38 citations · 45 across the 7 of their papers we have counts for
9 papers
EEG-FuseFormer: A Transformer-Driven Feature Fusion Framework for Seizure Onset Prediction
Vigneshwar Hariharan, Chithra Reghuvaran, Arlene John +4
Epilepsy is one of the most common neurological disorders globally, characterized by recurring seizures and significantly impacting the quality of life. Despite advancements in dia…
TinyDrop: Tiny Model Guided Token Dropping for Vision Transformers
Guoxin Wang, Qingyuan Wang, Binhua Huang +2
Vision Transformers (ViTs) achieve strong performance in image classification but incur high computational costs from processing all image tokens. To reduce inference costs in larg…
Optimal Brain Connection: Towards Efficient Structural Pruning
Shaowu Chen, Wei Ma, Binhua Huang +5
Structural pruning has been widely studied for its effectiveness in compressing neural networks. However, existing methods often neglect the interconnections among parameters. To a…
ORXE: Orchestrating Experts for Dynamically Configurable Efficiency
Qingyuan Wang, Guoxin Wang, Barry Cardiff +1
This paper presents ORXE, a modular and adaptable framework for achieving real-time configurable efficiency in AI models. By leveraging a collection of pre-trained experts with div…
ECG Biometric Authentication Using Self-Supervised Learning for IoT Edge Sensors
Guoxin Wang, Shreejith Shanker, Avishek Nag +2
Wearable Internet of Things (IoT) devices are gaining ground for continuous physiological data acquisition and health monitoring. These physiological signals can be used for securi…
Tiny Models are the Computational Saver for Large Models
Qingyuan Wang, Barry Cardiff, Antoine Frappé +2
This paper introduces TinySaver, an early-exit-like dynamic model compression approach which employs tiny models to substitute large models adaptively. Distinct from traditional co…
DyCE: Dynamically Configurable Exiting for Deep Learning Compression and Real-time Scaling
Qingyuan Wang, Barry Cardiff, Antoine Frappé +2
Conventional deep learning (DL) model compression and scaling methods focus on altering the model's components, impacting the results across all samples uniformly. However, since s…
Unsupervised Pre-Training Using Masked Autoencoders for ECG Analysis
Guoxin Wang, Qingyuan Wang, Ganesh Neelakanta Iyer +2
Unsupervised learning methods have become increasingly important in deep learning due to their demonstrated large utilization of datasets and higher accuracy in computer vision and…
POCKET: Pruning Random Convolution Kernels for Time Series Classification from a Feature Selection Perspective
Shaowu Chen, Weize Sun, Lei Huang +3
In recent years, two competitive time series classification models, namely, ROCKET and MINIROCKET, have garnered considerable attention due to their low training cost and high accu…