Search Results for author:
Found 7 papers, 2 papers with code
Self-supervised speech representation learning for keyword-spotting with light-weight transformers
Self-supervised speech representation learning (S3RL) is revolutionizing the way we leverage the ever-growing availability of data.
Fixed-point quantization aware training for on-device keyword-spotting
Furthermore, on an in-house KWS dataset, we show that our 8-bit FXP-QAT models have a 4-6% improvement in relative false discovery rate at fixed false reject rate compared to full precision FLP models.
The International Workshop on Osteoarthritis Imaging Knee MRI Segmentation Challenge: A Multi-Institute Evaluation and Analysis Framework on a Standardized Dataset
Purpose: To organize a knee MRI segmentation challenge for characterizing the semantic and clinical efficacy of automatic segmentation methods relevant for monitoring osteoarthritis progression.
Hierarchical Severity Staging of Anterior Cruciate Ligament Injuries using Deep Learning with MRI Images
Results: The overall accuracy and weighted Cohen's kappa reported for ACL injury classification were higher using the 2D CNN (accuracy: 92% (233/254) and kappa: 0. 83) than the 3D CNN (accuracy: 89% (225/254) and kappa: 0. 83) (P = . 27).
Distance Map Loss Penalty Term for Semantic Segmentation
We propose to use distance maps, derived from ground truth masks, to create a penalty term, guiding the network's focus towards hard-to-segment boundary regions.
Deep Bayesian Self-Training
Supervised Deep Learning has been highly successful in recent years, achieving state-of-the-art results in most tasks.
Towards a Deep Unified Framework for Nuclear Reactor Perturbation Analysis
512 dimensional representations were extracted from the 3D-CNN and LSTM architectures, and used as input to a fused multi-sigmoid classification layer to recognise the perturbation type.
