Reinforcement learning algorithms, though successful, tend to over-fit to training environments hampering their application to the real-world. This paper proposes WR$^{2}$L; a robust reinforcement learning algorithm with significant robust performance on low and high-dimensional control tasks. Our method formalises robust reinforcement learning as a novel min-max game with a Wasserstein constraint for a correct and convergent solver. Apart from the formulation, we also propose an efficient and scalable solver following a novel zero-order optimisation method that we believe can be useful to numerical optimisation in general. We contribute both theoretically and empirically. On the theory side, we prove that WR$^{2}$L converges to a stationary point in the general setting of continuous state and action spaces. Empirically, we demonstrate significant gains compared to standard and robust state-of-the-art algorithms on high-dimensional MuJuCo environments.

From using a simple web cam to identify objects to training a network in the cloud, these resources will help you take advantage of all MATLAB has to offer for deep learning.

As companies ramp up hiring to develop AI, workers agree they need retraining for today's in-demand skills. At the same time, global workers -- particularly Americans -- say going back to school is no longer a feasible option, according to a survey by Northeastern University and Gallup. Just 1 in 4 Americans are confident that the higher education system is doing enough to address the need for career-long learning and retraining. Tuition costs are the biggest deterrent, followed by academic programs that aren't keeping up with an evolving workplace environment, according to the survey. Adults have even less confidence in the government's ability to prepare the public for the latest technological revolution.

Hundreds of schoolchildren have been drafted in to make Amazon's Alexa devices in China as part of a controversial and often illegal attempt to meet production targets, documents seen by the Guardian reveal. Interviews with workers and leaked documents from Amazon's supplier Foxconn show that many of the children have been required to work nights and overtime to produce the smart-speaker devices, in breach of Chinese labour laws. According to the documents, the teenagers – drafted in from schools and technical colleges in and around the central southern city of Hengyang – are classified as "interns", and their teachers are paid by the factory to accompany them. Teachers are asked to encourage uncooperative pupils to accept overtime work on top of regular shifts. Some of the pupils making Amazon's Alexa-enabled Echo and Echo Dot devices along with Kindles have been required to work for more than two months to supplement staffing levels at the factory during peak production periods, researchers found.

In MEAP, you read a book chapter-by-chapter while it's being written and get the final book as soon as it's finished. Save big on Manning books and liveVideo courses with our exclusive Tech in a Box bundles! Each bundle is carefully curated to enhance your skills in a key subject area. Deep learning is exploding, driving everything from autonomous vehicles to real-time computer vision and speech recognition. New languages and new approaches to programming are always emerging.

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Variational inference is becoming more and more popular for approximating intractable posterior distributions in Bayesian statistics and machine learning. Meanwhile, a few recent works have provided theoretical justification and new insights on deep neural networks for estimating smooth functions in usual settings such as nonparametric regression. In this paper, we show that variational inference for sparse deep learning retains the same generalization properties than exact Bayesian inference. In particular, we highlight the connection between estimation and approximation theories via the classical bias-variance trade-off and show that it leads to near-minimax rates of convergence for H\"older smooth functions. Additionally, we show that the model selection framework over the neural network architecture via ELBO maximization does not overfit and adaptively achieves the optimal rate of convergence.

There is an increasing interest in estimating heterogeneity in causal effects in randomized and observational studies. However, little research has been conducted to understand heterogeneity in an instrumental variables study. In this work, we present a method to estimate heterogeneous causal effects using an instrumental variable approach. The method has two parts. The first part uses subject-matter knowledge and interpretable machine learning techniques, such as classification and regression trees, to discover potential effect modifiers. The second part uses closed testing to test for the statistical significance of the effect modifiers while strongly controlling familywise error rate. We conducted this method on the Oregon Health Insurance Experiment, estimating the effect of Medicaid on the number of days an individual's health does not impede their usual activities, and found evidence of heterogeneity in older men who prefer English and don't self-identify as Asian and younger individuals who have at most a high school diploma or GED and prefer English.

Qualitative relationships describe how increasing or decreasing one property (e.g. altitude) affects another (e.g. temperature). They are an important aspect of natural language question answering and are crucial for building chatbots or voice agents where one may enquire about qualitative relationships. Recently a dataset about question answering involving qualitative relationships has been proposed, and a few approaches to answer such questions have been explored, in the heart of which lies a semantic parser that converts the natural language input to a suitable logical form. A problem with existing semantic parsers is that they try to directly convert the input sentences to a logical form. Since the output language varies with each application, it forces the semantic parser to learn almost everything from scratch. In this paper, we show that instead of using a semantic parser to produce the logical form, if we apply the generate-validate framework i.e. generate a natural language description of the logical form and validate if the natural language description is followed from the input text, we get a better scope for transfer learning and our method outperforms the state-of-the-art by a large margin of 7.93%.

Object oriented programming with classes Understanding the power of object oriented programming using abstract data types Defining abstract data types using classes Writing class member and static functions Understanding the class and object structure Exploiting Python's dynamic class and object behaviour