Search Results for author: Yew-Soon Ong

Found 56 papers, 19 papers with code

HPCR: Holistic Proxy-based Contrastive Replay for Online Continual Learning

1 code implementation26 Sep 2023 Huiwei Lin, Shanshan Feng, Baoquan Zhang, Xutao Li, Yew-Soon Ong, Yunming Ye

Inspired by this finding, we propose a novel replay-based method called proxy-based contrastive replay (PCR), which replaces anchor-to-sample pairs with anchor-to-proxy pairs in the contrastive-based loss to alleviate the phenomenon of forgetting.

Continual Learning

Neural Influence Estimator: Towards Real-time Solutions to Influence Blocking Maximization

no code implementations27 Aug 2023 Wenjie Chen, Shengcai Liu, Yew-Soon Ong, Ke Tang

Moreover, given a real-time constraint of one minute, the NIE-based method can solve IBM problems with up to hundreds of thousands of nodes, which is at least one order of magnitude larger than what can be solved by existing methods.

Blocking Misinformation

Meta-learning enhanced next POI recommendation by leveraging check-ins from auxiliary cities

1 code implementation18 Aug 2023 Jinze Wang, Lu Zhang, Zhu Sun, Yew-Soon Ong

Particularly, a city-level correlation strategy is devised to attentively capture common patterns among cities, so as to transfer more relevant knowledge from more correlated cities.


Prompt Evolution for Generative AI: A Classifier-Guided Approach

no code implementations24 May 2023 Melvin Wong, Yew-Soon Ong, Abhishek Gupta, Kavitesh K. Bali, Caishun Chen

Synthesis of digital artifacts conditioned on user prompts has become an important paradigm facilitating an explosion of use cases with generative AI.

Bayesian Federated Learning: A Survey

no code implementations26 Apr 2023 Longbing Cao, Hui Chen, Xuhui Fan, Joao Gama, Yew-Soon Ong, Vipin Kumar

This survey presents a critical overview of BFL, including its basic concepts, its relations to Bayesian learning in the context of FL, and a taxonomy of BFL from both Bayesian and federated perspectives.

Federated Learning Privacy Preserving

Policy Dispersion in Non-Markovian Environment

no code implementations28 Feb 2023 Bohao Qu, Xiaofeng Cao, Jielong Yang, Hechang Chen, Chang Yi, Ivor W. Tsang, Yew-Soon Ong

To resolve this problem, this paper tries to learn the diverse policies from the history of state-action pairs under a non-Markovian environment, in which a policy dispersion scheme is designed for seeking diverse policy representation.

LSA-PINN: Linear Boundary Connectivity Loss for Solving PDEs on Complex Geometry

no code implementations3 Feb 2023 Jian Cheng Wong, Pao-Hsiung Chiu, Chinchun Ooi, My Ha Dao, Yew-Soon Ong

On the other hand, if the samples are too sparse, existing PINNs tend to overfit the near boundary region, leading to incorrect solution.

Not All Neighbors Are Worth Attending to: Graph Selective Attention Networks for Semi-supervised Learning

no code implementations14 Oct 2022 Tiantian He, Haicang Zhou, Yew-Soon Ong, Gao Cong

We further propose Graph selective attention networks (SATs) to learn representations from the highly correlated node features identified and investigated by different SA mechanisms.

Graph Attention

A Multi-Channel Next POI Recommendation Framework with Multi-Granularity Check-in Signals

1 code implementation1 Sep 2022 Zhu Sun, Yu Lei, Lu Zhang, Chen Li, Yew-Soon Ong, Jie Zhang

Being equipped with three modules (i. e., global user behavior encoder, local multi-channel encoder, and region-aware weighting strategy), MCMG is capable of capturing both fine- and coarse-grained sequential regularities as well as exploring the dynamic impact of multi-channel by differentiating the region check-in patterns.

Understanding Diversity in Session-Based Recommendation

1 code implementation29 Aug 2022 Qing Yin, Hui Fang, Zhu Sun, Yew-Soon Ong

Besides the "trade-off" relationship, they might be positively correlated with each other, that is, having a same-trend (win-win or lose-lose) relationship, which varies across different methods and datasets.

Session-Based Recommendations

DaisyRec 2.0: Benchmarking Recommendation for Rigorous Evaluation

2 code implementations22 Jun 2022 Zhu Sun, Hui Fang, Jie Yang, Xinghua Qu, Hongyang Liu, Di Yu, Yew-Soon Ong, Jie Zhang

Recently, one critical issue looms large in the field of recommender systems -- there are no effective benchmarks for rigorous evaluation -- which consequently leads to unreproducible evaluation and unfair comparison.

