Kenneth Ezukwoke

Mines Saint-Etienne | ENSM-SE · Génie industriel et mathématiques appliquées

Pre-trained large language models (LLMs) have gained significant attention in the field of natural language processing (NLP), especially for the task of text summarization, generation, and question answering. The success of LMs can be attributed to the attention mechanism introduced in Transformer models, which have outperformed traditional recurre...

Fault analysis (FA) is the process of collecting and analyzing data to determine the cause of a failure. It plays an important role in ensuring the quality in manufacturing process. Traditional FA techniques are time-consuming and labor-intensive, relying heavily on human expertise and the availability of failure inspection equipment. In semiconduc...

Failure analysis has grown in importance as a means of ensuring high quality in the production of electronic components. The findings of a failure analysis can be used to pinpoint weaknesses in a component and get a deeper understanding of the mechanisms and causes of failure, enabling the adoption of corrective actions to raise the quality and rel...

Microelectronics production failure analysis is an important step in improving product quality and development. In fact, the understanding of the failure mechanisms and therefore the implementation of corrective actions on the cause of the failure depend on the results of this analysis. These analyses are saved under textual features format. Then s...

Failure analysis has become an important part of guaranteeing good quality in the electronic component manufacturing process. The conclusions of a failure analysis can be used to identify a component’s flaws and to better understand the mechanisms and causes of failure, allowing for the implementation of remedial steps to improve the product’s qual...

Microelectronics production failure analysis is an important step in improving product quality and development. Indeed, the understanding of the failure mechanisms and therefore the implementation of corrective actions on the cause of the failure depend on the results of these analysis. These analysis are saved under textual features format. Then s...

Pre-trained Language Models recently gained traction in the Natural Language Processing (NLP) domain for text summarization, generation and question-answering tasks. This stems from the innovation introduced in Transformer models and their overwhelming performance compared with Recurrent Neural Network Models (Long Short Term Memory (LSTM)). In thi...

In the semiconductor industry, Failure Analysis (FA) is an investigation to determine the root causes of a failure. It also involves an intermediate analysis to build the steps of the failure analysis in order to mitigate future failures and to facilitate the future FA. In the framework of the FA 4.0 project, the reporting system records three item...

Variational autoencoders (VAEs) have recently been used for unsupervised disentanglement learning of complex density distributions. Numerous variants exist to encourage disentanglement in latent space while improving reconstruction. However, none have simultaneously managed the trade-off between attaining extremely low reconstruction error and a hi...

Microelectronics production failure analysis is a time-consuming and complicated task involving successive steps of analysis of complex process chains. The analysis is triggered to find the root cause of a failure and its findings, recorded in a reporting system using natural language. Fault analysis, physical analysis, sample preparation and packa...

Passive aggressive online learning is an extension of Support Vector Machine (SVM) to the context of online learning for binary classification. In this paper we consider the application of the algorithm on anomaly labeling for IJCNN 2001 Neural Network Competition dataset from LibSVM dataset repository 1 from Ford Research Laboratory. We also work...

We analyse two algorithms for approximating the optimal transport distance for domain adaption problems. We compare precisely the result of subspace alignment algorithm and Sinkhorm's algorithm (from python optimal transport (POT) library). We experiment using the Office/Caltech dataset and perform One nearest neighbour classification (1-NN). Final...

Support Vector Data Description (SVDD) is a variant of Support Vector Machines (SVM) used for one class classification. It is particularly designed for outlier detection and hence the focus of our paper. In this paper we solve the SVDD optimization problem using gradient descent (primal problem) and minibatch gradient ascent(dual problem). We compa...

Kmeans is a simple yet efficient unsupervised clustering algorithm. In this paper we study classical and different kernel Kmeans. We take an experimental analysis on datasets including (moon, circle , classification and iris dataset). We briefly introduce multiple kernel learning and its applications in kernel kmeans. We conclude by expanding on th...

Logistic regression is a linear binary classification algorithm frequently used for classification problems. In this paper we present its kernel version which is used for classification of non-linearly separable problems. We briefly introduce the concept of multiple kernel learning and apply it to kernel logistic regression. We elaborate the perfor...

Principal Component Analysis (PCA) is a statistical technique for linear dimensionality reduction. Its Kernel version kernel-PCA is a prominent non-linear extension of the classical dimensionality reduction technique. In this paper, we present an experimental comparison between the classical PCA and the kernel-PCA. We briefly introduce and compare...

A breakdown of the statistical and algorithmic difference between logistic regression and perceptron. The purpose of this abstract is to derive the learning algorithm behind this widely used machine/deep learning algorithm with their scratch python implementation.