4 accepted papers
Inferring ground truth from noisy, crowdsourced labels is a fundamental challenge in machine learning. For decades, the dominant paradigm has relied on dataset-specific parameter estimation, a non-scalable method that fails to transfer knowledge. Recent efforts toward universal aggregation models do…
Point-based models have achieved impressive success in point cloud processing, but current methods mainly address issues such as disorder, sparsity, and rotation of point clouds. In addition to these characteristics, we also found that for the point set P (X<inf xmlns:mml="http://www.w3.org/1998/Mat…
A point cloud is a geometric data structure that is a set of coordinates p(x, y, z) of points, with additional normal n(n <inf xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">x</inf> , n <inf xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http:/…
Designed to establish potential relations and distill high-order representations, graph-based recommendation systems continue to reveal promising results by jointly modeling ratings and reviews. However, existing studies capture simple review relations, failing to (1) completely explore hidden conne…