Search Results for author:
Found 14 papers, 2 papers with code
Examining Modality Incongruity in Multimodal Federated Learning for Medical Vision and Language-based Disease Detection
Secondly, we introduce a modality imputation network (MIN) pre-trained in a multimodal client for modality translation in unimodal clients and investigate its potential towards mitigating the missing modality problem.
Investigating YOLO Models Towards Outdoor Obstacle Detection For Visually Impaired People
The utilization of deep learning-based object detection is an effective approach to assist visually impaired individuals in avoiding obstacles.
Forecasting Lithium-Ion Battery Longevity with Limited Data Availability: Benchmarking Different Machine Learning Algorithms
Our experiments revealed that the machine learning algorithms on handcrafted features performed particularly well, resulting in 10-20% average mean absolute percentage error.
Dual Conditioned Diffusion Models for Out-Of-Distribution Detection: Application to Fetal Ultrasound Videos
To detect OOD samples in this context, the resulting model should generalise to the intra-anatomy variations while rejecting similar OOD samples.
Rethinking Semi-Supervised Federated Learning: How to co-train fully-labeled and fully-unlabeled client imaging data
The most challenging, yet practical, setting of semi-supervised federated learning (SSFL) is where a few clients have fully labeled data whereas the other clients have fully unlabeled data.
Post-Deployment Adaptation with Access to Source Data via Federated Learning and Source-Target Remote Gradient Alignment
This paper challenges this assumption and introduces FedPDA, a novel adaptation framework that brings the utility of learning from remote data from Federated Learning into PDA.
SPEAK WITH YOUR HANDS Using Continuous Hand Gestures to control Articulatory Speech Synthesizer
Considering the upper palate as fixed and the spline model as the dynamically moving lower surface (tongue) of the vocal tract, we compute 1D area functional values that are fed to the Pink Trombone, generating continuous speech sounds.
Ultra2Speech -- A Deep Learning Framework for Formant Frequency Estimation and Tracking from Ultrasound Tongue Images
Thousands of individuals need surgical removal of their larynx due to critical diseases every year and therefore, require an alternative form of communication to articulate speech sounds after the loss of their voice box.
Learning Joint Articulatory-Acoustic Representations with Normalizing Flows
The articulatory geometric configurations of the vocal tract and the acoustic properties of the resultant speech sound are considered to have a strong causal relationship.
SPEAK YOUR MIND! Towards Imagined Speech Recognition With Hierarchical Deep Learning
Speech-related Brain Computer Interface (BCI) technologies provide effective vocal communication strategies for controlling devices through speech commands interpreted from brain signals.
Hierarchical Deep Feature Learning For Decoding Imagined Speech From EEG
We propose a mixed deep neural network strategy, incorporating parallel combination of Convolutional (CNN) and Recurrent Neural Networks (RNN), cascaded with deep autoencoders and fully connected layers towards automatic identification of imagined speech from EEG.
Deep Learning the EEG Manifold for Phonological Categorization from Active Thoughts
Speech-related Brain Computer Interfaces (BCI) aim primarily at finding an alternative vocal communication pathway for people with speaking disabilities.
Muscle Excitation Estimation in Biomechanical Simulation Using NAF Reinforcement Learning
In this article, we propose a deep reinforcement learning method to estimate the muscle excitations in simulated biomechanical systems.
Towards Automatic Speech Identification from Vocal Tract Shape Dynamics in Real-time MRI
Interestingly, the results show a marked difference in the model performance in the context of speech classification with respect to generic sequence or video classification tasks.
