no code implementations • 7 Feb 2024 • Arash Amini, Yigit Ege Bayiz, Ashwin Ram, Radu Marculescu, Ufuk Topcu
In the era of social media platforms, identifying the credibility of online content is crucial to combat misinformation.
no code implementations • 28 Dec 2023 • Amirhossein Javaheri, Arash Amini, Farokh Marvasti, Daniel P. Palomar
Learning a graph from data is the key to taking advantage of graph signal processing tools.
no code implementations • 8 Nov 2023 • Mohammad Bokaei, Saeed Razavikia, Stefano Rini, Arash Amini, Hamid Behrouzi
In this paper, we investigate the problem of recovering the frequency components of a mixture of $K$ complex sinusoids from a random subset of $N$ equally-spaced time-domain samples.
no code implementations • 17 Oct 2023 • MohammadHossein Ashoori, Arash Amini
Super-resolution (SR) is the technique of increasing the nominal resolution of image / video content accompanied with quality improvement.
no code implementations • 10 Sep 2023 • Vivek Pandey, Guangyi Liu, Arash Amini, Nader Motee
In this paper, we propose a distributionally robust risk framework to investigate cascading failures in platoons.
no code implementations • 21 Jul 2023 • Arul Selvam Periyasamy, Arash Amini, Vladimir Tsaturyan, Sven Behnke
6D object pose estimation is a crucial prerequisite for autonomous robot manipulation applications.
no code implementations • 20 Nov 2022 • Arash Amini, Qiyu Sun, Nader Motee
We consider a class of stochastic dynamical networks whose governing dynamics can be modeled using a coupling function.
no code implementations • 5 May 2022 • Arash Amini, Arul Selvam Periyasamy, Sven Behnke
6D object pose estimation is a crucial prerequisite for autonomous robot manipulation applications.
no code implementations • 10 Mar 2022 • Guangyi Liu, Arash Amini, Martin Takac, Nader Motee
For a given stable recurrent neural network (RNN) that is trained to perform a classification task using sequential inputs, we quantify explicit robustness bounds as a function of trainable weight matrices.
1 code implementation • 4 Mar 2022 • Luyi Shen, Arash Amini, Nathaniel Josephs, Lizhen Lin
The increasing prevalence of network data in a vast variety of fields and the need to extract useful information out of them have spurred fast developments in related models and algorithms.
no code implementations • 21 Feb 2022 • Mohammad Bokaei, Saeed Razavikia, Arash Amini, Stefano Rini
In this paper, we study the problem of estimating the direction of arrival (DOA) using a sparsely sampled uniform linear array (ULA).
no code implementations • NeurIPS 2021 • Linfan Zhang, Arash Amini
We provide conditions under which the estimated labels are close to a refinement of the true cluster labels.
no code implementations • 22 Sep 2021 • Arash Amini, Arul Selvam Periyasamy, Sven Behnke
We evaluate the performance of our method on the YCB-Video dataset.
1 code implementation • 6 Jul 2021 • Arash Amini, Hafez Farazi, Sven Behnke
Pose estimation commonly refers to computer vision methods that recognize people's body postures in images or videos.
no code implementations • 3 May 2021 • Arash Amini, Guangyi Liu, Nader Motee
However, artificial neural networks are known to exhibit poor robustness in presence of input noise, where the sequential architecture of RNNs exacerbates the problem.
no code implementations • 1 Jan 2021 • Ali Shirali, Reza Kazemi, Arash Amini
The performance of the method is evaluated on two benchmark datasets (ML-100k and ML-1M).
no code implementations • 18 Dec 2020 • Guangyi Liu, Arash Amini, Martin Takáč, Héctor Muñoz-Avila, Nader Motee
We consider the problem of classifying a map using a team of communicating robots.
1 code implementation • NeurIPS 2020 • Zahra Razaee, Arash Amini
We introduce a variation on the Potts model that allows for general categorical marginals and Ising-type multivariate dependence.
1 code implementation • IEEE conference 2020 • Samira Malek, Saber Salehkaleybar, Arash Amini
In this paper, we introduce a new network architecture by increasing the number of variable-node layers, while keeping the check-node layers unchanged.
no code implementations • 12 Jan 2020 • Mohammad-Amin Charusaie, Arash Amini, Stefano Rini
When considering discrete-domain moving-average processes with non-Gaussian excitation noise, the above results allow us to evaluate the block-average RID and DRB, as well as to determine a relationship between these parameters and other existing compressibility measures.
no code implementations • NeurIPS 2019 • Bryon Aragam, Arash Amini, Qing Zhou
We prove that $\Omega(s\log p)$ samples suffice to learn a sparse Gaussian directed acyclic graph (DAG) from data, where $s$ is the maximum Markov blanket size.
no code implementations • NeurIPS 2017 • Jalil Kazemitabar, Arash Amini, Adam Bloniarz, Ameet S. Talwalkar
Decision trees and random forests are well established models that not only offer good predictive performance, but also provide rich feature importance information.
no code implementations • 26 Jun 2016 • Ashkan Esmaeili, Arash Amini, Farokh Marvasti
In this paper, we investigate the recovery of a sparse weight vector (parameters vector) from a set of noisy linear combinations.