Ng, Tin Lok James
- Deep Compositional Spatial Models
2019/06/06 by Zammit-Mangion, Andrew, Ng, Tin Lok James, Vu, Quan +1 · 5 citations
#Computation (stat.CO) #FOS: Computer and information sciences #Machine Learning (stat.ML) #Methodology (stat.ME)
- Generating Plausible Counterfactual Explanations for Deep Transformers in Financial Text Classification
2020/10/23 by Linyi Yang, Eoin M. Kenny, Yang, Linyi +9 · 3 citations
Computer Science · #Adversarial Robustness in Machine Learning #Artificial Intelligence (cs.AI) #Computation and Language (cs.CL) #Explainable Artificial Intelligence (XAI) #FOS: Computer and information sciences #Topic Modeling
- Universal Approximation on the Hypersphere
2020/04/14 by Ng, Tin Lok James, Kwong, Kwok-Kun · 2 citations
#FOS: Computer and information sciences #FOS: Mathematics #Methodology (stat.ME) #Statistics Theory (math.ST)
- Model-based clustering for random hypergraphs
2018/08/15 by Ng, Tin Lok James, Murphy, Thomas Brendan · 1 citation
#FOS: Computer and information sciences #Methodology (stat.ME)
- Penalized Maximum Likelihood Estimator for Mixture of von Mises-Fisher Distributions
2020/09/07 by Tin Lok James Ng, Ng, Tin Lok James · 1 citation
Computer Science · Mathematics · #Bayesian Methods and Mixture Models #FOS: Computer and information sciences #Methodology (stat.ME) #Statistical Distribution Estimation and Applications #Statistical Methods and Bayesian Inference
- Spherical Poisson Point Process Intensity Function Modeling and Estimation with Measure Transport
2022/01/24 by Tin Lok James Ng, Andrew Zammit‐Mangion, Ng, Tin Lok James +1 · 1 citation
Decision Sciences · Economics, Econometrics and Finance · Mathematics · #FOS: Computer and information sciences #Machine Learning (stat.ML) #Methodology (stat.ME) #Point processes and geometric inequalities #Spatial and Panel Data Analysis #demographic modeling and climate adaptation
- Bayesian Wasserstein Repulsive Gaussian Mixture Models
2025/04/30 by Huang, Weipeng, Ng, Tin Lok James · 1 citation
#FOS: Computer and information sciences #Methodology (stat.ME)