# Publications

You can also find my articles on my Google Scholar profile.

## Revisiting local neighborhood methods in machine learning

Invited paper Published in IEEE Data Science and Learning Workshop (DSLW), 2021

Several machine learning methods leverage the idea of locality by using $k$-nearest neighbor (KNN) techniques to design better pattern recognition models. However, the choice of KNN parameters such as $k$ is often made experimentally, e.g., via cross-validation, leading to local neighborhoods without a clear geometric interpretation. Read more

## Efficient graph construction for image representation

Best student paper Published in IEEE International Conference on Image Processing (ICIP), 2020

Graphs are useful to interpret widely used image processing methods, e.g., bilateral filtering, or to develop new ones, e.g., kernel based techniques. However, simple graph constructions are often used, where edge weight and connectivity depend on a few parameters. In particular, the sparsity of the graph is determined by the choice of a window size. Read more

## Graph-based Deep Learning Analysis and Instance Selection

Published in IEEE International Workshop on Multimedia Signal Processing (MMSP), 2020

While deep learning is a powerful tool for manyapplications, there has been only limited research about selectionof data for training, i.e., instance selection, which enhances deeplearning scalability by saving computational resources. Read more

## DeepNNK: Explaining deep models and their generalization using polytope interpolation

Published in arXiv Preprints, 2020

Modern machine learning systems based on neural networks have shown great success in learning complex data patterns while being able to make good predictions on unseen data points. However, the limited interpretability of these systems hinders further progress and application to several domains in the real world. Read more

## Graph Construction from Data by Non-Negative Kernel Regression

Published in IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), 2020

Data driven graph constructions are often used in machine learning applications. However, learning an optimal graph from data is still a challenging task. K-nearest neighbor and ϵ-neighborhood methods are among the most common graph construction methods, due to their computational simplicity, but the choice of parameters such as K and ϵ associated with these methods is often ad hoc and lacks a clear interpretation. Read more

## Graph construction from data using non negative kernel regression - Journal draft

Published in arXiv Preprints, 2019

Data driven graph constructions are often used in various applications, including several machine learning tasks, where the goal is to make predictions and discover patterns. However, learning an optimal graph from data is still a challenging task. Weighted K-nearest neighbor and ϵ-neighborhood methods are among the most common graph construction methods, due to their computational simplicity but the choice of parameters such as K and ϵ associated with these methods is often ad hoc and lacks a clear interpretation. Read more

## Optimizing training sets used for setting up inspection-related algorithms

Published in US Patent Office, 2019

Methods and systems for training an inspection-related algorithm are provided. One system includes one or more computer subsystems configured for performing an initial training of an inspection-related algorithm with a labeled set of defects thereby generating an initial version of the inspection-related algorithm and applying the initial version of the inspection-related algorithm to an unlabeled set of defects. Read more

## Detection and removal of Salt and Pepper noise in images by improved median filter

Published in IEEE Recent Advances in Intelligent Computational Systems, 2011

A methodology based on median filters for the removal of Salt and Pepper noise by its detection followed by filtering in both binary and gray level images has been proposed in this paper. Read more