Stochastic Expectation Propagation for Large Scale Gaussian Process Classification

A method for large scale Gaussian process classification has been recently proposed based on expectation propagation (EP). Such a method allows Gaussian process classifiers to be trained on very large datasets that were out of the reach of previous deployments of EP and has been shown to be competitive with related techniques based on stochastic variational inference. Nevertheless, the memory resources required scale linearly with the dataset size, unlike in variational methods. This is a severe limitation when the number of instances is very large. Here we show that this problem is avoided when stochastic EP is used to train the model

Training Deep Gaussian Processes using Stochastic Expectation Propagation and Probabilistic Backpropagation

Deep Gaussian processes (DGPs) are multi-layer hierarchical generalisations of Gaussian processes (GPs) and are formally equivalent to neural networks with multiple, infinitely wide hidden layers. DGPs are probabilistic and non-parametric and as such are arguably more flexible, have a greater capacity to generalise, and provide better calibrated uncertainty estimates than alternative deep models. The focus of this paper is scalable approximate Bayesian learning of these networks. The paper develops a novel and efficient extension of probabilistic backpropagation, a state-of-the-art method for training Bayesian neural networks, that can be used to train DGPs. The new method leverages a recently proposed method for scaling Expectation Propagation, called stochastic Expectation Propagation. The method is able to automatically discover useful input warping, expansion or compression, and it is therefore is a flexible form of Bayesian kernel design. We demonstrate the success of the new method for supervised learning on several real-world datasets, showing that it typically outperforms GP regression and is never much worse

Predictive Complexity Priors

Specifying a Bayesian prior is notoriously difficult for complex models such as neural networks. Reasoning about parameters is made challenging by the high-dimensionality and over-parameterization of the space. Priors that seem benign and uninformative can have unintuitive and detrimental effects on a model's predictions. For this reason, we propose predictive complexity priors: a functional prior that is defined by comparing the model's predictions to those of a reference model. Although originally defined on the model outputs, we transfer the prior to the model parameters via a change of variables. The traditional Bayesian workflow can then proceed as usual. We apply our predictive complexity prior to high-dimensional regression, reasoning over neural network depth, and sharing of statistical strength for few-shot learning

Acute Haemorrhagic Oedema of Infancy as a Manifestation of COVID-19

info:eu-repo/semantics/publishedVersio

The human-reptile bond and its implications for the welfare of captive semiaquatic turtles in Portugal

Semiaquatic turtles are common pets but are arguably one of the most difficult reptiles to maintain because of species-specific thermal, hydric, dietary and behavioural requirements that call for specialized care. Furthermore, keepers’ familiarity with reptilian behavioural and psychological health is largely uncommon. The purpose of this study was to investigate the welfare of captive semiaquatic turtles in Portugal and relate it with the human-animal bond. A survey was developed and 114 turtle keepers participated (Nov.2017 - Feb.2018). The majority of respondents considered the welfare of their animals as being good or very good (75.4%). Regarding the human-reptile bond, 65.8 % of keepers considered their turtle to be a “member of the family”, 64.0% of people claimed to talk with their turtle more than 5 times a week and 70.2% pet them at least once a week. Those who considered the animal to be a family member/friend were not seen to provide better husbandry conditions such as UVB lamp, heat sources or control over temperatures (p>0.05 for all). Over one third of owners (35.9%) never took their turtle to the veterinarian. Having a UVB lamp, providing a heat source and having control over temperatures were not influenced by having visited a veterinary clinician (p>0.05 for all). We conclude that, although most keepers perceive semiaquatic turtles as family members, talking to them and petting them regularly, basic husbandry requirements are not being adequately met. This puts into question to what extent is the human-reptile bond an indicator of good welfare. Whether the problem is lack of proper information, poor communication between the clinician and the keeper, noncompliance or mere negligence are questions that call for additional research.As tartarugas semiaquáticas são frequentemente mantidas como animais de estimação. No entanto, são dos répteis mais difíceis de manter devido às suas necessidades específicas de temperatura, água, dieta e de comportamento, que requerem cuidados especializados. Para além disto, os detentores destes animais têm, de uma forma geral, pouco conhecimento sobre o seu comportamento. O objectivo deste estudo foi investigar o bem-estar das tartarugas semiaquáticas em cativeiro em Portugal e relacioná-lo com a ligação homem-animal. Para o efeito foi desenvolvido um questionário no qual participaram 114 detentores de tartarugas (Nov.2017 - Feb.2018). A maioria dos participantes classificou o bem-estar do seu animal como bom ou muito bom (75.4%). Em relação à ligação homem-animal, 65.8 % dos detentores consideraram a tartaruga como “um membro da família”, 64.0% afirmaram que falavam com a sua tartaruga mais de cinco vezes por semana e 70.2% declararam que a acariciavam pelo menos uma vez por semana. Verificou-se que aqueles que consideraram o animal como um “membro da família” ou “amigo” não proporcionavam melhores condições de maneio como lâmpada UVB, fontes de aquecimento ou temperatura de alojamento controlada (p>0.05 para todos). Mais de um terço dos detentores (35.9%) nunca levou a tartaruga ao veterinário. Não se estabeleceu relação entre ter consultado um veterinário e fornecer lâmpada UVB e fontes de aquecimento, assim como controlar a temperatura do alojamento (p>0.05 para todos). Concluímos que, apesar de a maioria dos detentores de tartarugas semiaquáticas as considerarem como “membros da família”, interagindo e falando com elas regularmente, as condições básicas de maneio e alojamento para estes animais não estão a ser aplicadas corretamente. Estes resultados colocam-nos a seguinte questão: até que ponto pode a ligação homem-animal ser um indicador de bem-estar animal? Se o problema principal é falta de informação, má comunicação entre o detentor e o veterinário, não observância das recomendações veterinárias ou simples negligência, é uma questão que requer uma investigação mais aprofundada

