Search Results for author:
Found 15 papers, 3 papers with code
CNLL: A Semi-supervised Approach For Continual Noisy Label Learning
After purification, we perform fine-tuning in a semi-supervised fashion that ensures the participation of all available samples.
RODD: A Self-Supervised Approach for Robust Out-of-Distribution Detection
The method proposed in this work referred to as RODD outperforms SOTA detection performance on an extensive suite of benchmark datasets on OOD detection tasks.
Ranked #1 on
Out-of-Distribution Detection
on cifar100
(using extra training data)
Generative Model Adversarial Training for Deep Compressed Sensing
Deep compressed sensing assumes the data has sparse representation in a latent space, i. e., it is intrinsically of low-dimension.
Two-way Spectrum Pursuit for CUR Decomposition and Its Application in Joint Column/Row Subset Selection
The problem of simultaneous column and row subset selection is addressed in this paper.
LSDAT: Low-Rank and Sparse Decomposition for Decision-based Adversarial Attack
It is set forth that the proposed sparse perturbation is the most aligned sparse perturbation with the shortest path from the input sample to the decision boundary for some initial adversarial sample (the best sparse approximation of shortest path, likely to fool the model).
Asymptotic Optimality of Self-Representative Low-Rank Approximation and Its Applications
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.
A Novel Approach to Sparse Inverse Covariance Estimation Using Transform Domain Updates and Exponentially Adaptive Thresholding
Sparse Inverse Covariance Estimation (SICE) is useful in many practical data analyses.
A Novel Approach to Quantized Matrix Completion Using Huber Loss Measure
Next, we form an unconstrained optimization problem by regularizing the rank function with Huber loss.
Recovering Quantized Data with Missing Information Using Bilinear Factorization and Augmented Lagrangian Method
In this paper, we propose a novel approach in order to recover a quantized matrix with missing information.
Transduction with Matrix Completion Using Smoothed Rank Function
In this paper, we propose two new algorithms for transduction with Matrix Completion (MC) problem.
OBTAIN: Real-Time Beat Tracking in Audio Signals
In this paper, we design a system in order to perform the real-time beat tracking for an audio signal.
New Methods of Enhancing Prediction Accuracy in Linear Models with Missing Data
The algorithm is then modified and optimized for missing scenarios.
Using Empirical Covariance Matrix in Enhancing Prediction Accuracy of Linear Models with Missing Information
When sparse models are also suffering from MI, the sparse recovery and inference of the missing models are taken into account simultaneously.
Fast Methods for Recovering Sparse Parameters in Linear Low Rank Models
In this paper, we investigate the recovery of a sparse weight vector (parameters vector) from a set of noisy linear combinations.
Comparison of Several Sparse Recovery Methods for Low Rank Matrices with Random Samples
This paper will focus on comparing the power of IMAT in reconstruction of the desired sparse signal with LASSO.
