Search Results for author: Kay Chen Tan

Found 47 papers, 11 papers with code

AS-LLM: When Algorithm Selection Meets Large Language Model

no code implementations22 Nov 2023 Xingyu Wu, Yan Zhong, Jibin Wu, Kay Chen Tan

Following the extraction of embedding vectors for both algorithms and problems, the most suitable algorithm is determined through calculations of matching degrees.

AutoML Language Modelling +1

SE Territory: Monaural Speech Enhancement Meets the Fixed Virtual Perceptual Space Mapping

no code implementations3 Nov 2023 Xinmeng Xu, Jibin Wu, XiaoYong Wei, Yan Liu, Richard So, Yuhong Yang, Weiping tu, Kay Chen Tan

To overcome this limitation, we introduce a strategy to map monaural speech into a fixed simulation space for better differentiation between target speech and noise.

Multi-Task Learning Speech Enhancement

LC-TTFS: Towards Lossless Network Conversion for Spiking Neural Networks with TTFS Coding

no code implementations23 Oct 2023 Qu Yang, Malu Zhang, Jibin Wu, Kay Chen Tan, Haizhou Li

With TTFS coding, we can achieve up to orders of magnitude saving in computation over ANN and other rate-based SNNs.

Edge-computing Image Classification +2

Solving Expensive Optimization Problems in Dynamic Environments with Meta-learning

no code implementations19 Oct 2023 huan zhang, Jinliang Ding, Liang Feng, Kay Chen Tan, Ke Li

Although data-driven evolutionary optimization and Bayesian optimization (BO) approaches have shown promise in solving expensive optimization problems in static environments, the attempts to develop such approaches in dynamic environments remain rarely unexplored.

Bayesian Optimization Meta-Learning

Typing to Listen at the Cocktail Party: Text-Guided Target Speaker Extraction

1 code implementation11 Oct 2023 Xiang Hao, Jibin Wu, Jianwei Yu, Chenglin Xu, Kay Chen Tan

However, the effectiveness of these models is hindered in real-world scenarios due to the unreliable or even absence of pre-registered cues.

Language Modelling Large Language Model +1

Unleashing the Potential of Spiking Neural Networks for Sequential Modeling with Contextual Embedding

no code implementations29 Aug 2023 Xinyi Chen, Jibin Wu, Huajin Tang, Qinyuan Ren, Kay Chen Tan

The human brain exhibits remarkable abilities in integrating temporally distant sensory inputs for decision-making.

Decision Making

TC-LIF: A Two-Compartment Spiking Neuron Model for Long-term Sequential Modelling

no code implementations25 Aug 2023 Shimin Zhang, Qu Yang, Chenxiang Ma, Jibin Wu, Haizhou Li, Kay Chen Tan

The identification of sensory cues associated with potential opportunities and dangers is frequently complicated by unrelated events that separate useful cues by long delays.

Long Short-term Memory with Two-Compartment Spiking Neuron

no code implementations14 Jul 2023 Shimin Zhang, Qu Yang, Chenxiang Ma, Jibin Wu, Haizhou Li, Kay Chen Tan

The identification of sensory cues associated with potential opportunities and dangers is frequently complicated by unrelated events that separate useful cues by long delays.

SoftGPT: Learn Goal-oriented Soft Object Manipulation Skills by Generative Pre-trained Heterogeneous Graph Transformer

1 code implementation22 Jun 2023 Junjia Liu, Zhihao LI, WanYu Lin, Sylvain Calinon, Kay Chen Tan, Fei Chen

Soft object manipulation tasks in domestic scenes pose a significant challenge for existing robotic skill learning techniques due to their complex dynamics and variable shape characteristics.

A Hybrid Neural Coding Approach for Pattern Recognition with Spiking Neural Networks

no code implementations26 May 2023 Xinyi Chen, Qu Yang, Jibin Wu, Haizhou Li, Kay Chen Tan

The biological neural systems evolved to adapt to ecological environment for efficiency and effectiveness, wherein neurons with heterogeneous structures and rich dynamics are optimized to accomplish complex cognitive tasks.

A Scalable Test Problem Generator for Sequential Transfer Optimization

2 code implementations17 Apr 2023 Xiaoming Xue, Cuie Yang, Liang Feng, Kai Zhang, Linqi Song, Kay Chen Tan

Lastly, a benchmark suite with 12 STO problems featured by a variety of customized similarity relationships is developed using the proposed generator.


