39 citations · 61 across the 12 of their papers we have counts for
15 papers
CNLL: A Semi-supervised Approach For Continual Noisy Label Learning
Nazmul Karim, Umar Khalid, Ashkan Esmaeili +1
The task of continual learning requires careful design of algorithms that can tackle catastrophic forgetting. However, the noisy label, which is inevitable in a real-world scenario…
RODD: A Self-Supervised Approach for Robust Out-of-Distribution Detection
Umar Khalid, Ashkan Esmaeili, Nazmul Karim +1
Recent studies have addressed the concern of detecting and rejecting the out-of-distribution (OOD) samples as a major challenge in the safe deployment of deep learning (DL) models.…
Generative Model Adversarial Training for Deep Compressed Sensing
Ashkan Esmaeili
Deep compressed sensing assumes the data has sparse representation in a latent space, i.e., it is intrinsically of low-dimension. The original data is assumed to be mapped from a l…
Two-way Spectrum Pursuit for CUR Decomposition and Its Application in Joint Column/Row Subset Selection
Ashkan Esmaeili, Mohsen Joneidi, Mehrdad Salimitari +2
The problem of simultaneous column and row subset selection is addressed in this paper. The column space and row space of a matrix are spanned by its left and right singular vector…
LSDAT: Low-Rank and Sparse Decomposition for Decision-based Adversarial Attack
Ashkan Esmaeili, Marzieh Edraki, Nazanin Rahnavard +2
We propose LSDAT, an image-agnostic decision-based black-box attack that exploits low-rank and sparse decomposition (LSD) to dramatically reduce the number of queries and achieve s…
A Novel Approach to Sparse Inverse Covariance Estimation Using Transform Domain Updates and Exponentially Adaptive Thresholding
Ashkan Esmaeili, Farokh Marvasti
Sparse Inverse Covariance Estimation (SICE) is useful in many practical data analyses. Recovering the connectivity, non-connectivity graph of covariates is classified amongst the m…
A Novel Approach to Quantized Matrix Completion Using Huber Loss Measure
Ashkan Esmaeili, Farokh Marvasti
In this paper, we introduce a novel and robust approach to Quantized Matrix Completion (QMC). First, we propose a rank minimization problem with constraints induced by quantization…
Recovering Quantized Data with Missing Information Using Bilinear Factorization and Augmented Lagrangian Method
Ashkan Esmaeili, Kayhan Behdin, Sina Al-E-Mohammad +1
In this paper, we propose a novel approach in order to recover a quantized matrix with missing information. We propose a regularized convex cost function composed of a log-likeliho…
Transduction with Matrix Completion Using Smoothed Rank Function
Ashkan Esmaeili, Kayhan Behdin, Mohammad Amin Fakharian +1
In this paper, we propose two new algorithms for transduction with Matrix Completion (MC) problem. The joint MC and prediction tasks are addressed simultaneously to enhance the acc…
OBTAIN: Real-Time Beat Tracking in Audio Signals
Ali Mottaghi, Kayhan Behdin, Ashkan Esmaeili +2
In this paper, we design a system in order to perform the real-time beat tracking for an audio signal. We use Onset Strength Signal (OSS) to detect the onsets and estimate the temp…
New Methods of Enhancing Prediction Accuracy in Linear Models with Missing Data
Mohammad Amin Fakharian, Ashkan Esmaeili, Farokh Marvasti
In this paper, prediction for linear systems with missing information is investigated. New methods are introduced to improve the Mean Squared Error (MSE) on the test set in compari…
Iterative Null-space Projection Method with Adaptive Thresholding in Sparse Signal Recovery and Matrix Completion
Ashkan Esmaeili, Ehsan Asadi, Farokh Marvasti
Adaptive thresholding methods have proved to yield high SNRs and fast convergence in finding the solution to the Compressed Sensing (CS) problems. Recently, it was observed that th…
Using Empirical Covariance Matrix in Enhancing Prediction Accuracy of Linear Models with Missing Information
Ahmadreza Moradipari, Sina Shahsavari, Ashkan Esmaeili +1
Inference and Estimation in Missing Information (MI) scenarios are important topics in Statistical Learning Theory and Machine Learning (ML). In ML literature, attempts have been m…
Comparison of Several Sparse Recovery Methods for Low Rank Matrices with Random Samples
Ashkan Esmaeili, Farokh Marvasti
In this paper, we will investigate the efficacy of IMAT (Iterative Method of Adaptive Thresholding) in recovering the sparse signal (parameters) for linear models with missing data…
Fast Methods for Recovering Sparse Parameters in Linear Low Rank Models
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. However, only partial information about the matrix…