106 citations · 333 across the 19 of their papers we have counts for
6 papers · 1 filter
SensiX: A Platform for Collaborative Machine Learning on the Edge
Chulhong Min, Akhil Mathur, Alessandro Montanari +2
The emergence of multiple sensory devices on or near a human body is uncovering new dynamics of extreme edge computing. In this, a powerful and resource-rich edge device such as a…
Leveraging Activity Recognition to Enable Protective Behavior Detection in Continuous Data
Chongyang Wang, Yuan Gao, Akhil Mathur +3
Protective behavior exhibited by people with chronic pain (CP) during physical activities is the key to understanding their physical and emotional states. Existing automatic protec…
Can Federated Learning Save The Planet?
Xinchi Qiu, Titouan Parcollet, Daniel J. Beutel +3
Despite impressive results, deep learning-based technologies also raise severe privacy and environmental concerns induced by the training procedure often conducted in data centers.…
Libri-Adapt: A New Speech Dataset for Unsupervised Domain Adaptation
Akhil Mathur, Fahim Kawsar, Nadia Berthouze +1
This paper introduces a new dataset, Libri-Adapt, to support unsupervised domain adaptation research on speech recognition models. Built on top of the LibriSpeech corpus, Libri-Ada…
Flower: A Friendly Federated Learning Research Framework
Daniel J. Beutel, Taner Topal, Akhil Mathur +8
Federated Learning (FL) has emerged as a promising technique for edge devices to collaboratively learn a shared prediction model, while keeping their training data on the device, t…
Mic2Mic: Using Cycle-Consistent Generative Adversarial Networks to Overcome Microphone Variability in Speech Systems
Akhil Mathur, Anton Isopoussu, Fahim Kawsar +2
Mobile and embedded devices are increasingly using microphones and audio-based computational models to infer user context. A major challenge in building systems that combine audio…