4 citations · 5 across the 5 of their papers we have counts for
4 papers · 1 filter
Interpretable Few-shot Learning with Online Attribute Selection
Mohammad Reza Zarei, Majid Komeili
Few-shot learning (FSL) presents a challenging learning problem in which only a few samples are available for each class. Decision interpretation is more important in few-shot clas…
Interpretable Concept-based Prototypical Networks for Few-Shot Learning
Mohammad Reza Zarei, Majid Komeili
Few-shot learning aims at recognizing new instances from classes with limited samples. This challenging task is usually alleviated by performing meta-learning on similar tasks. How…
Feature-Based Interpretable Reinforcement Learning based on State-Transition Models
Omid Davoodi, Majid Komeili
Growing concerns regarding the operational usage of AI models in the real-world has caused a surge of interest in explaining AI models' decisions to humans. Reinforcement Learning…
Cause and Effect: Hierarchical Concept-based Explanation of Neural Networks
Mohammad Nokhbeh Zaeem, Majid Komeili
In many scenarios, human decisions are explained based on some high-level concepts. In this work, we take a step in the interpretability of neural networks by examining their inter…