Rahim Entezari

I am a PhD studet at Institute for Technical Informatics, TU Graz and Complexity Science Hub, Vienna. I try to understand how/why deep learning works.

Before that, I did my master in AI at IUST and bachelor in computer engineering at AUT, Iran.

I am on the job market!

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Research

I'm interested in deep learning, optimization/generalization and sparsity. Much of my research is about understaning deep learning phenomena.

The Role of Permutation Invariance in Linear Mode Connectivity of Neural Networks
Rahim Entezari, Hanie Sedghi, Olga Saukh, Behnam Neyshabur
Accpeted at ICLR, 2022
Twitter / arXiv

We conjecture that if the permutation invariance of neural networks is taken into account, SGD solutions will likely have no barrier in the linear interpolation between them.

Understanding the effect of sparsity on neural networks robustness
Lukas Timple*, Rahim Entezari*, Hanie Sedghi, Behnam Neyshabur, Olga Saukh
ICML Overparameterization workshop, 2021
Paper

We show that, up to a certain sparsity achieved by increasing network width and depth while keeping the network capacity fixed, sparsified networks consistently match and often outperform their initially dense versions.

Class-dependent Pruning of Deep Neural Networks
Rahim Entezari, Olga Saukh
IEEE Second Workshop on Machine Learning on Edge in Sensor Systems (SenSys-ML), 2020
arXiv

we propose an iterative deep model compression technique, which keeps the number of false negatives of the compressed model close to the one of the original model at the price of increasing the number of false positives if necessary.

Deep and Efficient Impact Models for Edge Characterization and Control of Energy Events
Grigore Stamatescu, Rahim Entezari, Kay Roemer, Olga Saukh
IEEE 25th International Conference on Parallel and Distributed Systems (ICPADS), 2019
Paper

we present a hierarchical energy system architecture with embedded control for network control in microgrids.

Avid: Adversarial visual irregularity detection
Mohammad Sabokrou, Masoud Pourreza, Mohsen Fayyaz, Rahim Entezari, Mahmood Fathy, Juergen Gall, Ehsan Adeli
Asian Conference on Computer Vision (ACCV), 2018
arXiv

We propose an end-to-end deep network for detection and fine localization of irregularities in videos (and images).


Thanks Jon!