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Register a new userProceedings of the First Workshop on Weakly Supervised Learning (WeaSuL)
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Michael A. Hedderich
Publication date
2021
fields
Informatics Engineering
and research's language is
English
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Welcome to WeaSuL 2021, the First Workshop on Weakly Supervised Learning, co-located with ICLR 2021. In this workshop, we want to advance theory, methods and tools for allowing experts to express prior coded knowledge for automatic data annotations that can be used to train arbitrary deep neural networks for prediction. The ICLR 2021 Workshop on Weak Supervision aims at advancing methods that help modern machine-learning methods to generalize from knowledge provided by experts, in interaction with observable (unlabeled) data. In total, 15 papers were accepted. All the accepted contributions are listed in these Proceedings.
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Proceedings of the First International Workshop on Deep Learning and Music, joint with IJCNN, Anchorage, US, May 17-18, 2017
These are the proceedings of the 4th workshop on Machine Learning for the Developing World (ML4D), held as part of the Thirty-fourth Conference on Neural Information Processing Systems (NeurIPS) on Saturday, December 12th 2020.
With the wide application of machine learning algorithms to the real world, class imbalance and concept drift have become crucial learning issues. Class imbalance happens when the data categories are not equally represented, i.e., at least one category is minority compared to other categories. It can cause learning bias towards the majority class and poor generalization. Concept drift is a change in the underlying distribution of the problem, and is a significant issue specially when learning from data streams. It requires learners to be adaptive to dynamic changes. Class imbalance and concept drift can significantly hinder predictive performance, and the problem becomes particularly challenging when they occur simultaneously. This challenge arises from the fact that one problem can affect the treatment of the other. For example, drift detection algorithms based on the traditional classification error may be sensitive to the imbalanced degree and become less effective; and class imbalance techniques need to be adaptive to changing imbalance rates, otherwise the class receiving the preferential treatment may not be the correct minority class at the current moment. Therefore, the mutual effect of class imbalance and concept drift should be considered during algorithm design. The aim of this workshop is to bring together researchers from the areas of class imbalance learning and concept drift in order to encourage discussions and new collaborations on solving the combined issue of class imbalance and concept drift. It provides a forum for international researchers and practitioners to share and discuss their original work on addressing new challenges and research issues in class imbalance learning, concept drift, and the combined issues of class imbalance and concept drift. The proceedings include 8 papers on these topics.
We present the activities performed during the first MadAnalysis 5 workshop on LHC recasting that has been organized at High 1 (Gangwon privince, Korea) on August 20-27, 2017. This report includes details on the implementation in the MadAnalysis 5 framework of eight ATLAS and CMS analyses, as well as a description of the corresponding validation and the various issues that have been observed.
This volume contains the proceedings of the First Workshop on Agents and Robots for reliable Engineered Autonomy (AREA 2020), co-located with the 24th European Conference on Artificial Intelligence (ECAI 2020). AREA brings together researchers from autonomous agents, software engineering and robotic communities, as combining knowledge coming from these research areas may lead to innovative approaches that solve complex problems related with the verification and validation of autonomous robotic systems.
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