A Survey on Distributed Evolutionary Computation

no code implementations12 Apr 2023 Wei-neng Chen, Feng-Feng Wei, Tian-Fang Zhao, Kay Chen Tan, Jun Zhang

Based on this taxonomy, existing studies on DEC are reviewed in terms of purpose, parallel structure of the algorithm, parallel model for implementation, and the implementation environment.

Distributed Computing Distributed Optimization

A Recommender System Approach for Very Large-scale Multiobjective Optimization

no code implementations8 Apr 2023 Haokai Hong, Min Jiang, Jonathan M. Garibaldi, Qiuzhen Lin, Kay Chen Tan

The idea of the proposed method is to transform the defined such very large-scale problems into a problem that can be tackled by a recommender system.

Multiobjective Optimization Recommendation Systems +1

EvoX: A Distributed GPU-accelerated Framework for Scalable Evolutionary Computation

1 code implementation29 Jan 2023 Beichen Huang, Ran Cheng, Zhuozhao Li, Yaochu Jin, Kay Chen Tan

At the core of EvoX lies a functional programming model that simplifies the development of parallelized EC algorithms, seamlessly integrated with a high-performance computation model designed specifically for distributed GPU-accelerated execution.

Navigate OpenAI Gym

Differentiable Search of Accurate and Robust Architectures

no code implementations28 Dec 2022 Yuwei Ou, Xiangning Xie, Shangce Gao, Yanan sun, Kay Chen Tan, Jiancheng Lv

Deep neural networks (DNNs) are found to be vulnerable to adversarial attacks, and various methods have been proposed for the defense.

An Evolutionary Multitasking Algorithm with Multiple Filtering for High-Dimensional Feature Selection

1 code implementation17 Dec 2022 Lingjie Li, Manlin Xuan, Qiuzhen Lin, Min Jiang, Zhong Ming, Kay Chen Tan

Thus, this paper devises a new EMT algorithm for FS in high-dimensional classification, which first adopts different filtering methods to produce multiple tasks and then modifies a competitive swarm optimizer to efficiently solve these related tasks via knowledge transfer.

feature selection Transfer Learning

Neural Architecture Search as Multiobjective Optimization Benchmarks: Problem Formulation and Performance Assessment

1 code implementation8 Aug 2022 Zhichao Lu, Ran Cheng, Yaochu Jin, Kay Chen Tan, Kalyanmoy Deb

From an optimization point of view, the NAS tasks involving multiple design criteria are intrinsically multiobjective optimization problems; hence, it is reasonable to adopt evolutionary multiobjective optimization (EMO) algorithms for tackling them.

Multiobjective Optimization Neural Architecture Search +1

Architecture Augmentation for Performance Predictor Based on Graph Isomorphism

no code implementations3 Jul 2022 Xiangning Xie, Yuqiao Liu, Yanan sun, Mengjie Zhang, Kay Chen Tan

Performance predictors can greatly alleviate the prohibitive cost of NAS by directly predicting the performance of DNNs.

Neural Architecture Search

A Survey on Learnable Evolutionary Algorithms for Scalable Multiobjective Optimization

no code implementations23 Jun 2022 Songbai Liu, Qiuzhen Lin, Jianqiang Li, Kay Chen Tan

This paper begins with a general taxonomy of scaling-up MOPs and learnable MOEAs, followed by an analysis of the challenges that these MOPs pose to traditional MOEAs.

Evolutionary Algorithms Multiobjective Optimization

Balancing Exploration and Exploitation for Solving Large-scale Multiobjective Optimization via Attention Mechanism

no code implementations20 May 2022 Haokai Hong, Min Jiang, Liang Feng, Qiuzhen Lin, Kay Chen Tan

However, these algorithms ignore the significance of tackling this issue from the perspective of decision variables, which makes the algorithm lack the ability to search from different dimensions and limits the performance of the algorithm.

Evolutionary Algorithms Multiobjective Optimization

Crowd Counting in the Frequency Domain

1 code implementation CVPR 2022 Weibo Shu, Jia Wan, Kay Chen Tan, Sam Kwong, Antoni B. Chan

By transforming the density map into the frequency domain and using the nice properties of the characteristic function, we propose a novel method that is simple, effective, and efficient.

Crowd Counting

Benchmark Problems for CEC2021 Competition on Evolutionary Transfer Multiobjectve Optimization

1 code implementation15 Oct 2021 Songbai Liu, Qiuzhen Lin, Kay Chen Tan, Qing Li

Evolutionary transfer multiobjective optimization (ETMO) has been becoming a hot research topic in the field of evolutionary computation, which is based on the fact that knowledge learning and transfer across the related optimization exercises can improve the efficiency of others.

