

no code implementations18 Jan 2020 Zixiang Ding, Yaran Chen, Nannan Li, Dongbin Zhao, Zhiquan Sun, C. L. Philip Chen

In this paper, we propose Broad Neural Architecture Search (BNAS) where we elaborately design broad scalable architecture dubbed Broad Convolutional Neural Network (BCNN) to solve the above issue.

Neural Architecture Search reinforcement-learning

Geometry-Aware Generation of Adversarial Point Clouds

2 code implementations24 Dec 2019 Yuxin Wen, Jiehong Lin, Ke Chen, C. L. Philip Chen, Kui Jia

Regularizing the targeted attack loss with our proposed geometry-aware objectives results in our proposed method, Geometry-Aware Adversarial Attack ($GeoA^3$).

Adversarial Attack Fairness

Multi Pseudo Q-learning Based Deterministic Policy Gradient for Tracking Control of Autonomous Underwater Vehicles

no code implementations7 Sep 2019 Wenjie Shi, Shiji Song, Cheng Wu, C. L. Philip Chen

Different from existing policy gradient methods which employ single actor-critic but cannot realize satisfactory tracking control accuracy and stable learning, our proposed algorithm can achieve high-level tracking control accuracy of AUVs and stable learning by applying a hybrid actors-critics architecture, where multiple actors and critics are trained to learn a deterministic policy and action-value function, respectively.

Policy Gradient Methods Q-Learning

3DViewGraph: Learning Global Features for 3D Shapes from A Graph of Unordered Views with Attention

no code implementations17 May 2019 Zhizhong Han, Xiyang Wang, Chi-Man Vong, Yu-Shen Liu, Matthias Zwicker, C. L. Philip Chen

Then, the content and spatial information of each pair of view nodes are encoded by a novel spatial pattern correlation, where the correlation is computed among latent semantic patterns.

Multi-view Hybrid Embedding: A Divide-and-Conquer Approach

no code implementations19 Apr 2018 Jiamiao Xu, Shujian Yu, Xinge You, Mengjun Leng, Xiao-Yuan Jing, C. L. Philip Chen

We present a novel cross-view classification algorithm where the gallery and probe data come from different views.

Classification General Classification

A Many-Objective Evolutionary Algorithm With Two Interacting Processes: Cascade Clustering and Reference Point Incremental Learning

no code implementations3 Mar 2018 Hongwei Ge, Mingde Zhao, Liang Sun, Zhen Wang, Guozhen Tan, Qiang Zhang, C. L. Philip Chen

This paper proposes a many-objective optimization algorithm with two interacting processes: cascade clustering and reference point incremental learning (CLIA).

Incremental Learning

Broad Learning System: An Effective and Efficient Incremental Learning System Without the Need for Deep Architecture

no code implementations IEEE Transactions on Neural Networks and Learning Systems 2017 C. L. Philip Chen, Zhulin Liu

The BLS is established in the form of a flat network, where the original inputs are transferred and placed as “mapped features” in feature nodes and the structure is expanded in wide sense in the “enhancement nodes.” The incremental learning algorithms are developed for fast remodeling in broad expansion without a retraining process if the network deems to be expanded.

Incremental Learning Object Recognition

Approximation learning methods of Harmonic Mappings in relation to Hardy Spaces

no code implementations24 May 2017 Zhulin Liu, C. L. Philip Chen

A new Hardy space Hardy space approach of Dirichlet type problem based on Tikhonov regularization and Reproducing Hilbert kernel space is discussed in this paper, which turns out to be a typical extremal problem located on the upper upper-high complex plane.

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