Search Results for author:
Found 7 papers, 4 papers with code
Debiasing surgeon: fantastic weights and how to find them
Nowadays an ever-growing concerning phenomenon, the emergence of algorithmic biases that can lead to unfair models, emerges.
SCoTTi: Save Computation at Training Time with an adaptive framework
On-device training is an emerging approach in machine learning where models are trained on edge devices, aiming to enhance privacy protection and real-time performance.
Enhanced EEG-Based Mental State Classification : A novel approach to eliminate data leakage and improve training optimization for Machine Learning
In this paper, we explore prior research and introduce a new methodology for classifying mental state levels based on EEG signals utilizing machine learning (ML).
Towards On-device Learning on the Edge: Ways to Select Neurons to Update under a Budget Constraint
In the realm of efficient on-device learning under extreme memory and computation constraints, a significant gap in successful approaches persists.
Can Unstructured Pruning Reduce the Depth in Deep Neural Networks?
Pruning is a widely used technique for reducing the size of deep neural networks while maintaining their performance.
Mining bias-target Alignment from Voronoi Cells
Despite significant research efforts, deep neural networks are still vulnerable to biases: this raises concerns about their fairness and limits their generalization.
Optimized preprocessing and Tiny ML for Attention State Classification
In this paper, we present a new approach to mental state classification from EEG signals by combining signal processing techniques and machine learning (ML) algorithms.
