367 citations · 1.3k across the 62 of their papers we have counts for
12 papers · 1 filter
A Generic Reinforced Explainable Framework with Knowledge Graph for Session-based Recommendation
Huizi Wu, Hui Fang, Zhu Sun +3
Session-based recommendation (SR) has gained increasing attention in recent years. Quite a great amount of studies have been devoted to designing complex algorithms to improve reco…
Fast, broad-band magnetic resonance spectroscopy with diamond widefield relaxometry
C. Mignon, A. R. Ortiz Moreno, H. Shirzad +5
We present an alternative to conventional Electron Paramagnetic Resonance (EPR) spectroscopy equipment. Avoiding the use of bulky magnets and magnetron equipment, we use the photol…
Not All Neighbors Are Worth Attending to: Graph Selective Attention Networks for Semi-supervised Learning
Tiantian He, Haicang Zhou, Yew-Soon Ong +1
Graph attention networks (GATs) are powerful tools for analyzing graph data from various real-world scenarios. To learn representations for downstream tasks, GATs generally attend…
A Multi-Channel Next POI Recommendation Framework with Multi-Granularity Check-in Signals
Zhu Sun, Yu Lei, Lu Zhang +3
Current study on next POI recommendation mainly explores user sequential transitions with the fine-grained individual-user POI check-in trajectories only, which suffers from the se…
Understanding Diversity in Session-Based Recommendation
Qing Yin, Hui Fang, Zhu Sun +1
Current session-based recommender systems (SBRSs) mainly focus on maximizing recommendation accuracy, while few studies have been devoted to improve diversity beyond accuracy. Mean…
DaisyRec 2.0: Benchmarking Recommendation for Rigorous Evaluation
Zhu Sun, Hui Fang, Jie Yang +5
Recently, one critical issue looms large in the field of recommender systems -- there are no effective benchmarks for rigorous evaluation -- which consequently leads to unreproduci…
Geo-Neus: Geometry-Consistent Neural Implicit Surfaces Learning for Multi-view Reconstruction
Qiancheng Fu, Qingshan Xu, Yew-Soon Ong +1
Recently, neural implicit surfaces learning by volume rendering has become popular for multi-view reconstruction. However, one key challenge remains: existing approaches lack expli…
Jacobian Granger Causal Neural Networks for Analysis of Stationary and Nonstationary Data
Suryadi, Yew-Soon Ong, Lock Yue Chew
Granger causality is a commonly used method for uncovering information flow and dependencies in a time series. Here we introduce JGC (Jacobian Granger Causality), a neural network-…
How Does Frequency Bias Affect the Robustness of Neural Image Classifiers against Common Corruption and Adversarial Perturbations?
Alvin Chan, Yew-Soon Ong, Clement Tan
Model robustness is vital for the reliable deployment of machine learning models in real-world applications. Recent studies have shown that data augmentation can result in model ov…
A Survey on AI Sustainability: Emerging Trends on Learning Algorithms and Research Challenges
Zhenghua Chen, Min Wu, Alvin Chan +2
Artificial Intelligence (AI) is a fast-growing research and development (R&D) discipline which is attracting increasing attention because of its promises to bring vast benefits for…
Multitask Neuroevolution for Reinforcement Learning with Long and Short Episodes
Nick Zhang, Abhishek Gupta, Zefeng Chen +1
Studies have shown evolution strategies (ES) to be a promising approach for reinforcement learning (RL) with deep neural networks. However, the issue of high sample complexity pers…
Learning Multi-Task Gaussian Process Over Heterogeneous Input Domains
Haitao Liu, Kai Wu, Yew-Soon Ong +3
Multi-task Gaussian process (MTGP) is a well-known non-parametric Bayesian model for learning correlated tasks effectively by transferring knowledge across tasks. But current MTGPs…