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Double Randomized Underdamped Langevin with Dimension-Independent Convergence Guarantee Y uanshi Liu, Cong Fang, Tong Zhang School of Intelligence Science and Technology, Peking University
Sampling from a high-dimensional distribution serves as one of the key components in statistics, machine learning, and scientific computing, and constitutes the foundation of the fields including Bayesian statistics and generative models [Liu and Liu, 2001, Brooks et al., 2011, Song et al.,
Double Randomized Underdamped Langevin with Dimension-Independent Convergence Guarantee Y uanshi Liu, Cong Fang, Tong Zhang School of Intelligence Science and Technology, Peking University
Sampling from a high-dimensional distribution serves as one of the key components in statistics, machine learning, and scientific computing, and constitutes the foundation of the fields including Bayesian statistics and generative models [Liu and Liu, 2001, Brooks et al., 2011, Song et al.,
Datasheet Y ubo Ma
Q1: F or what purpose was the dataset created? As stated in Section 1, most previous datasets on DU focus on single-page DU. Our benchmark is constructed to bridge such a gap. Q2: Who created the dataset (e.g., which team, research group) and on behalf of which entity (e.g., Q3: What support was needed to make this dataset? Q1: What do the instances that comprise the dataset represent (e.g., documents, photos, people, countries)?
Benchmarking Long-context Document Understanding with Visualizations
Moreover, 33.7% of the questions are cross-page questions
Label-Only Model Inversion Attacks via Knowledge Transfer Supplementary Materials
It's important to note that
