b'Yew Soon Ong'

research

∙ 05/24/2023

Prompt Evolution for Generative AI: A Classifier-Guided Approach

Synthesis of digital artifacts conditioned on user prompts has become an...

0 Melvin Wong, et al. ∙

research

∙ 04/26/2023

Bayesian Federated Learning: A Survey

Federated learning (FL) demonstrates its advantages in integrating distr...

0 Longbing Cao, et al. ∙

research

∙ 04/05/2023

Unfolded Self-Reconstruction LSH: Towards Machine Unlearning in Approximate Nearest Neighbour Search

Approximate nearest neighbour (ANN) search is an essential component of ...

0 Kim Yong Tan, et al. ∙

research

∙ 02/28/2023

Policy Dispersion in Non-Markovian Environment

Markov Decision Process (MDP) presents a mathematical framework to formu...

0 Bohao Qu, et al. ∙

research

∙ 02/03/2023

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

We present a novel loss formulation for efficient learning of complex dy...

0 Jian Cheng Wong, et al. ∙

research

∙ 12/15/2022

Neuroevolution Surpasses Stochastic Gradient Descent for Physics-Informed Neural Networks

The potential of learned models for fundamental scientific research and ...

0 Nicholas Sung Wei Yong, et al. ∙

research

∙ 10/14/2022

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

Graph attention networks (GATs) are powerful tools for analyzing graph d...

0 Tiantian He, et al. ∙

research

∙ 09/01/2022

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

Current study on next POI recommendation mainly explores user sequential...

0 Zhu Sun, et al. ∙

research

∙ 08/29/2022

Understanding Diversity in Session-Based Recommendation

Current session-based recommender systems (SBRSs) mainly focus on maximi...

0 Qing Yin, et al. ∙

research

∙ 06/22/2022

DaisyRec 2.0: Benchmarking Recommendation for Rigorous Evaluation

Recently, one critical issue looms large in the field of recommender sys...

2 Zhu Sun, et al. ∙

research

∙ 06/15/2022

Masked Frequency Modeling for Self-Supervised Visual Pre-Training

We present Masked Frequency Modeling (MFM), a unified frequency-domain-b...

0 Jiahao Xie, et al. ∙

research

∙ 05/31/2022

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

Recently, neural implicit surfaces learning by volume rendering has beco...

0 Qiancheng Fu, et al. ∙

research

∙ 05/19/2022

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

Granger causality is a commonly used method for uncovering information f...

0 Suryadi, et al. ∙

research

∙ 05/09/2022

How Does Frequency Bias Affect the Robustness of Neural Image Classifiers against Common Corruption and Adversarial Perturbations?

Model robustness is vital for the reliable deployment of machine learnin...

0 Alvin Chan, et al. ∙

research

∙ 05/08/2022

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

Artificial Intelligence (AI) is a fast-growing research and development ...

0 Zhenghua Chen, et al. ∙

research

∙ 05/02/2022

Jack and Masters of All Trades: One-Pass Learning of a Set of Model Sets from Foundation AI Models

For deep learning, size is power. Massive neural nets trained on broad d...

9 Han Xiang Choong, et al. ∙

research

∙ 03/21/2022

Lean Evolutionary Reinforcement Learning by Multitasking with Importance Sampling

Studies have shown evolution strategies (ES) to be a promising approach ...

0 Nick Zhang, et al. ∙

research

∙ 02/25/2022

Learning Multi-Task Gaussian Process Over Heterogeneous Input Domains

Multi-task Gaussian process (MTGP) is a well-known non-parametric Bayesi...

2 Haitao Liu, et al. ∙

research

∙ 10/29/2021

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

In this study, novel physics-informed neural network (PINN) methods for ...

39 Pao-Hsiung Chiu, et al. ∙

research

∙ 09/27/2021

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

Until recently, the potential to transfer evolved skills across distinct...

0 Abhishek Gupta, et al. ∙

research

∙ 09/20/2021

Learning in Sinusoidal Spaces with Physics-Informed Neural Networks

A physics-informed neural network (PINN) uses physics-augmented loss fun...

7 Jian Cheng Wong, et al. ∙

research

∙ 07/31/2021

Word2Pix: Word to Pixel Cross Attention Transformer in Visual Grounding

Current one-stage methods for visual grounding encode the language query...

0 Heng Zhao, et al. ∙

research

∙ 06/22/2021

Unsupervised Object-Level Representation Learning from Scene Images

Contrastive self-supervised learning has largely narrowed the gap to sup...

0 Jiahao Xie, et al. ∙

research

∙ 04/14/2021

Multi-Party Dual Learning

The performance of machine learning algorithms heavily relies on the ava...

5 Maoguo Gong, et al. ∙

research

∙ 03/07/2021

RNA Alternative Splicing Prediction with Discrete Compositional Energy Network

A single gene can encode for different protein versions through a proces...

