3 citations · 4 across the 5 of their papers we have counts for
5 papers
PCS-UQ: Uncertainty Quantification via the Predictability-Computability-Stability Framework
Abhineet Agarwal, Fange Xiao, Rebecca Barter +3
As machine learning (ML) enters high-stakes domains, trustworthy uncertainty quantification (UQ) is essential for safety. In this paper we introduce PCS-UQ, a framework based on th…
Command A: An Enterprise-Ready Large Language Model
Team Cohere, :, Aakanksha +227
In this report we describe the development of Command A, a powerful large language model purpose-built to excel at real-world enterprise use cases. Command A is an agent-optimised…
FedTSA: A Cluster-based Two-Stage Aggregation Method for Model-heterogeneous Federated Learning
Boyu Fan, Chenrui Wu, Xiang Su +1
Despite extensive research into data heterogeneity in federated learning (FL), system heterogeneity remains a significant yet often overlooked challenge. Traditional FL approaches…
A Survey on Model-heterogeneous Federated Learning: Problems, Methods, and Prospects
Boyu Fan, Siyang Jiang, Xiang Su +2
As privacy concerns continue to grow, federated learning (FL) has gained significant attention as a promising privacy-preserving technology, leading to considerable advancements in…
EmgAuth: Unlocking Smartphones with EMG Signals
Boyu Fan, Xiang Su, Jianwei Niu +1
Screen lock is a critical security feature for smartphones to prevent unauthorized access. Although various screen unlocking technologies, including fingerprint and facial recognit…