Quantifying the separability of data classes in neural networks-Reference-Cited by-同舟云学术

Quantifying the separability of data classes in neural networks

Published:2021-07 Issue: Volume:139 Page:278-293
ISSN:0893-6080
Container-title:Neural Networks
language:en
Short-container-title:Neural Networks

Author:

Schilling Achim^ORCID,Maier Andreas,Gerum Richard,Metzner Claus^ORCID,Krauss Patrick^ORCID

Funder

Deutsche Forschungsgemeinschaft

Publisher

Elsevier BV

Subject

Artificial Intelligence,Cognitive Neuroscience

Reference106 articles.

1. Abadi, Martín, Barham, Paul, Chen, Jianmin, Chen, Zhifeng, Davis, Andy, & Dean, Jeffrey, et al. (2016). TensorFlow: A system for large-scale machine learning TensorFlow. In 12th USENIX symposium on operating systems design and implementation (Vol. 16) (pp. 265–284).

2. On the surprising behavior of distance metrics in high dimensional space;Aggarwal,2001

3. Artwork personalization at netflix;Amat,2018

4. Vector-based navigation using grid-like representations in artificial agents;Banino;Nature,2018

5. Greedy layer-wise training of deep networks;Bengio,2007

Cited by 31 articles. 订阅此论文施引文献订阅此论文施引文献，注册后可以免费订阅5篇论文的施引文献，订阅后可以查看论文全部施引文献

1. Estimating data complexity and drift through a multiscale generalized impurity approach;Journal of Computational Mathematics and Data Science;2024-09

2. Deep learning based decoding of single local field potential events;NeuroImage;2024-08

3. Abstaining ECG Classifiers Through Explainable Prototypical Spaces;2024 IEEE 12th International Conference on Healthcare Informatics (ICHI);2024-06-03

4. Quantifying and Maximizing the Information Flux in Recurrent Neural Networks;Neural Computation;2024-02-16

5. Virtual Environment Model Generation for CPS Goal Verification using Imitation Learning;ACM Transactions on Embedded Computing Systems;2024-01-10