0 Alvin Chan, et al. ∙

research

∙ 02/23/2021

Multi-Space Evolutionary Search for Large-Scale Optimization

In recent years, to improve the evolutionary algorithms used to solve op...

0 Liang Feng, et al. ∙

research

∙ 02/05/2021

Graph Joint Attention Networks

Graph attention networks (GATs) have been recognized as powerful tools f...

0 Tiantian He, et al. ∙

research

∙ 01/06/2021

Can Transfer Neuroevolution Tractably Solve Your Differential Equations?

This paper introduces neuroevolution for solving differential equations....

6 Jian Cheng Wong, et al. ∙

research

∙ 12/03/2020

Scalable Transfer Evolutionary Optimization: Coping with Big Task Instances

In today's digital world, we are confronted with an explosion of data an...

21 Mojtaba Shakeri, et al. ∙

research

∙ 10/06/2020

Poison Attacks against Text Datasets with Conditional Adversarially Regularized Autoencoder

This paper demonstrates a fatal vulnerability in natural language infere...

21 Alvin Chan, et al. ∙

research

∙ 08/29/2020

Modulating Scalable Gaussian Processes for Expressive Statistical Learning

For a learning task, Gaussian process (GP) is interested in learning the...

0 Haitao Liu, et al. ∙

research

∙ 08/26/2020

Delving into Inter-Image Invariance for Unsupervised Visual Representations

Contrastive learning has recently shown immense potential in unsupervise...

0 Jiahao Xie, et al. ∙

research

∙ 08/14/2020

Defending Adversarial Attacks without Adversarial Attacks in Deep Reinforcement Learning

Many recent studies in deep reinforcement learning (DRL) have proposed t...

8 Xinghua Qu, et al. ∙

research

∙ 06/18/2020

Online Deep Clustering for Unsupervised Representation Learning

Joint clustering and feature learning methods have shown remarkable perf...

54 Xiaohang Zhan, et al. ∙

research

∙ 06/05/2020

CoCon: A Self-Supervised Approach for Controlled Text Generation

Pretrained Transformer-based language models (LMs) display remarkable na...

9 Alvin Chan, et al. ∙

research

∙ 05/18/2020

Deep Latent-Variable Kernel Learning

Deep kernel learning (DKL) leverages the connection between Gaussian pro...

24 Haitao Liu, et al. ∙

research

∙ 04/28/2020

Heterogeneous Representation Learning: A Review

The real-world data usually exhibits heterogeneous properties such as mo...

0 Joey Tianyi Zhou, et al. ∙

research

∙ 12/21/2019

Jacobian Adversarially Regularized Networks for Robustness

Adversarial examples are crafted with imperceptible perturbations with t...

15 Alvin Chan, et al. ∙

research

∙ 12/11/2019

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

Adversarial perturbations are imperceptible changes to input pixels that...

27 Alvin Chan, et al. ∙

research

∙ 11/19/2019

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

Deep learning models have recently shown to be vulnerable to backdoor po...

32 Alvin Chan, et al. ∙

research

∙ 11/18/2019

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

Multi-label learning studies the problem where an instance is associated...

0 Lu Bai, et al. ∙

research

∙ 11/10/2019

Minimalistic Attacks: How Little it Takes to Fool a Deep Reinforcement Learning Policy

Recent studies have revealed that neural network-based policies can be e...

0 Xinghua Qu, et al. ∙

research

∙ 10/08/2019

DEVDAN: Deep Evolving Denoising Autoencoder

The Denoising Autoencoder (DAE) enhances the flexibility of the data str...

16 Andri Ashfahani, et al. ∙

research

∙ 10/08/2019

Automatic Construction of Multi-layer Perceptron Network from Streaming Examples

Autonomous construction of deep neural network (DNNs) is desired for dat...

0 Mahardhika Pratama, et al. ∙

research

∙ 09/14/2019

Scalable Gaussian Process Classification with Additive Noise for Various Likelihoods

Gaussian process classification (GPC) provides a flexible and powerful s...

13 Haitao Liu, et al. ∙

research

∙ 01/02/2019

A Survey on Multi-output Learning

Multi-output learning aims to simultaneously predict multiple outputs gi...

38 Donna Xu, et al. ∙

research

∙ 12/30/2018

AIR5: Five Pillars of Artificial Intelligence Research

In this article, we provide and overview of what we consider to be some ...

1 Yew Soon Ong, et al. ∙

research

∙ 11/16/2018

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

Symbolic analysis of security exploits in smart contracts has demonstrat...

0 Wesley J T, et al. ∙

research

∙ 11/03/2018

Large-scale Heteroscedastic Regression via Gaussian Process

Heteroscedastic regression which considers varying noises across input d...

0 Haitao Liu, et al. ∙

research

∙ 11/03/2018

Understanding and Comparing Scalable Gaussian Process Regression for Big Data

As a non-parametric Bayesian model which produces informative predictive...

0 Haitao Liu, et al. ∙

Yew Soon Ong

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