77 citations · 99 across the 9 of their papers we have counts for
10 papers
Can lies be faked? Comparing low-stakes and high-stakes deception video datasets from a Machine Learning perspective
Mateus Karvat Camara, Adriana Postal, Tomas Henrique Maul +1
Despite the great impact of lies in human societies and a meager 54% human accuracy for Deception Detection (DD), Machine Learning systems that perform automated DD are still not v…
Towards a Universal Gating Network for Mixtures of Experts
Chen Wen Kang, Chua Meng Hong, Tomas Maul
The combination and aggregation of knowledge from multiple neural networks can be commonly seen in the form of mixtures of experts. However, such combinations are usually done usin…
Data Augmentation by AutoEncoders for Unsupervised Anomaly Detection
Kasra Babaei, ZhiYuan Chen, Tomas Maul
This paper proposes an autoencoder (AE) that is used for improving the performance of once-class classifiers for the purpose of detecting anomalies. Traditional one-class classifie…
Reducing Catastrophic Forgetting in Modular Neural Networks by Dynamic Information Balancing
Mohammed Amer, Tomás Maul
Lifelong learning is a very important step toward realizing robust autonomous artificial agents. Neural networks are the main engine of deep learning, which is the current state-of…
AEGR: A simple approach to gradient reversal in autoencoders for network anomaly detection
Kasra Babaei, Zhi Yuan Chen, Tomas Maul
Anomaly detection is referred to as a process in which the aim is to detect data points that follow a different pattern from the majority of data points. Anomaly detection methods…
Detecting Point Outliers Using Prune-based Outlier Factor (PLOF)
Kasra Babaei, ZhiYuan Chen, Tomas Maul
Outlier detection (also known as anomaly detection or deviation detection) is a process of detecting data points in which their patterns deviate significantly from others. It is co…
Weight Map Layer for Noise and Adversarial Attack Robustness
Mohammed Amer, Tomás Maul
Convolutional neural networks (CNNs) are known for their good performance and generalization in vision-related tasks and have become state-of-the-art in both application and resear…
A Review of Modularization Techniques in Artificial Neural Networks
Mohammed Amer, Tomás Maul
Artificial neural networks (ANNs) have achieved significant success in tackling classical and modern machine learning problems. As learning problems grow in scale and complexity, a…
Path Capsule Networks
Mohammed Amer, Tomás Maul
Capsule network (CapsNet) was introduced as an enhancement over convolutional neural networks, supplementing the latter's invariance properties with equivariance through pose estim…
Improving Neural Network Generalization by Combining Parallel Circuits with Dropout
Kien Tuong Phan, Tomas Henrique Maul, Tuong Thuy Vu +1
In an attempt to solve the lengthy training times of neural networks, we proposed Parallel Circuits (PCs), a biologically inspired architecture. Previous work has shown that this a…