3 accepted papers
Machine learning (ML) has been actively adopted in Linear Programming (LP) and Mixed-Integer Linear Programming (MILP), whose potential is hindered by instance scarcity. Current synthetic instance generation methods often fall short in closely mirroring the distribution of original datasets or ensur…
We propose a novel multi-scale neural network for Granger causality discovery (MSNGC) in multivariate time series. Compared with existing counterparts, our model avoids the explicit data segmentation between series and between time lags for the first time. By extracting diverse causal information fr…
Standard classification models rely on the assumption that all the classes of interest are equally represented in training datasets. However, visual phenomena exhibit a long-tailed distribution, such that many standard approaches fail to properly model and result in a considerable degeneration on ac…