We have developed a model for online continual or lifelong reinforcement learning (RL) inspired on the insect brain. Our model leverages the offline training of a feature extraction and a common general policy layer to enable the convergence of RL algorithms in online settings. Sharing a common policy layer across tasks leads to positive backward transfer, where the agent continuously improved in older tasks sharing the same underlying general policy. Biologically inspired restrictions to the agent's network are key for the convergence of RL algorithms. This provides a pathway towards efficient online RL in resource-constrained scenarios.

Surrogate models have shown to be an extremely efficient aid in solving engineering problems that require repeated evaluations of an expensive computational model. They are built by sparsely evaluating the costly original model and have provided a way to solve otherwise intractable problems. A crucial aspect in surrogate modelling is the assumption of smoothness and regularity of the model to approximate. This assumption is however not always met in reality. For instance in civil or mechanical engineering, some models may present discontinuities or non-smoothness, e.g., in case of instability patterns such as buckling or snap-through. Building a single surrogate model capable of accounting for these fundamentally different behaviors or discontinuities is not an easy task. In this paper, we propose a three-stage approach for the approximation of non-smooth functions which combines clustering, classification and regression. The idea is to split the space following the localized behaviors or regimes of the system and build local surrogates that are eventually assembled. A sequence of well-known machine learning techniques are used: Dirichlet process mixtures models (DPMM), support vector machines and Gaussian process modelling. The approach is tested and validated on two analytical functions and a finite element model of a tensile membrane structure.

In this article, we plan to provide an introduction about some basics about robots for readers. Several key topics of classic robotics will be introduced, including robot representation, robot rotational motion, coordinates transformation and velocity transformation. By now, classic rigid-body robot analysis is still the main-stream approach in robot controlling and motion planning. In this article, no data-driven or machine learning based methods will be introduced. Most of the materials covered in this article are based on the rigid-body kinematics that the readers probably have learned from the physics course at high-school or college. Meanwhile, these classic robot kinematics analyses will serve as the foundation for the latest intelligent robot control algorithms in modern robotics studies.

Abstract--Partial label learning (PLL) is an important problem that allows each training example to be labeled with a coarse candidate set, which well suits many real-world data annotation scenarios with label ambiguity. Despite the promise, the performance of PLL often lags behind the supervised counterpart. Specifically, our proposed framework PiCO consists of a contrastive learning module along with a novel class prototype-based label disambiguation algorithm. PiCO produces closely aligned representations for examples from the same classes and facilitates label disambiguation. Theoretically, we show that these two components are mutually beneficial, and can be rigorously justified from an expectation-maximization (EM) algorithm perspective. Moreover, we study a challenging yet practical noisy partial label learning setup, where the ground-truth may not be included in the candidate set. To remedy this problem, we present an extension PiCO+ that performs distance-based clean sample selection and learns robust classifiers by a semi-supervised contrastive learning algorithm. Extensive experiments demonstrate that our proposed methods significantly outperform the current state-of-the-art approaches in standard and noisy PLL tasks and even achieve comparable results to fully supervised learning.

Welcome to the first advanced and project-based Pandas Data Science Course! No matter if you need excellent Pandas skills for Data Analysis, Machine Learning or Finance purposes, this is the right Course for you to get your skills to Expert Level! Efficiently import and merge Data from many text/CSV files. Clean, handle and flatten nested and stringified Data in DataFrames. Know how to handle and normalize Unicode strings.

Whether you're just beginning to work with Artificial Intelligence (AI) or you already have AI experience and are new to Microsoft Azure, this course provides you with everything you need to get started. Artificial Intelligence (AI) empowers amazing new solutions and experiences; and Microsoft Azure provides easy to use services to help you build solutions that seemed like science fiction a short time ago; enabling incredible advances in health care, financial management, environmental protection, and other areas to make a better world for everyone. In this course, you will learn the key AI concepts of machine learning, anomaly detection, computer vision, natural language processing, and conversational AI. You'll see some of the ways that AI can be used and explore the principles of responsible AI that can help you understand some of the challenges facing developers as they try to create ethical AI solutions. This course will help you prepare for Exam AI-900: Microsoft Azure AI Fundamentals.

AI Explorations and Their Practical Use in School Environments is an ISTE initiative funded by General Motors. The program provides professional learning opportunities for educators, with the goal of preparing all students for careers with AI. Recently, we spoke with three more participants of the AI Explorations program to learn about its ongoing impact in K-12 classrooms. Here, they share how the program is helping their districts implement AI curriculum with an eye toward equity in the classroom. Monica Rodriguez is a kindergarten teacher with Ector County Independent School District in Odessa, Texas.

I Can't Believe It's Not Better: Understanding Deep Learning Through Empirical Falsification Workshop Organizers include: Javier Antorán Panelists include: Kevin Murphy

Just as teachers help students gain new skills, the same is true of artificial intelligence (AI). Machine learning algorithms can adapt and change, much like the learning process itself. Using the machine teaching paradigm, a subject matter expert (SME) can teach AI to improve and optimize a variety of systems and processes. The result is an autonomous AI system. In this course, you'll learn how automated systems make decisions and how to approach building an AI system that will outperform current capabilities.

The recent success and proliferation of machine learning and deep learning have provided powerful tools, which are also utilized for encrypted traffic analysis, classification, and threat detection in computer networks. These methods, neural networks in particular, are often complex and require a huge corpus of training data. Therefore, this paper focuses on collecting a large up-to-date dataset with almost 200 fine-grained service labels and 140 million network flows extended with packet-level metadata. The number of flows is three orders of magnitude higher than in other existing public labeled datasets of encrypted traffic. The number of service labels, which is important to make the problem hard and realistic, is four times higher than in the public dataset with the most class labels. The published dataset is intended as a benchmark for identifying services in encrypted traffic. Service identification can be further extended with the task of "rejecting" unknown services, i.e., the traffic not seen during the training phase. Neural networks offer superior performance for tackling this more challenging problem. To showcase the dataset's usefulness, we implemented a neural network with a multi-modal architecture, which is the state-of-the-art approach, and achieved 97.04% classification accuracy and detected 91.94% of unknown services with 5% false positive rate.