Country
Bayesian Nonparametric Spectral Estimation
Neural Information Processing Systems http://nips.cc/
Iterative Least Trimmed Squares for Mixed Linear Regression
Neural Information Processing Systems http://nips.cc/
Conditional Adversarial Domain Adaptation
Mingsheng Long, ZHANGJIE CAO, Jianmin Wang, Michael I. Jordan
Neural Information Processing Systems http://nips.cc/
DarkSAM: Fooling Segment Anything Model to Segment Nothing Ziqi Zhou 1,2,3, Y ufei Song
Segment Anything Model (SAM) has recently gained much attention for its outstanding generalization to unseen data and tasks. Despite its promising prospect, the vulnerabilities of SAM, especially to universal adversarial perturbation (UAP) have not been thoroughly investigated yet. In this paper, we propose Dark-SAM, the first prompt-free universal attack framework against SAM, including a semantic decoupling-based spatial attack and a texture distortion-based frequency attack. We first divide the output of SAM into foreground and background. Then, we design a shadow target strategy to obtain the semantic blueprint of the image as the attack target.
Bipartite Stochastic Block Models with Tiny Clusters
Discovering clusters in bipartite graphs has been researched in many different settings. However, most of these algorithms were heuristics and do not provide theoretical guarantees for the quality oftheir results.
Goal-conditioned Imitation Learning
Yiming Ding, Carlos Florensa, Pieter Abbeel, Mariano Phielipp
Neural Information Processing Systems http://nips.cc/
Noise
The user selects a workload matrixArepresenting a desired first-order optimization method.