2 citations · 6 across the 12 of their papers we have counts for
4 papers · 1 filter
When Trees Are Not Enough: Learning Mixed-Topology Feature Graphs with Adaptive Graph Sparse Autoencoders
Xiaozuo Shen, Yifei Cai, Tian Tan +3
Sparse autoencoders (SAEs) expose interpretable features in large language model activations, yet existing structured SAEs impose single-parent trees or forests, while post-hoc gra…
PRIVEE: Privacy-Preserving Vertical Federated Learning Against Feature Inference Attacks
Sindhuja Madabushi, Haider Ali, Ahmad Faraz Khan +5
Vertical Federated Learning (VFL) enables collaborative model training across organizations that share common user samples but hold disjoint feature spaces. Despite its potential,…
Comet: A Communication-efficient and Performant Approximation for Private Transformer Inference
Xiangrui Xu, Qiao Zhang, Rui Ning +2
The prevalent use of Transformer-like models, exemplified by ChatGPT in modern language processing applications, underscores the critical need for enabling private inference essent…
CHEETAH: An Ultra-Fast, Approximation-Free, and Privacy-Preserved Neural Network Framework based on Joint Obscure Linear and Nonlinear Computations
Qiao Zhang, Cong Wang, Chunsheng Xin +1
Machine Learning as a Service (MLaaS) is enabling a wide range of smart applications on end devices. However, such convenience comes with a cost of privacy because users have to up…