In this paper we present a world model, which learns causal features using the invariance principle. In particular, we use contrastive unsupervised learning to learn the invariant causal features, which enforces invariance across augmentations of irrelevant parts or styles of the observation. The world-model-based reinforcement learning methods independently optimize representation learning and the policy. Thus naive contrastive loss implementation collapses due to a lack of supervisory signals to the representation learning module. We propose an intervention invariant auxiliary task to mitigate this issue. Specifically, we utilize depth prediction to explicitly enforce the invariance and use data augmentation as style intervention on the RGB observation space. Our design leverages unsupervised representation learning to learn the world model with invariant causal features. Our proposed method significantly outperforms current state-of-the-art model-based and model-free reinforcement learning methods on out-of-distribution point navigation tasks on the iGibson dataset. Moreover, our proposed model excels at the sim-to-real transfer of our perception learning module. Finally, we evaluate our approach on the DeepMind control suite and enforce invariance only implicitly since depth is not available. Nevertheless, our proposed model performs on par with the state-of-the-art counterpart.

We introduce ASPiRe (Adaptive Skill Prior for RL), a new approach that leverages prior experience to accelerate reinforcement learning. Unlike existing methods that learn a single skill prior from a large and diverse dataset, our framework learns a library of different distinction skill priors (i.e., behavior priors) from a collection of specialized datasets, and learns how to combine them to solve a new task. This formulation allows the algorithm to acquire a set of specialized skill priors that are more reusable for downstream tasks; however, it also brings up additional challenges of how to effectively combine these unstructured sets of skill priors to form a new prior for new tasks. Specifically, it requires the agent not only to identify which skill prior(s) to use but also how to combine them (either sequentially or concurrently) to form a new prior. To achieve this goal, ASPiRe includes Adaptive Weight Module (AWM) that learns to infer an adaptive weight assignment between different skill priors and uses them to guide policy learning for downstream tasks via weighted Kullback-Leibler divergences. Our experiments demonstrate that ASPiRe can significantly accelerate the learning of new downstream tasks in the presence of multiple priors and show improvement on competitive baselines.

The development of deep learning techniques has allowed Neural Machine Translation (NMT) models to become extremely powerful, given sufficient training data and training time. However, systems struggle when translating text from a new domain with a distinct style or vocabulary. Fine-tuning on in-domain data allows good domain adaptation, but requires sufficient relevant bilingual data. Even if this is available, simple fine-tuning can cause overfitting to new data and catastrophic forgetting of previously learned behaviour. We survey approaches to domain adaptation for NMT, particularly where a system may need to translate across multiple domains. We divide techniques into those revolving around data selection or generation, model architecture, parameter adaptation procedure, and inference procedure. We finally highlight the benefits of domain adaptation and multidomain adaptation techniques to other lines of NMT research.

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By Vincent Granville Ph.D, published in September 2022. The book is available here. For my upcoming course based on this book, see here. This book covers the foundations of machine learning, with modern approaches to solving complex problems. Emphasis is on scalability, automation, testing, optimizing, and interpretability (explainable AI).

Hi developers, tech visionaries, innovators, leaders: SAP TechEd is back in November with fresh, new virtual and in-person event experiences. We've heard you, we've missed you, and we can't wait to share with you what's in store for 2022! Registration fee applies, capacity is limited. This year's event consists of 7 tracks covering all aspects of SAP technology and today's most-relevant tech topics, in which you'll be able to attend lectures, breakouts, virtual workshops, in-person workshops, featuring SAP customers and experts: Does your team still process sales orders manually? Does your company still offer generic development paths to its employees?

In the present world, technology is refining very agile and every day we are getting in touch with various new technologies, machines, devices, etc. Human is the creator of such great devices which have a compact size, high speed and can make our life very simple. Now, Artificial Intelligence is the booming technology in computer science which is ready to build a new revolution in the world by building machines with brains. Just a daydream in the domains of science fiction, artificial intelligence (AI) is now mainstream technology in our usual lives with applications in image and voice recognition, language translations, chatbots, and predictive data analysis. A vital part of artificial intelligence deals with outlining or deliberation for a system which can perform mechanical motions. This sort of processing requires input provided by a computer vision system, acting as a vision sensor and providing high-level information about the moving system.

Almost weekly a friend or an acquaintance asks me, I want to learn to code; which language should I start with? More or less bi-weekly I get a DM on LinkedIn starting with My son should start programming; what is the best language for him? It's not just people who've never coded before. Often I get these messages from people who have several years of coding experience under their belts. I'm not saying this to complain.

This concentration features a multi-disciplinary curriculum that draws on insights from computer science, statistics, and business management. You will learn the statistical and computational methods for collecting, storing, and processing data; identifying patterns in large data sets; predicting and interpreting the findings; and making data-driven decisions. Developing additional skills will make you especially attractive to employers, and enable you to tap into more than one job market. Areas of study include actuarial science, marketing, quantitative risk analysis, law, and business. This concentration will prepare to use text mining, machine learning, and A.I. to detect patterns, predict outcomes, and derive insights related to regulation, compliance, litigation, and transactional law.

Interested in recommender systems or do you just want to learn how to build advanced systems consisting of both frontend and backend? Then this course is all you need! You will learn how to setup a API using the programming language Python such that a backend recommender can be remotely called. Furthermore you will learn how to develop a fully working frontend system using Angular and firebase which is capable of presenting user recommendations. During this course you will use a vast range of technologies including Angular, Python, Typescript, MySQL and firebase.