116 citations · 148 across the 9 of their papers we have counts for
11 papers
Reducing Overconfidence Predictions for Autonomous Driving Perception
Gledson Melotti, Cristiano Premebida, Jordan J. Bird +2
In state-of-the-art deep learning for object recognition, SoftMax and Sigmoid functions are most commonly employed as the predictor outputs. Such layers often produce overconfident…
Fruit Quality and Defect Image Classification with Conditional GAN Data Augmentation
Jordan J. Bird, Chloe M. Barnes, Luis J. Manso +2
Contemporary Artificial Intelligence technologies allow for the employment of Computer Vision to discern good crops from bad, providing a step in the pipeline of selecting healthy…
A Graph Neural Network to Model Disruption in Human-Aware Robot Navigation
Pilar Bachiller, Daniel Rodriguez-Criado, Ronit R. Jorvekar +3
Autonomous navigation is a key skill for assistive and service robots. To be successful, robots have to minimise the disruption caused to humans while moving. This implies predicti…
Chatbot Interaction with Artificial Intelligence: Human Data Augmentation with T5 and Language Transformer Ensemble for Text Classification
Jordan J. Bird, Anikó Ekárt, Diego R. Faria
In this work, we present the Chatbot Interaction with Artificial Intelligence (CI-AI) framework as an approach to the training of deep learning chatbots for task classification. Th…
Look and Listen: A Multi-modality Late Fusion Approach to Scene Classification for Autonomous Machines
Jordan J. Bird, Diego R. Faria, Cristiano Premebida +2
The novelty of this study consists in a multi-modality approach to scene classification, where image and audio complement each other in a process of deep late fusion. The approach…
LSTM and GPT-2 Synthetic Speech Transfer Learning for Speaker Recognition to Overcome Data Scarcity
Jordan J. Bird, Diego R. Faria, Anikó Ekárt +2
In speech recognition problems, data scarcity often poses an issue due to the willingness of humans to provide large amounts of data for learning and classification. In this work,…
Probabilistic Object Classification using CNN ML-MAP layers
G. Melotti, C. Premebida, J. J. Bird +2
Deep networks are currently the state-of-the-art for sensory perception in autonomous driving and robotics. However, deep models often generate overconfident predictions precluding…
On the Effects of Pseudo and Quantum Random Number Generators in Soft Computing
Jordan J. Bird, Anikó Ekárt, Diego R. Faria
In this work, we argue that the implications of Pseudo and Quantum Random Number Generators (PRNG and QRNG) inexplicably affect the performances and behaviours of various machine l…
Graph Neural Networks for Human-aware Social Navigation
Luis J. Manso, Ronit R. Jorvekar, Diego R. Faria +2
Autonomous navigation is a key skill for assistive and service robots. To be successful, robots have to navigate avoiding going through the personal spaces of the people surroundin…
SocNav1: A Dataset to Benchmark and Learn Social Navigation Conventions
Luis J. Manso, Pedro Nunez, Luis V. Calderita +2
Adapting to social conventions is an unavoidable requirement for the acceptance of assistive and social robots. While the scientific community broadly accepts that assistive robots…
A Deep Evolutionary Approach to Bioinspired Classifier Optimisation for Brain-Machine Interaction
Jordan J. Bird, Diego R. Faria, Luis J. Manso +2
This study suggests a new approach to EEG data classification by exploring the idea of using evolutionary computation to both select useful discriminative EEG features and optimise…