Researchers from the Embedded Systems and Robotics group at TCS Research & Innovation have recently developed a two-view depth network to infer depth and ego-motion from consecutive monocular sequences. Their approach, presented in a paper pre-published on arXiv, also...
Machine learning models that can recognize and predict human emotions have become increasingly popular over the past few years. In order for most of these techniques to perform well, however, the data used to train them is first annotated by human subjects. Moreover,...
Researchers at King Saud University, in Saudi Arabia, have developed a new approach to detect cyberbullying on Twitter using deep learning called OCDD. In contrast with other deep-learning approaches, which extract features from tweets and feed them to a classifier,...
Over the past few years, a growing number of researchers and companies worldwide have been developing techniques for automated driving. Before self-driving vehicles can be introduced on real roads, however, their efficiency and safety will need to be ascertained.
Researchers at the University of Wollongong, Deakin University, Monash University and Kyushu University have developed a framework that could be used to build a smart, AI-powered agile project management assistant. Their paper, pre-published on arXiv, has been...
A key limitation of existing artificial intelligence (AI) systems is that they are unable to tackle tasks for which they have not been trained. In fact, even when they are retrained, the majority these systems are prone to 'catastrophic forgetting,' which essentially...
Central pattern generators (CPGs) are biological neural circuits that can produce coordinated rhythmic outputs without requiring rhythmic inputs. CPGs are responsible for most rhythmic motions observed in living organisms, such as walking, breathing or swimming.
Researchers at TU Delft in the Netherlands have recently developed a convolutional neural network (CNN)-based model to reconstruct drawings that have deteriorated over time. In their study, published in Springer's Machine Vision and Applications, they specifically...
Adversarial audio attacks are small perturbations that are not perceivable by humans and are intentionally added to audio signals to impair the performance of machine learning (ML) models. These attacks raise serious concerns about the security of ML models, as they...
In recent years, researchers have proposed a wide variety of hardware implementations for feed-forward artificial neural networks. These implementations include three key components: a dot-product engine that can compute convolution and fully-connected layer...