no code implementations • 15 Apr 2022 • Mohammad Reza Karimi Dastjerdi, Yannick Hold-Geoffroy, Jonathan Eisenmann, Siavash Khodadadeh, Jean-François Lalonde
We propose a method to extrapolate a 360{\deg} field of view from a single image that allows for user-controlled synthesis of the out-painted content.
no code implementations • 2 Dec 2021 • Sharare Zehtabian, Siavash Khodadadeh, Damla Turgut, Ladislau Bölöni
Throughout the Covid-19 pandemic, a significant amount of effort had been put into developing techniques that predict the number of infections under various assumptions about the public policy and non-pharmaceutical interventions.
no code implementations • 17 Jan 2021 • Sharare Zehtabian, Siavash Khodadadeh, Ladislau Bölöni, Damla Turgut
The daily activities performed by a disabled or elderly person can be monitored by a smart environment, and the acquired data can be used to learn a predictive model of user behavior.
no code implementations • 1 Jan 2021 • Saeed Vahidian, Mohsen Joneidi, Ashkan Esmaeili, Siavash Khodadadeh, Sharare Zehtabian, Ladislau Boloni, Nazanin Rahnavard, Bill Lin, Mubarak Shah
The approach is based on the concept of {\em self-rank}, defined as the minimum number of samples needed to reconstruct all samples with an accuracy proportional to the rank-$K$ approximation.
no code implementations • ICLR 2021 • Siavash Khodadadeh, Sharare Zehtabian, Saeed Vahidian, Weijia Wang, Bill Lin, Ladislau Bölöni
Unsupervised meta-learning approaches rely on synthetic meta-tasks that are created using techniques such as random selection, clustering and/or augmentation.
no code implementations • NeurIPS 2019 • Siavash Khodadadeh, Ladislau Bölöni, Mubarak Shah
In this paper, we propose UMTRA, an algorithm that performs unsupervised, model-agnostic meta-learning for classification tasks.