1 code implementation • 27 Dec 2023 • Maghsood Salimi, Mohammad Loni, Sara Afshar, Antonio Cicchetti, Marjan Sirjani
The increasing demand for autonomous machines in construction environments necessitates the development of robust object detection algorithms that can perform effectively across various weather and environmental conditions.
1 code implementation • 18 May 2023 • Mohammad Loni, Aditya Mohan, Mehdi Asadi, Marius Lindauer
By conducting experiments on popular DNN models (LeNet-5, VGG-16, ResNet-18, and EfficientNet-B0) trained on MNIST, CIFAR-10, and ImageNet-16 datasets, we show that the novel combination of these two approaches, dubbed Sparse Activation Function Search, short: SAFS, results in up to 15. 53%, 8. 88%, and 6. 33% absolute improvement in the accuracy for LeNet-5, VGG-16, and ResNet-18 over the default training protocols, especially at high pruning ratios.
no code implementations • 17 Jan 2023 • Mehdi Asadi, Fatemeh Poursalim, Mohammad Loni, Masoud Daneshtalab, Mikael Sjödin, Arash Gharehbaghi
This paper presents a novel machine learning framework for detecting Paroxysmal Atrial Fibrillation (PxAF), a pathological characteristic of Electrocardiogram (ECG) that can lead to fatal conditions such as heart attack.
1 code implementation • International Conference on Artificial Neural Networks 2022 2022 • Ali Zoljodi, Mohammad Loni, Sadegh Abadijou, Mina Alibeigi & Masoud Daneshtalab
Lane detection is one of the most fundamental tasks for autonomous driving.
1 code implementation • 14 Jul 2022 • Hamid Mousavi, Mohammad Loni, Mina Alibeigi, Masoud Daneshtalab
In this paper, we propose a new method to search for sparsity-friendly neural architectures.
1 code implementation • Design, Automation and Test in Europe Conference (DATE) 2022 • Mohammad Loni, Hamid Mousavi, Mohammad Riazati, Masoud Daneshtalab, and Mikael Sjodin
This paper proposes TAS, a framework that drastically reduces the accuracy gap between TNNs and their full-precision counterparts by integrating quantization into the network design.