18 citations · 38 across the 4 of their papers we have counts for
4 papers
Probabilistic Modeling: Proving the Lottery Ticket Hypothesis in Spiking Neural Network
Man Yao, Yuhong Chou, Guangshe Zhao +4
The Lottery Ticket Hypothesis (LTH) states that a randomly-initialized large neural network contains a small sub-network (i.e., winning tickets) which, when trained in isolation, c…
Benchmarking Instance-Centric Counterfactual Algorithms for XAI: From White Box to Black Box
Catarina Moreira, Yu-Liang Chou, Chihcheng Hsieh +3
This study investigates the impact of machine learning models on the generation of counterfactual explanations by conducting a benchmark evaluation over three different types of mo…
Counterfactuals and Causability in Explainable Artificial Intelligence: Theory, Algorithms, and Applications
Yu-Liang Chou, Catarina Moreira, Peter Bruza +2
There has been a growing interest in model-agnostic methods that can make deep learning models more transparent and explainable to a user. Some researchers recently argued that for…
An Interpretable Probabilistic Approach for Demystifying Black-box Predictive Models
Catarina Moreira, Yu-Liang Chou, Mythreyi Velmurugan +3
The use of sophisticated machine learning models for critical decision making is faced with a challenge that these models are often applied as a "black-box". This has led to an inc…