Capturing aleatoric uncertainty is a critical part of many machine learning systems. In deep learning, a common approach to this end is to train a neural network to estimate the parameters of a heteroscedastic Gaussian distribution by maximizing the logarithm of the likelihood function under the observed data. In this work, we examine this approach and identify potential hazards associated with the use of log-likelihood in conjunction with gradient-based optimizers. First, we present a synthetic example illustrating how this approach can lead to very poor but stable parameter estimates. Second, we identify the culprit to be the log-likelihood loss, along with certain conditions that exacerbate the issue. Third, we present an alternative formulation, termed β NLL, in which each data point's contribution to the loss is weighted by the β-exponentiated variance estimate. We show that using an appropriate β largely mitigates the issue in our illustrative example. Fourth, we evaluate this approach on a range of domains and tasks and show that it achieves considerable improvements and performs more robustly concerning hyperparameters, both in predictive RMSE and log-likelihood criteria. Endowing models with the ability to capture uncertainty is of crucial importance in machine learning. Uncertainty can be categorized into two main types: epistemic uncertainty and aleatoric uncertainty (Kiureghian & Ditlevsen, 2009). Epistemic uncertainty accounts for subjective uncertainty in the model, one that is reducible given sufficient data. By contrast, aleatoric uncertainty captures the stochasticity inherent in the observations and can itself be subdivided into homoscedastic and heteroscedastic uncertainty. Homoscedastic uncertainty corresponds to noise that is constant across the input space, whereas heteroscedastic uncertainty corresponds to noise that varies with the input. There are well-established benefits for modeling each type of uncertainty.

For Misha Awad, the process of boarding a plane to Japan and then going through immigration brought with it a range of emotions -- shock, nervousness, bureaucracy-induced tedium and even amusement. While moving to a new country is a major step in anyone's life, Awad's case had an extra layer to it, as it was also the culmination of a long wait to return to Japan. A student at the Inter-University Center for Japanese Language Studies (IUC) in Yokohama, she and many fellow members of her program were finally able to enter the country in mid-March, having previously had their hopes dashed at the end of November, when their permission to enter Japan was canceled just days before they were supposed to fly due to tightened restrictions following the discovery of the omicron variant . "Knowing for sure I had made it to the gates, I don't need any of these papers (for immigration) anymore, I'm actually here -- that was kind of just mind-blowing," she said. Following the easing of Japan's strict border controls on March 1, which have been in place for most of the pandemic, at least some foreign students have been able to arrive in time for Friday's start of the academic year.

So, back in 2021, we were in the development phase of our final year project. I will start with a little bit of background on the project. It was a classic sentiment analysis study on political parties in the country. There were 3 group members in total, and neither of us had foreseen the kind of hardware difficulties we might face at this stage of development. The thing is, we had no money to invest in those high-end machines to do extensive machine learning tasks.

ANJA KASPERSEN: Today I am very pleased to be joined by Pascale Fung. Pascale is a;rofessor in the Department of Electronic and Computer Engineering and Department of Computer Science and Engineering at The Hong Kong University of Science and Technology. She is known globally for her pioneering work on conversational artificial intelligence (AI), computational linguistics, and was one of the earliest proponents of statistical and machine-learning approaches for natural language processing (NLP). She is now leading groundbreaking research on how to build intelligent systems that can understand and empathize with humans. I have really been looking forward to this conversation with you. Your professional accolades are many, most of which we will touch on during our conversation. However, for our listeners to get to know you a bit better, I would like us to go back to your upbringing during what I understand to be a very tenuous political period in China. I was born, spent my childhood, ...

For language learners who want to grow their vocabulary and develop confidence, browser extensions are an opportunity to casually practice while online. But before you download anything, here's how to audit a browser extension to ensure it's safe. Whether you're streaming Netflix series during your spare time or gobbling up the latest tech news, at least one of the following picks is likely worth incorporating into your language learning process. Test the options that spark your interest and simply uninstall anything that you don't enjoy. Although you will not receive speaking practice, these browser extensions can help hone your reading and listening skills.

The thriving industry of Data Science is continuously evolving with the technological advancements in Machine Learning and Artificial intelligence. This has opened up whole new avenues for Data Scientists worldwide. Professionals who can handle Big Data and have the necessary knowledge required for understanding, analysing and processing data are in high demand in the job market. However, there is one important thing that also needs to be addressed is the raging problem caused by the gender gap in this sector. As per the statistical report from the Boston Consulting Group, only 15 to 22 per cent of the Data Science-related professional roles are occupied by women.

Machine Learning is one of the hottest fields of the 21st century, and it will be one of the main drivers of the next technological revolution. Let us give you the best information to learn about it. The best books to learn Machine Learning, Python Programming, Data Analysis and Artificial Intelligence. Tutorials on all topics related to ML:Deep Learning, Computer Vision, NLP, Explainable AI and more! Different Machine learning Dataset repositories to get started with your own projects! It is an incredible time to learn about this technology, however, it is not always easy to do so: there are so many resources out there that it is not easy to distinguish the good from the time wasters.

There is widespread agreement that responsible artificial intelligence requires principles such as fairness, transparency, privacy, human safety, and explainability. Nearly all ethicists and tech policy advocates stress these factors and push for algorithms that are fair, transparent, safe, and understandable.1 But it is not always clear how to operationalize these broad principles or how to handle situations where there are conflicts between competing goals.2 It is not easy to move from the abstract to the concrete in developing algorithms and sometimes a focus on one goal comes at the detriment of alternative objectives.3 In the criminal justice area, for example, Richard Berk and colleagues argue that there are many kinds of fairness and it is "impossible to maximize accuracy and fairness at the same time, and impossible simultaneously to satisfy all kinds of fairness."4

JavaServer Pages (JSP) is a technology for developing Webpages that supports dynamic content. This helps developers insert java code in HTML pages by making use of special JSP tags, most of which start with % and end with % . A JavaServer Pages component is a type of Java servlet that is designed to fulfill the role of a user interface for a Java web application. Web developers write JSPs as text files that combine HTML or XHTML code, XML elements, and embedded JSP actions and commands. Using JSP, you can collect input from users through Webpage forms, present records from a database or another source, and create Webpages dynamically.

The improvements in the edge computing technology pave the road for diversified applications that demand real-time interaction. However, due to the mobility of the end-users and the dynamic edge environment, it becomes challenging to handle the task offloading with high performance. Moreover, since each application in mobile devices has different characteristics, a task orchestrator must be adaptive and have the ability to learn the dynamics of the environment. For this purpose, we develop a deep reinforcement learning based task orchestrator, DeepEdge, which learns to meet different task requirements without needing human interaction even under the heavily-loaded stochastic network conditions in terms of mobile users and applications. Given the dynamic offloading requests and time-varying communication conditions, we successfully model the problem as a Markov process and then apply the Double Deep Q-Network (DDQN) algorithm to implement DeepEdge. To evaluate the robustness of DeepEdge, we experiment with four different applications including image rendering, infotainment, pervasive health, and augmented reality in the network under various loads. Furthermore, we compare the performance of our agent with the four different task offloading approaches in the literature. Our results show that DeepEdge outperforms its competitors in terms of the percentage of satisfactorily completed tasks.