This paper provides a unifying view of optimal kernel hypothesis testing across the MMD two-sample, HSIC independence, and KSD goodness-of-fit frameworks. Minimax optimal separation rates in the kernel and $L^2$ metrics are presented, with two adaptive kernel selection methods (kernel pooling and aggregation), and under various testing constraints: computational efficiency, differential privacy, and robustness to data corruption. Intuition behind the derivation of the power results is provided in a unified way accross the three frameworks, and open problems are highlighted.

This article provides a practical introduction to kernel discrepancies, focusing on the Maximum Mean Discrepancy (MMD), the Hilbert-Schmidt Independence Criterion (HSIC), and the Kernel Stein Discrepancy (KSD). Various estimators for these discrepancies are presented, including the commonly-used V-statistics and U-statistics, as well as several forms of the more computationallyefficient incomplete U-statistics. The importance of the choice of kernel bandwidth is stressed, showing how it affects the behaviour of the discrepancy estimation. Adaptive estimators are introduced, which combine multiple estimators with various kernels, addressing the problem of kernel selection. This paper corresponds to the introduction of my PhD thesis (Schrab, 2025a, Chapter 2) and is presented as a standalone article to introduce the reader to kernel discrepancies estimators. First, in Section 1, we define kernels, Reproducing Kernel Hilbert Spaces, mean embeddings and cross-covariance operators, and present kernel properties such as characteristicity, universality and translation invariance. Then, in Section 2, we introduce the Maximum Mean Discprecancy, the Hilbert-Schmidt Independence Criterion, and the Kernel Stein Discrepancy, as well as their estimators, and we discuss the importance of the choice of kernel for such measures. We then introduce a collection of statistics in Section 3, including the commonly-used complete statistics, as well as their incomplete counterparts which trade accuracy for computational efficiency. Finally, in Section 4, we construct adaptive estimators combining multiple statistics with various kernels, which is one method to address the problem of kernel selection.

Sequential feature selection is a supervised approach to feature selection. It makes use of a supervised model and it can be used to remove useless features from a large dataset or to select useful features by adding them sequentially. This is a forward approach because we start with 1 feature and then we add other features. There's a backward approach as well, that starts from all the features and removes the less relevant ones according to the same maximization criteria. Since, at each step, we check the performance of the model with the same dataset with the addition of each remaining feature (one by one), it's a greedy approach. The algorithm stops when the desired number of features is reached or if the performance doesn't increase above a certain threshold.

Model extraction is a major threat for embedded deep neural network models that leverages an extended attack surface. Indeed, by physically accessing a device, an adversary may exploit side-channel leakages to extract critical information of a model (i.e., its architecture or internal parameters). Different adversarial objectives are possible including a fidelity-based scenario where the architecture and parameters are precisely extracted (model cloning). We focus this work on software implementation of deep neural networks embedded in a high-end 32-bit microcontroller (Cortex-M7) and expose several challenges related to fidelity-based parameters extraction through side-channel analysis, from the basic multiplication operation to the feed-forward connection through the layers. To precisely extract the value of parameters represented in the single-precision floating point IEEE-754 standard, we propose an iterative process that is evaluated with both simulations and traces from a Cortex-M7 target. To our knowledge, this work is the first to target such an high-end 32-bit platform. Importantly, we raise and discuss the remaining challenges for the complete extraction of a deep neural network model, more particularly the critical case of biases.

Author: Murat Karakaya Date created….. 17 09 2021 Date published… 11 03 2022 Last modified…. We will cover all the topics related to solving Multi-Class Text Classification problems with sample implementations in Python / TensorFlow / Keras environment. We will use a Kaggle Dataset in which there are 32 topics and more than 400K total reviews. You can access all the codes, videos, and posts of this tutorial series from the links below. In this tutorial series, there are several parts to cover the Text Classification with various Deep Learning Models topics.

Hierarchical clustering is part of the group of unsupervised learning models known as clustering. This means that we don't have a defined target variable unlike in traditional regression or classification tasks. The point of this machine learning algorithm, therefore, is to identify distinct clusters of objects that share similar characteristics by using defined distance metrics on the selected variables. Other machine learning algorithms that fit within this family include Kmeans or DBscan. This specific algorithm comes in two main flavours or forms: top-down or bottom-up.

Udemy Coupon - Practical Introduction to Machine Learning with Python, Quickly Learn the Essentials of Artificial Intelligence (AI) and Machine Learning (ML) Created by Madhu Siddalingaiah English [Auto] Students also bought Spring & Hibernate for Beginners (includes Spring Boot) Data Structures and Algorithms: Deep Dive Using Java SQL Beginner to Guru: MySQL Edition - Master SQL with MySQL Full Stack: Angular and Spring Boot Mastering your own communication: The fundamentals Next Level Conversation: Improve Your Communication Skills Preview this Course GET COUPON CODE Description LinkedIn released it's annual "Emerging Jobs" list, which ranks the fastest growing job categories. The top role is Artificial Intelligence Specialist, which is any role related to machine learning. Hiring for this role has grown 74% in the past few years! Machine learning is the technology behind self driving cars, smart speakers, recommendations, and sophisticated predictions. Machine learning is an exciting and rapidly growing field full of opportunities.

Are you interested in learning about Artificial Intelligence and Machine Learning? If so, this FREE webinar is for you. Dr Temitope Sam-Odusina (Computer Vision and Artificial Intelligence Engineer) and Dr Abbas Egbeyemi (Software Engineer) will give an overview of Artificial Intelligence and Machine Learning via real-life examples and applications and a live demonstration on how to develop a facial recognition application. The webinar will take place on Saturday 5th September 2020 at 6:00 PM (West Africa Time) via Zoom (Zoom link will be provided after registration) and will be hosted by Dr Adeayo Sotayo. Any programming experience will be beneficial.

In this article, we will focus on adding and customizing Early Stopping in our machine learning model and look at an example of how we do this in practice with Keras and TensorFlow 2.0. In machine learning, early stopping is one of the most widely used regularization techniques to combat the overfitting issue. Early Stopping monitors the performance of the model for every epoch on a held-out validation set during the training, and terminate the training conditional on the validation performance. Early Stopping is a very different way to regularize the machine learning model. The way it does is to stop training as soon as the validation error reaches a minimum.

Udemy Coupon - Practical Introduction to Machine Learning with Python, Quickly Learn the Essentials of Artificial Intelligence (AI) and Machine Learning (ML) Created by Madhu Siddalingaiah English [Auto] Students also bought Spring & Hibernate for Beginners (includes Spring Boot) Data Structures and Algorithms: Deep Dive Using Java SQL Beginner to Guru: MySQL Edition - Master SQL with MySQL Full Stack: Angular and Spring Boot Mastering your own communication: The fundamentals Next Level Conversation: Improve Your Communication Skills Preview this Course GET COUPON CODE Description LinkedIn released it's annual "Emerging Jobs" list, which ranks the fastest growing job categories. The top role is Artificial Intelligence Specialist, which is any role related to machine learning. Hiring for this role has grown 74% in the past few years! Machine learning is the technology behind self driving cars, smart speakers, recommendations, and sophisticated predictions. Machine learning is an exciting and rapidly growing field full of opportunities.