dblp: Learning Analytics and Knowledge
4 days ago · Proceedings of CrossMMLA in practice: Collecting, annotating and analyzing multimodal data across spaces co-located with 10th International Learning and Analytics …
EOLE assumes that it can also be a way to address student dropout, and thus proposes to design a dashboard that is targeting students. The key point in this design is that students are at its core.
Learnable Bernoulli Dropout for Bayesian Deep Learning
Learnable Bernoulli Dropout for Bayesian Deep Learning. S. Boluki, R. Ardywibowo, S. Dadaneh, M. Zhou, and X. Qian. AISTATS, volume 108 of Proceedings of Machine Learning Research, …
dblp: Learning Analytics and Knowledge 2020
Jan 11, 2024 · LAK '20: 10th International Conference on Learning Analytics and Knowledge, Frankfurt, Germany, March 23-27, 2020. ACM 2020, ISBN 978-1-4503-7712-6. The …
LAK20 Conference Proceedings Celebrating 10 years of LAK: Shaping the future of the field
Dropout vs. batch normalization: an empirical study of their …
Jan 22, 2020 · In this paper we conduct an empirical study to investigate the effect of dropout and batch normalization on training deep learning models.
dblp: BPE-Dropout: Simple and Effective Subword Regularization.
Aug 6, 2021 · Bibliographic details on BPE-Dropout: Simple and Effective Subword Regularization.
Meta Dropout: Learning to Perturb Latent Features for
Meta Dropout: Learning to Perturb Latent Features for Generalization. H. Lee, T. Nam, E. Yang, and S. Hwang. ICLR, OpenReview.net, (2020)
LAK: Lasso and K-Means Based Single-Cell RNA-Seq Data …
In this study, we have developed an imputation method (GraphSCI) to impute the dropout events in scRNA-seq data based on the graph convolution networks. The method takes advantage of …
How Does Learning Analytics Contribute to Prevent Students’ Dropout …
Nov 4, 2021 · Retention and dropout of higher education students is a subject that must be analysed carefully. Learning analytics can be used to help prevent failure cases. The purpose …