24 citations · 44 across the 9 of their papers we have counts for
9 papers
Self-supervised speech representation learning for keyword-spotting with light-weight transformers
Chenyang Gao, Yue Gu, Francesco Caliva +1
Self-supervised speech representation learning (S3RL) is revolutionizing the way we leverage the ever-growing availability of data. While S3RL related studies typically use large m…
Fixed-point quantization aware training for on-device keyword-spotting
Sashank Macha, Om Oza, Alex Escott +5
Fixed-point (FXP) inference has proven suitable for embedded devices with limited computational resources, and yet model training is continually performed in floating-point (FLP).…
Adversarial Robust Training of Deep Learning MRI Reconstruction Models
Francesco Calivá, Kaiyang Cheng, Rutwik Shah +1
Deep Learning (DL) has shown potential in accelerating Magnetic Resonance Image acquisition and reconstruction. Nevertheless, there is a dearth of tailored methods to guarantee tha…
The International Workshop on Osteoarthritis Imaging Knee MRI Segmentation Challenge: A Multi-Institute Evaluation and Analysis Framework on a Standardized Dataset
Arjun D. Desai, Francesco Caliva, Claudia Iriondo +26
Purpose: To organize a knee MRI segmentation challenge for characterizing the semantic and clinical efficacy of automatic segmentation methods relevant for monitoring osteoarthriti…
Hierarchical Severity Staging of Anterior Cruciate Ligament Injuries using Deep Learning with MRI Images
Nikan K. Namiri, Io Flament, Bruno Astuto +6
Purpose: To evaluate the diagnostic utility of two convolutional neural networks (CNNs) for severity staging of anterior cruciate ligament (ACL) injuries. Materials and Methods: Th…
Adversarial Policy Gradient for Deep Learning Image Augmentation
Kaiyang Cheng, Claudia Iriondo, Francesco Calivá +3
The use of semantic segmentation for masking and cropping input images has proven to be a significant aid in medical imaging classification tasks by decreasing the noise and varian…
Distance Map Loss Penalty Term for Semantic Segmentation
Francesco Caliva, Claudia Iriondo, Alejandro Morales Martinez +2
Convolutional neural networks for semantic segmentation suffer from low performance at object boundaries. In medical imaging, accurate representation of tissue surfaces and volumes…
Deep Bayesian Self-Training
Fabio De Sousa Ribeiro, Francesco Caliva, Mark Swainson +3
Supervised Deep Learning has been highly successful in recent years, achieving state-of-the-art results in most tasks. However, with the ongoing uptake of such methods in industria…
Towards a Deep Unified Framework for Nuclear Reactor Perturbation Analysis
Fabio De Sousa Ribeiro, Francesco Caliva, Dionysios Chionis +5
In this paper, we take the first steps towards a novel unified framework for the analysis of perturbations in both the Time and Frequency domains. The identification of type and so…