24 citations · 54 across the 7 of their papers we have counts for
7 papers
FP8 versus INT8 for efficient deep learning inference
Mart van Baalen, Andrey Kuzmin, Suparna S Nair +8
Recently, the idea of using FP8 as a number format for neural network training has been floating around the deep learning world. Given that most training is currently conducted wit…
Neural RF SLAM for unsupervised positioning and mapping with channel state information
Shreya Kadambi, Arash Behboodi, Joseph B. Soriaga +4
We present a neural network architecture for jointly learning user locations and environment mapping up to isometry, in an unsupervised way, from channel state information (CSI) va…
An Expectation-Maximization Perspective on Federated Learning
Christos Louizos, Matthias Reisser, Joseph Soriaga +1
Federated learning describes the distributed training of models across multiple clients while keeping the data private on-device. In this work, we view the server-orchestrated fede…
Neural Augmentation of Kalman Filter with Hypernetwork for Channel Tracking
Kumar Pratik, Rana Ali Amjad, Arash Behboodi +2
We propose Hypernetwork Kalman Filter (HKF) for tracking applications with multiple different dynamics. The HKF combines generalization power of Kalman filters with expressive powe…
Unsupervised Information Obfuscation for Split Inference of Neural Networks
Mohammad Samragh, Hossein Hosseini, Aleksei Triastcyn +3
Splitting network computations between the edge device and a server enables low edge-compute inference of neural networks but might expose sensitive information about the test quer…
Federated Learning of User Verification Models Without Sharing Embeddings
Hossein Hosseini, Hyunsin Park, Sungrack Yun +3
We consider the problem of training User Verification (UV) models in federated setting, where each user has access to the data of only one class and user embeddings cannot be share…
Federated Learning of User Authentication Models
Hossein Hosseini, Sungrack Yun, Hyunsin Park +3
Machine learning-based User Authentication (UA) models have been widely deployed in smart devices. UA models are trained to map input data of different users to highly separable em…