4 citations · 4 across the 6 of their papers we have counts for
6 papers
Brain-Aligned Multi-Stream Video Transformers with Sparse Self-Selection
Amir Hosein Fadaei, Mahyar Maleki, Mohammad-Reza A. Dehaqani
Modern video transformers typically ignore principles from primate vision and are rarely evaluated against neural data, limiting their biological interpretability. We introduce a s…
Enhancing Video Understanding: Deep Neural Networks for Spatiotemporal Analysis
Amir Hosein Fadaei, Mohammad-Reza A. Dehaqani
It's no secret that video has become the primary way we share information online. That's why there's been a surge in demand for algorithms that can analyze and understand video con…
Beyond still images: Temporal features and input variance resilience
Amir Hosein Fadaei, Mohammad-Reza A. Dehaqani
Traditionally, vision models have predominantly relied on spatial features extracted from static images, deviating from the continuous stream of spatiotemporal features processed b…
Spyker: High-performance Library for Spiking Deep Neural Networks
Shahriar Rezghi Shirsavar, Mohammad-Reza A. Dehaqani
Spiking neural networks (SNNs) have been recently brought to light due to their promising capabilities. SNNs simulate the brain with higher biological plausibility compared to prev…
Models Developed for Spiking Neural Networks
Shahriar Rezghi Shirsavar, Abdol-Hossein Vahabie, Mohammad-Reza A. Dehaqani
Emergence of deep neural networks (DNNs) has raised enormous attention towards artificial neural networks (ANNs) once again. They have become the state-of-the-art models and have w…
A Faster Approach to Spiking Deep Convolutional Neural Networks
Shahriar Rezghi Shirsavar, Mohammad-Reza A. Dehaqani
Spiking neural networks (SNNs) have closer dynamics to the brain than current deep neural networks. Their low power consumption and sample efficiency make these networks interestin…