44 citations · 45 across the 8 of their papers we have counts for
5 papers · 1 filter
Superposed Latent Autoencoder
Quanling Zhao, Jiaying Yang, Tianqi Zhang +4
Autoencoders typically meet tight latent-memory budgets by making each latent representation smaller, sacrificing representational capacity. We ask a different question: can multip…
Bridging the Gap Between Hyperdimensional Computing and Kernel Methods via the Nyström Method
Quanling Zhao, Anthony Hitchcock Thomas, Ari Brin +2
Hyperdimensional computing (HDC) is an approach from the cognitive science literature for solving information processing tasks using data represented as high-dimensional random vec…
RGLD: Randomized Global-Local Density Estimation for Tabular Anomaly Detection
Quanling Zhao, Jiaying Yang, Ye Tian +5
Unsupervised tabular anomaly detection requires methods that are accurate, robust across heterogeneous datasets, and computationally efficient. Classical statistical detectors are…
FedUHD: Unsupervised Federated Learning using In-Memory Hyperdimensional Computing
You Hak Lee, Xiaofan Yu, Keming Fan +4
Unsupervised federated learning (UFL) enables privacy-preserving distributed training without data labeling, yet practical deployment remains challenging due to non-IID data, high…
Async-HFL: Efficient and Robust Asynchronous Federated Learning in Hierarchical IoT Networks
Xiaofan Yu, Ludmila Cherkasova, Harsh Vardhan +5
Federated Learning (FL) has gained increasing interest in recent years as a distributed on-device learning paradigm. However, multiple challenges remain to be addressed for deployi…