Search Results for author: Arash Amini

Found 14 papers, 2 papers with code

Robustness Analysis of Classification Using Recurrent Neural Networks with Perturbed Sequential Input

no code implementations10 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.


Two-snapshot DOA Estimation via Hankel-structured Matrix Completion

no code implementations21 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).

Matrix Completion

Label consistency in overfitted generalized $k$-means

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.

Real-time Pose Estimation from Images for Multiple Humanoid Robots

1 code implementation6 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.

Pose Estimation

Robust Learning of Recurrent Neural Networks in Presence of Exogenous Noise

no code implementations3 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.

The Potts-Ising model for discrete multivariate data

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.

Compressibility Measures for Affinely Singular Random Vectors

no code implementations12 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.

Globally optimal score-based learning of directed acyclic graphs in high-dimensions

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.

Variable Importance Using Decision Trees

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.

Feature Importance

Fast Methods for Recovering Sparse Parameters in Linear Low Rank Models

no code implementations26 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.

Matrix Completion

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