---
_id: '6985'
abstract:
- lang: eng
  text: In this paper, we introduce a novel method to interpret recurrent neural networks
    (RNNs), particularly long short-term memory networks (LSTMs) at the cellular level.
    We propose a systematic pipeline for interpreting individual hidden state dynamics
    within the network using response characterization methods. The ranked contribution
    of individual cells to the network's output is computed by analyzing a set of
    interpretable metrics of their decoupled step and sinusoidal responses. As a result,
    our method is able to uniquely identify neurons with insightful dynamics, quantify
    relationships between dynamical properties and test accuracy through ablation
    analysis, and interpret the impact of network capacity on a network's dynamical
    distribution. Finally, we demonstrate the generalizability and scalability of
    our method by evaluating a series of different benchmark sequential datasets.
article_number: '8851954'
arxiv: 1
author:
- first_name: Ramin
  full_name: Hasani, Ramin
  last_name: Hasani
- first_name: Alexander
  full_name: Amini, Alexander
  last_name: Amini
- first_name: Mathias
  full_name: Lechner, Mathias
  id: 3DC22916-F248-11E8-B48F-1D18A9856A87
  last_name: Lechner
- first_name: Felix
  full_name: Naser, Felix
  last_name: Naser
- first_name: Radu
  full_name: Grosu, Radu
  last_name: Grosu
- first_name: Daniela
  full_name: Rus, Daniela
  last_name: Rus
citation:
  ama: 'Hasani R, Amini A, Lechner M, Naser F, Grosu R, Rus D. Response characterization
    for auditing cell dynamics in long short-term memory networks. In: <i>Proceedings
    of the International Joint Conference on Neural Networks</i>. IEEE; 2019. doi:<a
    href="https://doi.org/10.1109/ijcnn.2019.8851954">10.1109/ijcnn.2019.8851954</a>'
  apa: 'Hasani, R., Amini, A., Lechner, M., Naser, F., Grosu, R., &#38; Rus, D. (2019).
    Response characterization for auditing cell dynamics in long short-term memory
    networks. In <i>Proceedings of the International Joint Conference on Neural Networks</i>.
    Budapest, Hungary: IEEE. <a href="https://doi.org/10.1109/ijcnn.2019.8851954">https://doi.org/10.1109/ijcnn.2019.8851954</a>'
  chicago: Hasani, Ramin, Alexander Amini, Mathias Lechner, Felix Naser, Radu Grosu,
    and Daniela Rus. “Response Characterization for Auditing Cell Dynamics in Long
    Short-Term Memory Networks.” In <i>Proceedings of the International Joint Conference
    on Neural Networks</i>. IEEE, 2019. <a href="https://doi.org/10.1109/ijcnn.2019.8851954">https://doi.org/10.1109/ijcnn.2019.8851954</a>.
  ieee: R. Hasani, A. Amini, M. Lechner, F. Naser, R. Grosu, and D. Rus, “Response
    characterization for auditing cell dynamics in long short-term memory networks,”
    in <i>Proceedings of the International Joint Conference on Neural Networks</i>,
    Budapest, Hungary, 2019.
  ista: 'Hasani R, Amini A, Lechner M, Naser F, Grosu R, Rus D. 2019. Response characterization
    for auditing cell dynamics in long short-term memory networks. Proceedings of
    the International Joint Conference on Neural Networks. IJCNN: International Joint
    Conference on Neural Networks, 8851954.'
  mla: Hasani, Ramin, et al. “Response Characterization for Auditing Cell Dynamics
    in Long Short-Term Memory Networks.” <i>Proceedings of the International Joint
    Conference on Neural Networks</i>, 8851954, IEEE, 2019, doi:<a href="https://doi.org/10.1109/ijcnn.2019.8851954">10.1109/ijcnn.2019.8851954</a>.
  short: R. Hasani, A. Amini, M. Lechner, F. Naser, R. Grosu, D. Rus, in:, Proceedings
    of the International Joint Conference on Neural Networks, IEEE, 2019.
conference:
  end_date: 2019-07-19
  location: Budapest, Hungary
  name: 'IJCNN: International Joint Conference on Neural Networks'
  start_date: 2019-07-14
date_created: 2019-11-04T15:59:58Z
date_published: 2019-09-30T00:00:00Z
date_updated: 2021-01-12T08:11:19Z
day: '30'
department:
- _id: ToHe
doi: 10.1109/ijcnn.2019.8851954
external_id:
  arxiv:
  - '1809.03864'
language:
- iso: eng
main_file_link:
- open_access: '1'
  url: https://arxiv.org/abs/1809.03864
month: '09'
oa: 1
oa_version: Preprint
publication: Proceedings of the International Joint Conference on Neural Networks
publication_identifier:
  isbn:
  - '9781728119854'
publication_status: published
publisher: IEEE
quality_controlled: '1'
scopus_import: 1
status: public
title: Response characterization for auditing cell dynamics in long short-term memory
  networks
type: conference
user_id: 2DF688A6-F248-11E8-B48F-1D18A9856A87
year: '2019'
...