Cluster Structures with Machine Learning Support in Neutron Star M-R relations

Neutron stars (NS) are compact objects with strong gravitational fields, and a matter composition subject to extreme physical conditions. The properties of strongly interacting matter at ultra-high densities and temperatures impose a big challenge to our understanding and modelling tools. Some difficulties are critical, since one cannot reproduce such conditions in our laboratories or assess them purely from astronomical observations. The information we have about neutron star interiors are often extracted indirectly, e.g., from the star mass-radius relation. The mass and radius are global quantities and still have a significant uncertainty, which leads to great variability in studying the micro-physics of the neutron star interior. This leaves open many questions in nuclear astrophysics and the suitable equation of state (EoS) of NS. Recently, new observations appear to constrain the mass-radius and consequently has helped to close some open questions. In this work, utilizing modern machine learning techniques, we analyze the NS mass-radius (M-R) relationship for a set of EoS containing a variety of physical models. Our objective is to determine patterns through the M-R data analysis and develop tools to understand the EoS of neutron stars in forthcoming works.Comment: Contribution to the XLIV Brazilian Workshop on Nuclear Physics, Brazi

Bayesian batch active learning as sparse subset approximation

Leveraging the wealth of unlabeled data produced in recent years provides great potential for improving supervised models. When the cost of acquiring labels is high, probabilistic active learning methods can be used to greedily select the most informative data points to be labeled. However, for many large-scale problems standard greedy procedures become computationally infeasible and suffer from negligible model change. In this paper, we introduce a novel Bayesian batch active learning approach that mitigates these issues. Our approach is motivated by approximating the complete data posterior of the model parameters. While naive batch construction methods result in correlated queries, our algorithm produces diverse batches that enable efficient active learning at scale. We derive interpretable closed-form solutions akin to existing active learning procedures for linear models, and generalize to arbitrary models using random projections. We demonstrate the benefits of our approach on several large-scale regression and classification tasks.Comment: NeurIPS 201

Comparative study of the degree of patient satisfaction in intermittent catheterization with Lofric and polyvinyl chloride catheters

Actas Urol Esp. 2001 Nov-Dec;25(10):725-30. [Comparative study of the degree of patient satisfaction in intermittent catheterization with Lofric and polyvinyl chloride catheters]. [Article in Spanish] López Pereira P, Martínez Urrutia MJ, Lobato L, Rivas S, Jaureguizar Monereo E. SourceUnidad de Urología Infantil, Hospital Universitario La Paz, Madrid. Abstract PURPOSE: To assess the grade of satisfaction in children on intermittent catheterization with the use of LoFric and PVC conventional catheters. MATERIAL AND METHODS: A total of 40 p with experience in CIC were included in this study. An anonymous questionnaire was sent to all patients after 2-months using the LoFric catheter. Patients were divided in 3 groups (bladder augmentation, artificial sphincter, Mitrofanoff) because of major differences in CIC discomfort between these groups. RESULTS: The questionnaire was completed by 87.5% of the patients (35 p). In 86% (30 p) LoFric catheter training was easy or very easy but in 14% (5 p) it was difficult. Four patients had some difficulty during conventional catheter insertion, in 3 (75%) the difficulty disappeared with the use of LoFric catheter. Of the 51% (18 p) who reported some discomfort during the insertion of conventional catheter, 72% said it was eliminated when the LoFric catheter was used. Of 6 p with some discomfort when removing the conventional catheter, 5 (83%) said it disappeared with the new catheter. Th LoFric catheter was favored by 70% of patients because it reduced the discomfort caused by conventional catheters, bladder insertion was easier and smoother, and gel lubrication was not needed. The 17% of patients reported some difficulty dealing with this slippery catheter. CONCLUSIONS: The use of the LoFric catheter could be justified in patients who report with conventional catheters have some discomfort. It can also be recommended in patients with artificial sphincter, bladder augmentation and Mitrofanoff procedure, in whom any complication related to CIC would have serious consequences