13 citations · 19 across the 8 of their papers we have counts for
5 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…
Crash Data Augmentation Using Conditional Generative Adversarial Networks (CGAN) for Improving Safety Performance Functions
Mohammad Zarei, Bruce Hellinga
In this paper, we present a crash frequency data augmentation method based on Conditional Generative Adversarial Networks to improve crash frequency models. The proposed method is…
CGAN-EB: A Non-parametric Empirical Bayes Method for Crash Hotspot Identification Using Conditional Generative Adversarial Networks: A Real-world Crash Data Study
Mohammad Zarei, Bruce Hellinga, Pedram Izadpanah
The empirical Bayes (EB) method based on parametric statistical models such as the negative binomial (NB) has been widely used for ranking sites in road network safety screening pr…
CGAN-EB: A Non-parametric Empirical Bayes Method for Crash Hotspot Identification Using Conditional Generative Adversarial Networks: A Simulated Crash Data Study
Mohammad Zarei, Bruce Hellinga, Pedram Izadpanah
In this paper, a new non-parametric empirical Bayes approach called CGAN-EB is proposed for approximating empirical Bayes (EB) estimates in traffic locations (e.g., road segments)…