44 citations · 45 across the 8 of their papers we have counts for
9 papers
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…
AgentKVShift: Efficient KV Cache Reuse for Agentic Memory Systems
Nilesh Prasad Pandey, Jason Kong, Lanxiang Hu +5
Memory-augmented LLM agents maintain context across hundreds of interactions through agentic memory systems that actively curate retrieved content with LLM-generated metadata such…
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…
A3-FPN: Asymptotic Content-Aware Pyramid Attention Network for Dense Visual Prediction
Meng'en Qin, Yu Song, Quanling Zhao +3
Learning multi-scale representations is the common strategy to tackle object scale variation in dense prediction tasks. Although existing feature pyramid networks have greatly adva…
HDDB: Efficient In-Storage SQL Database Search Using Hyperdimensional Computing on Ferroelectric NAND Flash
Quanling Zhao, Yanru Chen, Runyang Tian +6
Hyperdimensional Computing (HDC) encodes information and data into high-dimensional distributed vectors that can be manipulated using simple bitwise operations and similarity searc…
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…
Offload Rethinking by Cloud Assistance for Efficient Environmental Sound Recognition on LPWANs
Le Zhang, Quanling Zhao, Run Wang +4
Learning-based environmental sound recognition has emerged as a crucial method for ultra-low-power environmental monitoring in biological research and city-scale sensing systems. T…
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…