3 citations · 3 across the 3 of their papers we have counts for
4 papers
Polynomial, trigonometric, and tropical activations
Ismail Khalfaoui-Hassani, Stefan Kesselheim
Which functions can be used as activations in deep neural networks? This article explores families of functions based on orthonormal bases, including the Hermite polynomial basis a…
Dilated Convolution with Learnable Spacings
Ismail Khalfaoui-Hassani
This thesis presents and evaluates the Dilated Convolution with Learnable Spacings (DCLS) method. Through various supervised learning experiments in the fields of computer vision,…
Dilated Convolution with Learnable Spacings makes visual models more aligned with humans: a Grad-CAM study
Rabih Chamas, Ismail Khalfaoui-Hassani, Timothee Masquelier
Dilated Convolution with Learnable Spacing (DCLS) is a recent advanced convolution method that allows enlarging the receptive fields (RF) without increasing the number of parameter…
Drastically Reducing the Number of Trainable Parameters in Deep CNNs by Inter-layer Kernel-sharing
Alireza Azadbakht, Saeed Reza Kheradpisheh, Ismail Khalfaoui-Hassani +1
Deep convolutional neural networks (DCNNs) have become the state-of-the-art (SOTA) approach for many computer vision tasks: image classification, object detection, semantic segment…