Benchmarking Recommendation Systems

Geo-Neus: Geometry-Consistent Neural Implicit Surfaces Learning for Multi-view Reconstruction

1 code implementation31 May 2022 Qiancheng Fu, Qingshan Xu, Yew-Soon Ong, Wenbing Tao

Recently, neural implicit surfaces learning by volume rendering has become popular for multi-view reconstruction.

Surface Reconstruction

Jacobian Granger Causal Neural Networks for Analysis of Stationary and Nonstationary Data

no code implementations19 May 2022 Suryadi, Yew-Soon Ong, Lock Yue Chew

Granger causality is a commonly used method for uncovering information flow and dependencies in a time series.

Time Series Time Series Analysis

A Survey on AI Sustainability: Emerging Trends on Learning Algorithms and Research Challenges

no code implementations8 May 2022 Zhenghua Chen, Min Wu, Alvin Chan, XiaoLi Li, Yew-Soon Ong

We believe that this technical review can help to promote a sustainable development of AI R&D activities for the research community.


Multitask Neuroevolution for Reinforcement Learning with Long and Short Episodes

no code implementations21 Mar 2022 Nick Zhang, Abhishek Gupta, Zefeng Chen, Yew-Soon Ong

This paper is the first to address the shortcoming of today's methods via a novel neuroevolutionary multitasking (NuEMT) algorithm, designed to transfer information from a set of auxiliary tasks (of short episode length) to the target (full length) RL task at hand.

Continuous Control OpenAI Gym +2

Learning Multi-Task Gaussian Process Over Heterogeneous Input Domains

no code implementations25 Feb 2022 Haitao Liu, Kai Wu, Yew-Soon Ong, Chao Bian, Xiaomo Jiang, Xiaofang Wang

Multi-task Gaussian process (MTGP) is a well-known non-parametric Bayesian model for learning correlated tasks effectively by transferring knowledge across tasks.

Dimensionality Reduction Inductive Bias

CAN-PINN: A Fast Physics-Informed Neural Network Based on Coupled-Automatic-Numerical Differentiation Method

no code implementations29 Oct 2021 Pao-Hsiung Chiu, Jian Cheng Wong, Chinchun Ooi, My Ha Dao, Yew-Soon Ong

In this study, novel physics-informed neural network (PINN) methods for coupling neighboring support points and their derivative terms which are obtained by automatic differentiation (AD), are proposed to allow efficient training with improved accuracy.

Synthesising Audio Adversarial Examples for Automatic Speech Recognition

no code implementations29 Sep 2021 Xinghua Qu, Pengfei Wei, Mingyong Gao, Zhu Sun, Yew-Soon Ong, Zejun Ma

Adversarial examples in automatic speech recognition (ASR) are naturally sounded by humans yet capable of fooling well trained ASR models to transcribe incorrectly.

Automatic Speech Recognition Automatic Speech Recognition (ASR) +1

Half a Dozen Real-World Applications of Evolutionary Multitasking, and More

no code implementations27 Sep 2021 Abhishek Gupta, Lei Zhou, Yew-Soon Ong, Zefeng Chen, Yaqing Hou

Until recently, the potential to transfer evolved skills across distinct optimization problem instances (or tasks) was seldom explored in evolutionary computation.

Learning in Sinusoidal Spaces with Physics-Informed Neural Networks

no code implementations20 Sep 2021 Jian Cheng Wong, Chinchun Ooi, Abhishek Gupta, Yew-Soon Ong

In this paper, we present a novel perspective of the merits of learning in sinusoidal spaces with PINNs.

Word2Pix: Word to Pixel Cross Attention Transformer in Visual Grounding

no code implementations31 Jul 2021 Heng Zhao, Joey Tianyi Zhou, Yew-Soon Ong

Current one-stage methods for visual grounding encode the language query as one holistic sentence embedding before fusion with visual feature.

Sentence Embedding Sentence-Embedding +1

Multi-Party Dual Learning

no code implementations14 Apr 2021 Maoguo Gong, Yuan Gao, Yu Xie, A. K. Qin, Ke Pan, Yew-Soon Ong

The performance of machine learning algorithms heavily relies on the availability of a large amount of training data.

BIG-bench Machine Learning Self-Learning

RNA Alternative Splicing Prediction with Discrete Compositional Energy Network

2 code implementations7 Mar 2021 Alvin Chan, Anna Korsakova, Yew-Soon Ong, Fernaldo Richtia Winnerdy, Kah Wai Lim, Anh Tuan Phan

In the case of alternative splicing prediction, DCEN models mRNA transcript probabilities through its constituent splice junctions' energy values.