Multiobjective Optimization Transfer Learning

Solving Large-Scale Multi-Objective Optimization via Probabilistic Prediction Model

no code implementations16 Jul 2021 Haokai Hong, Kai Ye, Min Jiang, Donglin Cao, Kay Chen Tan

At the same time, due to the adoption of an individual-based evolution mechanism, the computational cost of the proposed method is independent of the number of decision variables, thus avoiding the problem of exponential growth of the search space.

Principled Design of Translation, Scale, and Rotation Invariant Variation Operators for Metaheuristics

no code implementations22 May 2021 Ye Tian, Xingyi Zhang, Cheng He, Kay Chen Tan, Yaochu Jin

In the past three decades, a large number of metaheuristics have been proposed and shown high performance in solving complex optimization problems.


Multi-Space Evolutionary Search for Large-Scale Optimization

no code implementations23 Feb 2021 Liang Feng, Qingxia Shang, Yaqing Hou, Kay Chen Tan, Yew-Soon Ong

This paper thus proposes a new search paradigm, namely the multi-space evolutionary search, to enhance the existing evolutionary search methods for solving large-scale optimization problems.

Dimensionality Reduction Evolutionary Algorithms

Manifold Interpolation for Large-Scale Multi-Objective Optimization via Generative Adversarial Networks

no code implementations8 Jan 2021 Zhenzhong Wang, Haokai Hong, Kai Ye, Min Jiang, Kay Chen Tan

However, traditional evolutionary algorithms for solving LSMOPs have some deficiencies in dealing with this structural manifold, resulting in poor diversity, local optima, and inefficient searches.

Evolutionary Algorithms Multiobjective Optimization

Evolutionary Gait Transfer of Multi-Legged Robots in Complex Terrains

no code implementations24 Dec 2020 Min Jiang, Guokun Chi, Geqiang Pan, Shihui Guo, Kay Chen Tan

Given the high dimensions of control space, this problem is particularly challenging for multi-legged robots walking in complex and unknown environments.

Evolutionary Algorithms Transfer Learning

Progressive Tandem Learning for Pattern Recognition with Deep Spiking Neural Networks

no code implementations2 Jul 2020 Jibin Wu, Cheng-Lin Xu, Daquan Zhou, Haizhou Li, Kay Chen Tan

In this paper, we propose a novel ANN-to-SNN conversion and layer-wise learning framework for rapid and efficient pattern recognition, which is referred to as progressive tandem learning of deep SNNs.

Image Reconstruction Object Recognition +1

Synaptic Learning with Augmented Spikes

no code implementations11 May 2020 Qiang Yu, Shiming Song, Chenxiang Ma, Linqiang Pan, Kay Chen Tan

Traditional neuron models use analog values for information representation and computation, while all-or-nothing spikes are employed in the spiking ones.

Towards Efficient Processing and Learning with Spikes: New Approaches for Multi-Spike Learning

no code implementations2 May 2020 Qiang Yu, Shenglan Li, Huajin Tang, Longbiao Wang, Jianwu Dang, Kay Chen Tan

They are also believed to play an essential role in low-power consumption of the biological systems, whose efficiency attracts increasing attentions to the field of neuromorphic computing.

Understanding the Automated Parameter Optimization on Transfer Learning for CPDP: An Empirical Study

1 code implementation8 Feb 2020 Ke Li, Zilin Xiang, Tao Chen, Shuo Wang, Kay Chen Tan

Given a tight computational budget, it is more cost-effective to focus on optimizing the parameter configuration of transfer learning algorithms (3) The research on CPDP is far from mature where it is "not difficult" to find a better alternative by making a combination of existing transfer learning and classification techniques.

Transfer Learning

Deep Spiking Neural Networks for Large Vocabulary Automatic Speech Recognition

1 code implementation19 Nov 2019 Jibin Wu, Emre Yilmaz, Malu Zhang, Haizhou Li, Kay Chen Tan

The brain-inspired spiking neural networks (SNN) closely mimic the biological neural networks and can operate on low-power neuromorphic hardware with spike-based computation.

Automatic Speech Recognition Automatic Speech Recognition (ASR) +1

Online Bagging for Anytime Transfer Learning

no code implementations20 Oct 2019 Guokun Chi, Min Jiang, Xing Gao, Weizhen Hu, Shihui Guo, Kay Chen Tan

In practical applications, it is often necessary to face online learning problems in which the data samples are achieved sequentially.

