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

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anaerobic

思乡病

Irrepressible

bonnet

Continuous Depth Recurrent Neural Differential Equationsations over both depth and time to predict an output for a given input in the sequence. Specifically, we propose continuous depth recurrent neural differential equations (CDR-NDE) which generalize RNN models by continuously evolving the hidden states in both the temporal and depth dimensions. CDR-ND

Pathogen

Mitigating Algorithmic Bias with Limited Annotationsand it is theoretically proved to be capable of bounding the algorithmic bias. According to the evaluation on five benchmark datasets, APOD outperforms the state-of-the-arts baseline methods under the limited annotation budget, and shows comparable performance to fully annotated bias mitigation, whi

Alopecia-Areata

Infant

修改

Sample Prior Guided Robust Model Learning to Suppress Noisy Labelsabels have two key steps: 1) dividing samples into cleanly labeled and wrongly labeled sets by training loss, 2) using semi-supervised methods to generate pseudo-labels for samples in the wrongly labeled set. However, current methods always hurt the informative hard samples due to the similar loss d

SAGE

DCID: Deep Canonical Information Decompositionons. Canonical Correlation Analysis (CCA)-based methods have traditionally been used to identify shared variables, however, they were designed for multivariate targets and only offer trivial solutions for univariate cases. In the context of Multi-Task Learning (MTL), various models were postulated t