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

Learning Conjoint Attentions for Graph Neural Nets

1 code implementation NeurIPS 2021 Tiantian He, Yew-Soon Ong, Lu Bai

Given the novel Conjoint Attention strategies, we then propose Graph conjoint attention networks (CATs) that can learn representations embedded with significant latent features deemed by the Conjoint Attentions.

Benchmarking Graph Attention

Can Transfer Neuroevolution Tractably Solve Your Differential Equations?

no code implementations6 Jan 2021 Jian Cheng Wong, Abhishek Gupta, Yew-Soon Ong

In the context of solving differential equations, we are faced with the problem of finding globally optimum parameters of the network, instead of being concerned with out-of-sample generalization.

Scalable Transfer Evolutionary Optimization: Coping with Big Task Instances

1 code implementation3 Dec 2020 Mojtaba Shakeri, Erfan Miahi, Abhishek Gupta, Yew-Soon Ong

Under such settings, existing transfer evolutionary optimization frameworks grapple with simultaneously satisfying two important quality attributes, namely (1) scalability against a growing number of source tasks and (2) online learning agility against sparsity of relevant sources to the target task of interest.

Graph Joint Attention Networks

no code implementations28 Sep 2020 Tiantian He, Lu Bai, Yew-Soon Ong

In this paper, we propose Graph Joint Attention Networks (JATs) to address the aforementioned challenge.

Benchmarking Graph Attention +1

Modulating Scalable Gaussian Processes for Expressive Statistical Learning

1 code implementation29 Aug 2020 Haitao Liu, Yew-Soon Ong, Xiaomo Jiang, Xiaofang Wang

For a learning task, Gaussian process (GP) is interested in learning the statistical relationship between inputs and outputs, since it offers not only the prediction mean but also the associated variability.

Gaussian Processes Variational Inference

Adversary Agnostic Robust Deep Reinforcement Learning

no code implementations14 Aug 2020 Xinghua Qu, Yew-Soon Ong, Abhishek Gupta, Zhu Sun

Motivated by this finding, we propose a new policy distillation loss with two terms: 1) a prescription gap maximization loss aiming at simultaneously maximizing the likelihood of the action selected by the teacher policy and the entropy over the remaining actions; 2) a corresponding Jacobian regularization loss that minimizes the magnitude of the gradient with respect to the input state.

Adversarial Robustness Atari Games +2

CoCon: A Self-Supervised Approach for Controlled Text Generation

1 code implementation ICLR 2021 Alvin Chan, Yew-Soon Ong, Bill Pung, Aston Zhang, Jie Fu

While there are studies that seek to control high-level attributes (such as sentiment and topic) of generated text, there is still a lack of more precise control over its content at the word- and phrase-level.

Text Generation

Deep Latent-Variable Kernel Learning

1 code implementation18 May 2020 Haitao Liu, Yew-Soon Ong, Xiaomo Jiang, Xiaofang Wang

Deep kernel learning (DKL) leverages the connection between Gaussian process (GP) and neural networks (NN) to build an end-to-end, hybrid model.

Heterogeneous Representation Learning: A Review

no code implementations28 Apr 2020 Joey Tianyi Zhou, Xi Peng, Yew-Soon Ong

The real-world data usually exhibits heterogeneous properties such as modalities, views, or resources, which brings some unique challenges wherein the key is Heterogeneous Representation Learning (HRL) termed in this paper.

Multi-Task Learning MULTI-VIEW LEARNING +1

What it Thinks is Important is Important: Robustness Transfers through Input Gradients

2 code implementations CVPR 2020 Alvin Chan, Yi Tay, Yew-Soon Ong

Learned weights of models robust to such perturbations are previously found to be transferable across different tasks but this applies only if the model architecture for the source and target tasks is the same.

Adversarial Robustness

Poison as a Cure: Detecting & Neutralizing Variable-Sized Backdoor Attacks in Deep Neural Networks

no code implementations19 Nov 2019 Alvin Chan, Yew-Soon Ong

Existing defenses are effective under certain conditions such as a small size of the poison pattern, knowledge about the ratio of poisoned training samples or when a validated clean dataset is available.

A Multi-Task Gradient Descent Method for Multi-Label Learning

no code implementations18 Nov 2019 Lu Bai, Yew-Soon Ong, Tiantian He, Abhishek Gupta

Multi-label learning studies the problem where an instance is associated with a set of labels.

