1 citations · 1 across the 4 of their papers we have counts for
9 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…
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…