Transfer Learning

Solving Dynamic Multi-objective Optimization Problems Using Incremental Support Vector Machine

no code implementations19 Oct 2019 Weizhen Hu, Min Jiang, Xing Gao, Kay Chen Tan, Yiu-ming Cheung

The main feature of the Dynamic Multi-objective Optimization Problems (DMOPs) is that optimization objective functions will change with times or environments.

Evolutionary Algorithms POS +1

Solving dynamic multi-objective optimization problems via support vector machine

no code implementations19 Oct 2019 Min Jiang, Weizhen Hu, Liming Qiu, Minghui Shi, Kay Chen Tan

The algorithm uses the POS that has been obtained to train a SVM and then take the trained SVM to classify the solutions of the dynamic optimization problem at the next moment, and thus it is able to generate an initial population which consists of different individuals recognized by the trained SVM.


Evolutionary Multi-Objective Optimization Driven by Generative Adversarial Networks

no code implementations10 Jul 2019 Cheng He, Shihua Huang, Ran Cheng, Kay Chen Tan, Yaochu Jin

Recently, more and more works have proposed to drive evolutionary algorithms using machine learning models. Usually, the performance of such model based evolutionary algorithms is highly dependent on the training qualities of the adopted models. Since it usually requires a certain amount of data (i. e. the candidate solutions generated by the algorithms) for model training, the performance deteriorates rapidly with the increase of the problem scales, due to the curse of dimensionality. To address this issue, we propose a multi-objective evolutionary algorithm driven by the generative adversarial networks (GANs). At each generation of the proposed algorithm, the parent solutions are first classified into \emph{real} and \emph{fake} samples to train the GANs; then the offspring solutions are sampled by the trained GANs. Thanks to the powerful generative ability of the GANs, our proposed algorithm is capable of generating promising offspring solutions in high-dimensional decision space with limited training data. The proposed algorithm is tested on 10 benchmark problems with up to 200 decision variables. Experimental results on these test problems demonstrate the effectiveness of the proposed algorithm.

Evolutionary Algorithms Test

A Tandem Learning Rule for Effective Training and Rapid Inference of Deep Spiking Neural Networks

1 code implementation2 Jul 2019 Jibin Wu, Yansong Chua, Malu Zhang, Guoqi Li, Haizhou Li, Kay Chen Tan

Spiking neural networks (SNNs) represent the most prominent biologically inspired computing model for neuromorphic computing (NC) architectures.

Event-based vision

Robust Environmental Sound Recognition with Sparse Key-point Encoding and Efficient Multi-spike Learning

no code implementations4 Feb 2019 Qiang Yu, Yanli Yao, Longbiao Wang, Huajin Tang, Jianwu Dang, Kay Chen Tan

Our framework is a unifying system with a consistent integration of three major functional parts which are sparse encoding, efficient learning and robust readout.

Decision Making

Which Surrogate Works for Empirical Performance Modelling? A Case Study with Differential Evolution

no code implementations30 Jan 2019 Ke Li, Zilin Xiang, Kay Chen Tan

Perhaps surprisingly, it is possible to build a cheap-to-evaluate surrogate that models the algorithm's empirical performance as a function of its parameters.


A Multi-State Diagnosis and Prognosis Framework with Feature Learning for Tool Condition Monitoring

no code implementations30 Apr 2018 Chong Zhang, Geok Soon Hong, Jun-Hong Zhou, Kay Chen Tan, Haizhou Li, Huan Xu, Jihoon Hong, Hian-Leng Chan

For fault diagnosis, a cost-sensitive deep belief network (namely ECS-DBN) is applied to deal with the imbalanced data problem for tool state estimation.

Representation Learning

A Cost-Sensitive Deep Belief Network for Imbalanced Classification

no code implementations28 Apr 2018 Chong Zhang, Kay Chen Tan, Haizhou Li, Geok Soon Hong

Adaptive differential evolution optimization is implemented as the optimization algorithm that automatically updates its corresponding parameters without the need of prior domain knowledge.

Classification General Classification +1

Evolutionary Multitasking for Multiobjective Continuous Optimization: Benchmark Problems, Performance Metrics and Baseline Results

no code implementations8 Jun 2017 Yuan Yuan, Yew-Soon Ong, Liang Feng, A. K. Qin, Abhishek Gupta, Bingshui Da, Qingfu Zhang, Kay Chen Tan, Yaochu Jin, Hisao Ishibuchi

In this report, we suggest nine test problems for multi-task multi-objective optimization (MTMOO), each of which consists of two multiobjective optimization tasks that need to be solved simultaneously.

Multiobjective Optimization Test

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