We minimize over convex functions the maximum (over Wasserstein perturbations of the empirical measure) of the absolute regression errors.

Reading relevant scientific papers and analyzing research development trends is a critical step in generating new scientific ideas. However, the rapid increase in the volume of research literature and the complex citation relationships make it difficult for researchers to quickly analyze and derive meaningful research trends. The development of large language models (LLMs) has provided a novel approach for automatically summarizing papers and generating innovative research ideas. However, existing paper-based idea generation methods either simply input papers into LLMs via prompts or form logical chains of creative development based on citation relationships, without fully exploiting the semantic information embedded in these citations. Inspired by knowledge graphs and human cognitive processes, we propose a framework called the Graph of AI Ideas (GoAI) for the AI research field, which is dominated by open-access papers. This framework organizes relevant literature into entities within a knowledge graph and summarizes the semantic information contained in citations into relations within the graph. This organization effectively reflects the relationships between two academic papers and the advancement of the AI research field. Such organization aids LLMs in capturing the current progress of research, thereby enhancing their creativity. Experimental results demonstrate the effectiveness of our approach in generating novel, clear, and effective research ideas.

The Machine Learning Engineering for Production (MLOps) Specialization covers how to conceptualize, build, and maintain integrated systems that continuously operate in production. In striking contrast with standard machine learning modeling, production systems need to handle relentless evolving data. Moreover, the production system must run non-stop at the minimum cost while producing the maximum performance. In this Specialization, you will learn how to use well-established tools and methodologies for doing all of this effectively and efficiently. In this Specialization, you will become familiar with the capabilities, challenges, and consequences of machine learning engineering in production. By the end, you will be ready to employ your new production-ready skills to participate in the development of leading-edge AI technology to solve real-world problems.

Introduction to Research - This session will help you to start the wonderful journey of research. Finding a research problem - Finding a research problem is the most important aspect of any research project . Introduction to Machine Learning:- What is Machine Learning?, - in this session we will get an overview of machine learning

This is the official application form for the Ghana Data Science Summit 2023 (IndabaX Ghana). Date of Conference: Saturday, 13th May, 2023 Venue: Methodist University College, Accra Please read the following general instructions/comments before completing the application: (1) Please respond to as many questions as you can in a truthful manner. (2) All applications will be reviewed by the organizing team and decisions made based on interest and academic and professional background. If you are new to this field, you are still welcome to apply. (3) Admission to the conference is free but you must be accepted by the organizing team to attend. (4) Please submit only ONE application. Multiple applications will be disqualified. (5) This year's conference will take place in one day and will comprise a hands-on tutorials session, a hackathon and poster presentations. You will be asked to indicate your interest in this form. Kindly note that you can either choose the hands-on tutorial session OR the hackathon and NOT BOTH. Also, regardless of the option you choose, you can submit a proposal for a poster presentation. Hands-On Tutorials (Recommended for Beginners) The hands-on tutorial will cover basics in Python programming useful for Machine Learning. We would cover topics like Python Lists, Introduction to Numpy, and Introduction to Scikit-Learn. Additionally, we would go over a practical project using Python. This project will guide you in a step-by-step process of building a Machine Learning Project with Python. We encourage all participants who would be selected to participate to bring along their laptops. No prior knowledge of Python programming or Machine Learning is required. Hackathon (Recommended for Intermediates and Experts) The Hackathon will be based on a practical Machine Learning project where you would have access to a starter notebook. You will be required to work in a team to come up with a better solution that can get the best score on the leaderboard. All selected participants are highly encouraged to come along with their laptops to participate in the competition. We would also not be providing any GPUs as you will not necessarily need this in the Hackathon. Advanced or intermediate knowledge in Python programming and machine learning is highly required. Prizes will be awarded to the best 3 teams on the leaderboard. (6) Kindly email us via info@indabaxghana.com if you have any questions. www.indabaxghana.com

Six articles you don't want to miss

Any Metadata related to ML artifact creation is tracked here. We also track performance of the ML Model. Experiments become reproducible and comparable between each other. Model Registry could and in some cases should be treated as part of ML Metadata Store.

Springer has released hundreds of free books on a wide range of topics to the general public. The list, which includes 408 books in total, covers a wide range of scientific and technological topics. In order to save you some time, I have created one list of all the books (65 in number) that are relevant to the data and Machine Learning field. Among the books, you will find those dealing with the mathematical side of the domain (Algebra, Statistics, and more), along with more advanced books on Deep Learning and other advanced topics. You also could find some good books in various programming languages such as Python, R, MATLAB, etc.

A multi-armed bandit (MAB) is a machine learning framework that uses complex algorithms to dynamically allocate resources when presented with multiple choices. In other words, it's an advanced form of A/B testing that's most commonly used by data analysts, medicine researchers, and marketing specialists. Before we delve deeper into the concept of multi-armed bandits, we need to discuss reinforcement learning, as well as the exploration vs. exploitation dilemma. Then, we can focus on various bandit solutions and practical applications. Alongside supervised and unsupervised learning, reinforcement learning is one of the basic three paradigms of machine learning. Unlike the first two archetypes we mentioned, reinforcement learning focuses on rewards and punishments for the agent whenever it interacts with the environment.

GNNs are a type of neural network that can process data with complex, non-Euclidean structure, such as graphs and networks. They have been widely used in AI and ML for tasks such as node classification, graph classification, and link prediction. One key area of focus has been on developing more efficient GNN architectures for large-scale graphs. This has included the development of hierarchical and modular GNNs, as well as the use of sparsification and approximation techniques to reduce the computational complexity of training and inference. Another area of focus has been on improving the ability of GNNs to capture long-range dependencies and higher-order connectivity patterns in graphs.