Multi-Label Learning

Scalable Gaussian Process Classification with Additive Noise for Various Likelihoods

1 code implementation14 Sep 2019 Haitao Liu, Yew-Soon Ong, Ziwei Yu, Jianfei Cai, Xiaobo Shen

Gaussian process classification (GPC) provides a flexible and powerful statistical framework describing joint distributions over function space.

Classification General Classification +3

A Survey on Multi-output Learning

no code implementations2 Jan 2019 Donna Xu, Yaxin Shi, Ivor W. Tsang, Yew-Soon Ong, Chen Gong, Xiaobo Shen

Multi-output learning aims to simultaneously predict multiple outputs given an input.

Decision Making

AIR5: Five Pillars of Artificial Intelligence Research

no code implementations30 Dec 2018 Yew-Soon Ong, Abhishek Gupta

In this article, we provide and overview of what we consider to be some of the most pressing research questions facing the fields of artificial intelligence (AI) and computational intelligence (CI); with the latter focusing on algorithms that are inspired by various natural phenomena.

Artificial Life

Towards Safer Smart Contracts: A Sequence Learning Approach to Detecting Vulnerabilities

1 code implementation16 Nov 2018 Wesley Joon-Wie Tann, Xing Jie Han, Sourav Sen Gupta, Yew-Soon Ong

In particular, we propose a novel approach of sequential learning of smart contract vulnerabilities using machine learning --- long-short term memory (LSTM) --- that perpetually learns from an increasing number of contracts handled over time, leading to safer smart contracts.

Cryptography and Security

Understanding and Comparing Scalable Gaussian Process Regression for Big Data

no code implementations3 Nov 2018 Haitao Liu, Jianfei Cai, Yew-Soon Ong, Yi Wang

This paper devotes to investigating the methodological characteristics and performance of representative global and local scalable GPs including sparse approximations and local aggregations from four main perspectives: scalability, capability, controllability and robustness.


Large-scale Heteroscedastic Regression via Gaussian Process

no code implementations3 Nov 2018 Haitao Liu, Yew-Soon Ong, Jianfei Cai

To improve the scalability, we first develop a variational sparse inference algorithm, named VSHGP, to handle large-scale datasets.

regression Variational Inference

When Gaussian Process Meets Big Data: A Review of Scalable GPs

no code implementations3 Jul 2018 Haitao Liu, Yew-Soon Ong, Xiaobo Shen, Jianfei Cai

The review of scalable GPs in the GP community is timely and important due to the explosion of data size.

Generalized Robust Bayesian Committee Machine for Large-scale Gaussian Process Regression

1 code implementation ICML 2018 Haitao Liu, Jianfei Cai, Yi Wang, Yew-Soon Ong

In order to scale standard Gaussian process (GP) regression to large-scale datasets, aggregation models employ factorized training process and then combine predictions from distributed experts.

Distributed Computing regression

Evolutionary Multitasking for Single-objective Continuous Optimization: Benchmark Problems, Performance Metric, and Baseline Results

no code implementations12 Jun 2017 Bingshui Da, Yew-Soon Ong, Liang Feng, A. K. Qin, Abhishek Gupta, Zexuan Zhu, Chuan-Kang Ting, Ke Tang, Xin Yao

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

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

Co-evolutionary multi-task learning for dynamic time series prediction

1 code implementation27 Feb 2017 Rohitash Chandra, Yew-Soon Ong, Chi-Keong Goh

In this paper, we propose a co-evolutionary multi-task learning method that provides a synergy between multi-task learning and co-evolutionary algorithms to address dynamic time series prediction.

Evolutionary Algorithms Multi-Task Learning +2

Genetic Transfer or Population Diversification? Deciphering the Secret Ingredients of Evolutionary Multitask Optimization

no code implementations19 Jul 2016 Abhishek Gupta, Yew-Soon Ong

Evolutionary multitasking has recently emerged as a novel paradigm that enables the similarities and/or latent complementarities (if present) between distinct optimization tasks to be exploited in an autonomous manner simply by solving them together with a unified solution representation scheme.

Adaptive Subgradient Methods for Online AUC Maximization

no code implementations1 Feb 2016 Yi Ding, Peilin Zhao, Steven C. H. Hoi, Yew-Soon Ong

Despite their encouraging results reported, the existing online AUC maximization algorithms often adopt simple online gradient descent approaches that fail to exploit the geometrical knowledge of the data observed during the online learning process, and thus could suffer from relatively larger regret.

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