Meta Reinforcement Learning with Latent Variable Gaussian Processes

Learning from small data sets is critical in many practical applications where data collection is time consuming or expensive, e.g., robotics, animal experiments or drug design. Meta learning is one way to increase the data efficiency of learning algorithms by generalizing learned concepts from a set of training tasks to unseen, but related, tasks. Often, this relationship between tasks is hard coded or relies in some other way on human expertise. In this paper, we frame meta learning as a hierarchical latent variable model and infer the relationship between tasks automatically from data. We apply our framework in a model-based reinforcement learning setting and show that our meta-learning model effectively generalizes to novel tasks by identifying how new tasks relate to prior ones from minimal data. This results in up to a 60% reduction in the average interaction time needed to solve tasks compared to strong baselines.Comment: 11 pages, 7 figure

The Translatory Wave Model for Landslides

The Saint-Venant equations are usually the basis of numerical models for landslide flows. They are nonstationary and nonlinear. The theory for translatory waves in a prismatic channel and a funneling channel can be used for landslides using the assumption of either turbulent or laminar flow in the slide. The mathematics of translatory waves traveling over dry land or superimposed on another flow are developed. This results in a new slope factor controlling the flow velocity, together with the Chezy coefficient used in previous applications of the translatory wave theory. Flow times for the slide to reach a given destination, slide depth, and velocity can be calculated using the initial magnitude of the flow in the slide. The instabilities of the wave tail are discussed. Three case studies are presented: a submarine slide that started the Tohoku tsunami in Japan, the Morsárjökull rock avalanche in SE Iceland, and the Móafellshyrna slide in central N Iceland

Meta reinforcement learning with latent variable Gaussian processes

Learning from small data sets is critical in many practical applications where data collection is time consuming or expensive, e.g., robotics, animal experiments or drug design. Meta learning is one way to increase the data efficiency of learning algorithms by generalizing learned concepts from a set of training tasks to unseen, but related, tasks. Often, this relationship between tasks is hard coded or relies in some other way on human expertise. In this paper, we frame meta learning as a hierarchical latent variable model and infer the relationship between tasks automatically from data. We apply our framework in a modelbased reinforcement learning setting and show that our meta-learning model effectively generalizes to novel tasks by identifying how new tasks relate to prior ones from minimal data. This results in up to a 60% reduction in the average interaction time needed to solve tasks compared to strong baselines

Probabilistic Active Meta-Learning

Data-efficient learning algorithms are essential in many practical applications where data collection is expensive, e.g., in robotics due to the wear and tear. To address this problem, meta-learning algorithms use prior experience about tasks to learn new, related tasks efficiently. Typically, a set of training tasks is assumed given or randomly chosen. However, this setting does not take into account the sequential nature that naturally arises when training a model from scratch in real-life: how do we collect a set of training tasks in a data-efficient manner? In this work, we introduce task selection based on prior experience into a meta-learning algorithm by conceptualizing the learner and the active meta-learning setting using a probabilistic latent variable model. We provide empirical evidence that our approach improves data-efficiency when compared to strong baselines on simulated robotic experiments

Actinomycosis in a 70 year old woman with a forgotten intrauterine contraceptive device

Neðst á síðunni er hægt að nálgast greinina í heild sinni með því að smella á hlekkinn View/OpenActinomycosis is an infectious disease that has been known since the late nineteenth century. In the pre-antibiotic era it was thought to be rather common but with increased use of antimicrobial agents its incidence has decreased significantly. The causative agent, most commonly Actinomyces israelii, is part of the commensal bacterial flora. It can infect any tissue, respects no tissue boundaries and can spread throughout the body. The clinical presentation of this illness can be similar to malignant disease and definite diagnosis is sometimes not apparent until after surgery and histologic examination. We report the case of a 71 year old woman who suffered from actinomycosis of the uterus and ovaries due to a forgotten intrauterine contraceptive device that had been in place for over four decades. The disease presentation was consistent with malignant disease and tumor markers, CA 125, CA 19-9 and CEA, measured in blood were elevated. She was treated successfully with total hysterectomy and bilateral salphingo-oophorectomy, as well as penicillin for six months.Geislagerlabólga (actinomycosis) er sjúkdómur sem þekktur hefur verið síðan um lok nítjándu aldar. Fyrir tíma sýklalyfja var hann fremur algengur, en með tilkomu sýklalyfja hefur verulega dregið úr algengi sjúkdómsins. Sýkillinn er oftast Actinomyces israelii og finnst víða í líkamanum sem hluti af eðlilegri bakteríuflóru. Hann getur lagst á alla vefi, virðir ekki hefðbundin vefjamörk og getur dreifst víða. Birtingarmynd sýkingarinnar getur verið áþekk krabbameini og oft liggur endanleg greining ekki fyrir fyrr en eftir skurðaðgerð og vefjarannsókn. Hér er lýst sjúkratilfelli þar sem 71 árs gömul kona fékk geislagerlabólgu í leg og eggjastokka út frá lykkju sem hafði verið til staðar í rúmlega fjóra áratugi og gleymst. Birtingarmynd sjúkdómsins var áþekk því að um krabbamein væri að ræða og voru æxlisvísarnir CA 125, CA19-9 og CEA hækkaðir. Konan var læknuð með brottnámi á legi og eggjastokkum ásamt penicillíngjöf í sex mánuði

Variational Integrator Networks for Physically Meaningful Embeddings

Learning workable representations of dynamical systems is becoming an increasingly important problem in a number of application areas. By leveraging recent work connecting deep neural networks to systems of differential equations, we propose variational integrator networks, a class of neural network architectures designed to preserve the geometric structure of physical systems. This class of network architectures facilitates accurate long-term prediction, interpretability, and data-efficient learning, while still remaining highly flexible and capable of modeling complex behavior. We demonstrate that they can accurately learn dynamical systems from both noisy observations in phase space and from image pixels within which the unknown dynamics are embedded

Learning Contact Dynamics using Physically Structured Neural Networks

Learning physically structured representations of dynamical systems that include contact between different objects is an important problem for learning-based approaches in robotics. Black-box neural networks can learn to approximately represent discontinuous dynamics, but they typically require large quantities of data and often suffer from pathological behaviour when forecasting for longer time horizons. In this work, we use connections between deep neural networks and differential equations to design a family of deep network architectures for representing contact dynamics between objects. We show that these networks can learn discontinuous contact events in a data-efficient manner from noisy observations in settings that are traditionally difficult for black-box approaches and recent physics inspired neural networks. Our results indicate that an idealised form of touch feedback -- which is heavily relied upon by biological systems -- is a key component of making this learning problem tractable. Together with the inductive biases introduced through the network architectures, our techniques enable accurate learning of contact dynamics from observations