491 citations · 700 across the 6 of their papers we have counts for
6 papers
Signal in the Noise: Polysemantic Interference Transfers and Predicts Cross-Model Influence
Bofan Gong, Shiyang Lai, James Evans +1
Polysemanticity is pervasive in language models and remains a major challenge for interpretation and model behavioral control. Leveraging sparse autoencoders (SAEs), we map the pol…
Inferring Private Personal Attributes of Virtual Reality Users from Head and Hand Motion Data
Vivek Nair, Christian Rack, Wenbo Guo +8
Motion tracking "telemetry" data lies at the core of nearly all modern virtual reality (VR) and metaverse experiences. While generally presumed innocuous, recent studies have demon…
D2P-Fed: Differentially Private Federated Learning With Efficient Communication
Lun Wang, Ruoxi Jia, Dawn Song
In this paper, we propose the discrete Gaussian based differentially private federated learning (D2P-Fed), a unified scheme to achieve both differential privacy (DP) and communicat…
Scaling Out-of-Distribution Detection for Real-World Settings
Dan Hendrycks, Steven Basart, Mantas Mazeika +5
Detecting out-of-distribution examples is important for safety-critical machine learning applications such as detecting novel biological phenomena and self-driving cars. However, e…
Scalability vs. Utility: Do We Have to Sacrifice One for the Other in Data Importance Quantification?
Ruoxi Jia, Fan Wu, Xuehui Sun +6
Quantifying the importance of each training point to a learning task is a fundamental problem in machine learning and the estimated importance scores have been leveraged to guide a…
Robust Physical-World Attacks on Deep Learning Models
Kevin Eykholt, Ivan Evtimov, Earlence Fernandes +6
Recent studies show that the state-of-the-art deep neural networks (DNNs) are vulnerable to adversarial examples, resulting from small-magnitude perturbations added to the input. G…