Titlebook: Machine Learning and Knowledge Discovery in Databases: Research Track; European Conference, Danai Koutra,Claudia Plant,Francesco Bonchi Con

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Graph Rebasing and?Joint Similarity Reconstruction for?Cross-Modal Hash Retrievallarity is insufficient, and the large gap between modalities leads to semantic bias. In this paper, we propose a Graph Rebasing and Joint Similarity Reconstruction (GRJSR) method for cross-modal hash retrieval. Particularly, the graph rebasing module is used to filter out graph nodes with weak simil

modest

ARConvL: Adaptive Region-Based Convolutional Learning for?Multi-class Imbalance Classificationity classes. A typical way to address such problem is to adjust the loss function of deep networks by making use of class imbalance ratios. However, such static between-class imbalance ratios cannot monitor the changing latent feature distributions that are continuously learned by the deep network t

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Rényi Divergence Deep Mutual Learningeibler divergence, which is more flexible and tunable, to improve vanilla DML. This modification is able to consistently improve performance over vanilla DML with limited additional complexity. The convergence properties of the proposed paradigm are analyzed theoretically, and Stochastic Gradient De

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Scoring Rule Nets: Beyond Mean Target Prediction in?Multivariate Regressionis mostly problematic in the multivariate domain. While univariate models often optimize the popular Continuous Ranked Probability Score (CRPS), in the multivariate domain, no such alternative to MLE has yet been widely accepted. The Energy Score – the most investigated alternative – notoriously lac