79 citations · 603 across the 47 of their papers we have counts for
7 papers · 1 filter
Improving the Interpretability of Deep Neural Networks with Knowledge Distillation
Xuan Liu, Xiaoguang Wang, Stan Matwin
Deep Neural Networks have achieved huge success at a wide spectrum of applications from language modeling, computer vision to speech recognition. However, nowadays, good performanc…
On feature selection and evaluation of transportation mode prediction strategies
Mohammad Etemad, Amilcar Soares Junior, Stan Matwin
Transportation modes prediction is a fundamental task for decision making in smart cities and traffic management systems. Traffic policies designed based on trajectory mining can s…
On the Importance of Attention in Meta-Learning for Few-Shot Text Classification
Xiang Jiang, Mohammad Havaei, Gabriel Chartrand +5
Current deep learning based text classification methods are limited by their ability to achieve fast learning and generalization when the data is scarce. We address this problem by…
Predicting Crime Using Spatial Features
Fateha Khanam Bappee, Amilcar Soares Junior, Stan Matwin
Our study aims to build a machine learning model for crime prediction using geospatial features for different categories of crime. The reverse geocoding technique is applied to ret…
Predicting Transportation Modes of GPS Trajectories using Feature Engineering and Noise Removal
Mohammad Etemad, Amilcar Soares Junior, Stan Matwin
Understanding transportation mode from GPS (Global Positioning System) traces is an essential topic in the data mobility domain. In this paper, a framework is proposed to predict t…
One Single Deep Bidirectional LSTM Network for Word Sense Disambiguation of Text Data
Ahmad Pesaranghader, Ali Pesaranghader, Stan Matwin +1
Due to recent technical and scientific advances, we have a wealth of information hidden in unstructured text data such as offline/online narratives, research articles, and clinical…
Interpretable Deep Convolutional Neural Networks via Meta-learning
Xuan Liu, Xiaoguang Wang, Stan Matwin
Model interpretability is a requirement in many applications in which crucial decisions are made by users relying on a model's outputs. The recent movement for "algorithmic fairnes…