In principle, meta-reinforcement learning algorithms leverage experience across many tasks to learn fast and effective reinforcement learning (RL) strategies. However, current meta-RL approaches rely on manually-defined distributions of training tasks, and hand-crafting these task distributions can be challenging and time-consuming. Can useful'' pre-training tasks be discovered in an unsupervised manner? We develop an unsupervised algorithm for inducing an adaptive meta-training task distribution, i.e. an automatic curriculum, by modeling unsupervised interaction in a visual environment. The task distribution is scaffolded by a parametric density model of the meta-learner's trajectory distribution.

Wide neural networks with random weights and biases are Gaussian processes, as observed by Neal (1995) for shallow networks, and more recently by Lee et al. (2018) and Matthews et al. (2018) for deep fully-connected networks, as well as by Novak et al. (2019) and Garriga-Alonso et al. (2019) for deep convolutional networks. We show that this Neural Network-Gaussian Process correspondence surprisingly extends to all modern feedforward or recurrent neural networks composed of multilayer perceptron, RNNs (e.g. LSTMs, GRUs), (nD or graph) convolution, pooling, skip connection, attention, batch normalization, and/or layer normalization. More generally, we introduce a language for expressing neural network computations, and our result encompasses all such expressible neural networks. This work serves as a tutorial on the \emph{tensor programs} technique formulated in Yang (2019) and elucidates the Gaussian Process results obtained there.

Energy based models (EBMs) are appealing due to their generality and simplicity in likelihood modeling, but have been traditionally difficult to train. We present techniques to scale MCMC based EBM training on continuous neural networks, and we show its success on the high-dimensional data domains of ImageNet32x32, ImageNet128x128, CIFAR-10, and robotic hand trajectories, achieving better samples than other likelihood models and nearing the performance of contemporary GAN approaches, while covering all modes of the data. We highlight some unique capabilities of implicit generation such as compositionality and corrupt image reconstruction and inpainting. Finally, we show that EBMs are useful models across a wide variety of tasks, achieving state-of-the-art out-of-distribution classification, adversarially robust classification, state-of-the-art continual online class learning, and coherent long term predicted trajectory rollouts. Papers published at the Neural Information Processing Systems Conference.

Strategies, standards, and supporting resources to help you make the Web more accessible to people with disabilities. After recently deciding to learn Web Development as well as Data Science, this section of the introductory course really grabbed my attention.

On Wednesday, March 25, we will host a webinar titled, "Latin America: The Next Big AI Talent Pool" from 12 p.m. to 12:30 p.m. According to the 2020 Global Talent Competitiveness Index (GTCI) published by INSEAD, a lack of specialized talent is the main challenge to AI development. Professor Felipe Monteiro from INSEAD will discuss AI development across the globe. Marco Stefanini, CEO and founder of Stefanini and Fabio Caversan, Artificial Intelligence Research and Development Director at Stefanini, will cover AI talent in Latin America. Attendees will learn how they can take advantage of this ever-growing talent pool to fill any gaps in AI development.

Game theory is one of the most fascinating areas of mathematics that have influenced diverse fields such as economics, social sciences, biology and, obviously, computer science. Games are playing a key role in the evolution of artificial intelligence(AI). For starters, game environments are becoming a popular training mechanism in areas such as reinforcement learning or imitation learning. In theory, any multi-agent AI system can be subjected to gamified interactions between its participants. The branch of mathematics that formulates the principles of games is known as game theory.

Machine Learning is transforming many industries while enabling new types of products and services nobody even dreamed of until recently. However, the skill set required to develop real-life Machine Learning applications have mostly remained the playground of the few privileged academics and scientists. The world and the global workforce cannot afford to stay behind the curve on this key technology enabler, so we urgently need to produce a much larger group of ML-literate professionals such as developers, analysts, managers, and subject matter experts. To meaningfully contribute on this matter, BigML is bringing the second edition of our Machine Learning School to Seville. We will hold a two-day crash course ideal for business leaders, industry practitioners, advanced undergraduates, as well as graduate students, seeking a quick, practical, and hands-on introduction to Machine Learning to solve real-world problems.

Applied Machine Learning without coding using Orange 3 Link: new udemy course machine learning without any coding This is a course about data science and machine learning without using any programming! Orange 3 is used as the platform for the course. It is public domain and very powerful tool with a lot of flexibility.NEW by Yannis Faitakis What you'll learn Learn the basics of machine learning without any coding! Will use Orange 3 to learn about a variety of problems and ways you can solve them using the tools provided in the software. By the end of the course you will have a solid understanding of the most used machine learning algorithms for regression, forecasting and classification and how to prototype solutions in Orange 3 Description This is a course about data science and machine learning without using any programming!

In this post, you will discover the top 10 books available right now on deep learning. There are quite a few available online in which you may purchase. The book Deep Learning with Python written by Keras creator and Google AI researcher François Chollet introduces the field of Deep Learning using python with the powerful and Keras library. It was written in order to build the knowledge and minds of individuals using intuitive explanations and practical examples. The purchase of the print book includes a free eBook in PDF, Kindle, and ePub formats from Manning Publications.

This article deals with the IT security of connectionist artificial intelligence (AI) applications, focusing on threats to integrity, one of the three IT security goals. Such threats are for instance most relevant in prominent AI computer vision applications. In order to present a holistic view on the IT security goal integrity, many additional aspects such as interpretability, robustness and documentation are taken into account. A comprehensive list of threats and possible mitigations is presented by reviewing the state-of-the-art literature. AI-specific vulnerabilities such as adversarial attacks and poisoning attacks as well as their AI-specific root causes are discussed in detail. Additionally and in contrast to former reviews, the whole AI supply chain is analysed with respect to vulnerabilities, including the planning, data acquisition, training, evaluation and operation phases. The discussion of mitigations is likewise not restricted to the level of the AI system itself but rather advocates viewing AI systems in the context of their supply chains and their embeddings in larger IT infrastructures and hardware devices. Based on this and the observation that adaptive attackers may circumvent any single published AI-specific defence to date, the article concludes that single protective measures are not sufficient but rather multiple measures on different levels have to be combined to achieve a minimum level of IT security for AI applications.