diagNNose: A Library for Neural Activation Analysis

Jaap Jumelet


Abstract
In this paper we introduce diagNNose, an open source library for analysing the activations of deep neural networks. diagNNose contains a wide array of interpretability techniques that provide fundamental insights into the inner workings of neural networks. We demonstrate the functionality of diagNNose with a case study on subject-verb agreement within language models. diagNNose is available at https://github.com/i-machine-think/diagnnose.
Anthology ID:
2020.blackboxnlp-1.32
Volume:
Proceedings of the Third BlackboxNLP Workshop on Analyzing and Interpreting Neural Networks for NLP
Month:
November
Year:
2020
Address:
Online
Venues:
BlackboxNLP | EMNLP
SIG:
Publisher:
Association for Computational Linguistics
Note:
Pages:
342–350
Language:
URL:
https://www.aclweb.org/anthology/2020.blackboxnlp-1.32
DOI:
10.18653/v1/2020.blackboxnlp-1.32
Bib Export formats:
BibTeX MODS XML EndNote
PDF:
http://aclanthology.lst.uni-saarland.de/2020.blackboxnlp-1.32.pdf