296 citations · 1.5k across the 45 of their papers we have counts for
16 papers · 1 filter
FRuDA: Framework for Distributed Adversarial Domain Adaptation
Shaoduo Gan, Akhil Mathur, Anton Isopoussu +3
Breakthroughs in unsupervised domain adaptation (uDA) can help in adapting models from a label-rich source domain to unlabeled target domains. Despite these advancements, there is…
MedPerf: Open Benchmarking Platform for Medical Artificial Intelligence using Federated Evaluation
Alexandros Karargyris, Renato Umeton, Micah J. Sheller +39
Medical AI has tremendous potential to advance healthcare by supporting the evidence-based practice of medicine, personalizing patient treatment, reducing costs, and improving prov…
Inferring Facing Direction from Voice Signals
Yu-Lin Wei, Rui Li, Abhinav Mehrotra +2
Consider a home or office where multiple devices are running voice assistants (e.g., TVs, lights, ovens, refrigerators, etc.). A human user turns to a particular device and gives a…
Smart at what cost? Characterising Mobile Deep Neural Networks in the wild
Mario Almeida, Stefanos Laskaridis, Abhinav Mehrotra +3
With smartphones' omnipresence in people's pockets, Machine Learning (ML) on mobile is gaining traction as devices become more powerful. With applications ranging from visual filte…
Temporal Kernel Consistency for Blind Video Super-Resolution
Lichuan Xiang, Royson Lee, Mohamed S. Abdelfattah +2
Deep learning-based blind super-resolution (SR) methods have recently achieved unprecedented performance in upscaling frames with unknown degradation. These models are able to accu…
Deep Neural Network-based Enhancement for Image and Video Streaming Systems: A Survey and Future Directions
Royson Lee, Stylianos I. Venieris, Nicholas D. Lane
Internet-enabled smartphones and ultra-wide displays are transforming a variety of visual apps spanning from on-demand movies and 360° videos to video-conferencing and live streami…
Multi-Exit Semantic Segmentation Networks
Alexandros Kouris, Stylianos I. Venieris, Stefanos Laskaridis +1
Semantic segmentation arises as the backbone of many vision systems, spanning from self-driving cars and robot navigation to augmented reality and teleconferencing. Frequently oper…
On-device Federated Learning with Flower
Akhil Mathur, Daniel J. Beutel, Pedro Porto Buarque de Gusmão +6
Federated Learning (FL) allows edge devices to collaboratively learn a shared prediction model while keeping their training data on the device, thereby decoupling the ability to do…
unzipFPGA: Enhancing FPGA-based CNN Engines with On-the-Fly Weights Generation
Stylianos I. Venieris, Javier Fernandez-Marques, Nicholas D. Lane
Single computation engines have become a popular design choice for FPGA-based convolutional neural networks (CNNs) enabling the deployment of diverse models without fabric reconfig…
End-to-End Speech Recognition from Federated Acoustic Models
Yan Gao, Titouan Parcollet, Salah Zaiem +4
Training Automatic Speech Recognition (ASR) models under federated learning (FL) settings has attracted a lot of attention recently. However, the FL scenarios often presented in th…
DynO: Dynamic Onloading of Deep Neural Networks from Cloud to Device
Mario Almeida, Stefanos Laskaridis, Stylianos I. Venieris +2
Recently, there has been an explosive growth of mobile and embedded applications using convolutional neural networks(CNNs). To alleviate their excessive computational demands, deve…
It's always personal: Using Early Exits for Efficient On-Device CNN Personalisation
Ilias Leontiadis, Stefanos Laskaridis, Stylianos I. Venieris +1
On-device machine learning is becoming a reality thanks to the availability of powerful hardware and model compression techniques. Typically, these models are pretrained on large G…
FjORD: Fair and Accurate Federated Learning under heterogeneous targets with Ordered Dropout
Samuel Horvath, Stefanos Laskaridis, Mario Almeida +3
Federated Learning (FL) has been gaining significant traction across different ML tasks, ranging from vision to keyboard predictions. In large-scale deployments, client heterogenei…
A first look into the carbon footprint of federated learning
Xinchi Qiu, Titouan Parcollet, Javier Fernandez-Marques +6
Despite impressive results, deep learning-based technologies also raise severe privacy and environmental concerns induced by the training procedure often conducted in data centers.…
Predicting Patient Outcomes with Graph Representation Learning
Emma Rocheteau, Catherine Tong, Petar Veličković +2
Recent work on predicting patient outcomes in the Intensive Care Unit (ICU) has focused heavily on the physiological time series data, largely ignoring sparse data such as diagnose…
Zero-Cost Proxies for Lightweight NAS
Mohamed S. Abdelfattah, Abhinav Mehrotra, Łukasz Dudziak +1
Neural Architecture Search (NAS) is quickly becoming the standard methodology to design neural network models. However, NAS is typically compute-intensive because multiple models n…