all AI news
Robust Explainability: A Tutorial on Gradient-Based Attribution Methods for Deep Neural Networks. (arXiv:2107.11400v4 [cs.LG] UPDATED)
Jan. 17, 2022, 2:10 a.m. | Ian E. Nielsen, Dimah Dera, Ghulam Rasool, Nidhal Bouaynaya, Ravi P. Ramachandran
cs.LG updates on arXiv.org arxiv.org
With the rise of deep neural networks, the challenge of explaining the
predictions of these networks has become increasingly recognized. While many
methods for explaining the decisions of deep neural networks exist, there is
currently no consensus on how to evaluate them. On the other hand, robustness
is a popular topic for deep learning research; however, it is hardly talked
about in explainability until very recently. In this tutorial paper, we start
by presenting gradient-based interpretability methods. These techniques use …
arxiv attribution explainability gradient networks neural networks tutorial
More from arxiv.org / cs.LG updates on arXiv.org
Jobs in AI, ML, Big Data
Senior ML Researcher - 3D Geometry Processing | 3D Shape Generation | 3D Mesh Data
@ Promaton | Europe
Principal Data Engineer
@ RS21 | Remote
SQL/Power BI Developer
@ ICF | Virginia Remote Office (VA99)
Senior Machine Learning Engineer (Canada Remote)
@ Fullscript | Ottawa, ON
Software Engineer - MLOps.
@ Renesas Electronics | Toyosu, Japan
Junior Data Scientist / Artificial Intelligence consultant
@ Deloitte | Luxembourg, LU