$ECQ$^{\text{x}}$: Explainability-Driven Quantization for Low-Bit and Sparse DNNs$

ECQ $^{\text{x}}$ : Explainability-Driven Quantization for Low-Bit and Sparse DNNs

9 September 2021

Maximilian Dreyer

Karsten Müller

Sebastian Lapuschkin

Papers citing "ECQ$^{\text{x}}$: Explainability-Driven Quantization for Low-Bit and Sparse DNNs"

5 / 5 papers shown

Title
Explainable Artificial Intelligence Applications in Cyber Security: State-of-the-Art in Research Zhibo Zhang H. A. Hamadi Ernesto Damiani C. Yeun Fatma Taher AAML 29 148 0 31 Aug 2022
QuantNAS for super resolution: searching for efficient quantization-friendly architectures against quantization noise Egor Shvetsov Dmitry Osin Alexey Zaytsev Ivan Koryakovskiy Valentin Buchnev I. Trofimov Evgeny Burnaev MQ 23 2 0 31 Aug 2022
Explain to Not Forget: Defending Against Catastrophic Forgetting with XAI Sami Ede Serop Baghdadlian Leander Weber A. Nguyen Dario Zanca Wojciech Samek Sebastian Lapuschkin CLL 27 6 0 04 May 2022
Sparsity in Deep Learning: Pruning and growth for efficient inference and training in neural networks Torsten Hoefler Dan Alistarh Tal Ben-Nun Nikoli Dryden Alexandra Peste MQ 141 684 0 31 Jan 2021
Methods for Interpreting and Understanding Deep Neural Networks G. Montavon Wojciech Samek K. Müller FaML 234 2,238 0 24 Jun 2017