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3 days ago · The Hungarian matching algorithm, also called the Kuhn-Munkres algorithm, is a \(O\big(|V|^3\big)\) algorithm that can be used to find maximum-weight matchings in bipartite graphs, which is sometimes called the assignment problem.
Jun 10, 2024 · Recently, game-playing agents based on AI techniques have demonstrated superhuman performance in several sequential games, such as chess, Go, and poker. Surprisingly, the multi-agent learning techniques that allowed to reach these achievements do not take into account the actual behavior of the human player, potentially leading to an impressive ...
Jun 6, 2024 · Harold William Kuhn. Classics in game theory. princeton University press,... Hui Ge, Lei Zhao, Dong Yue, Xiangpeng Xie, Linghai Xie, Sergey Gorbachev, Iakov Korovin, and Yuan Ge.
To overcome these limitations, we introduce an innovative methodology that integrates pseudo-label generation to enable end-to-end training of the spotting network that optimizes text recognition and location estimation at the same time called TWIST.
Jun 6, 2024 · The goal of label-free coreset selection is to identify a high-performing subset of the data without relying on ground-truth labels, minimizing human annotation efforts. Modern machine learning systems, particularly deep learning frameworks, are increasingly computationally demanding and data-intensive [ 43, 1] .
Jun 19, 2024 · Harold W. Kuhn, BS 1947; Serge Lang, BS 1946; Benoît Mandelbrot, MS 1948, Eng 1949; pioneer of fractal geometry; Japan Prize laureate; Harvey Prize recipient; Wolf Prize winner; John McCarthy, BS 1948; inventor of the Lisp programming language and recipient of the 1971 Turing Award; Kyoto Prize laureate; National Medal of Science recipient
Jun 10, 2024 · In this paper, we tackle the problem of novel visual category discovery, i.e., grouping unlabelled images from new classes into different semantic partitions by leveraging a labelled dataset that contains images from other different but relevant categories. This is a more realistic and challenging setting than conventional semi-supervised learning.
