Grammar Variational Autoencoder

Deep generative models have been wildly successful at learning coherent latent representations for continuous data such as video and audio. However, generative modeling of discrete data such as arithmetic expressions and molecular structures still poses significant challenges. Crucially, state-of-the-art methods often produce outputs that are not valid. We make the key observation that frequently, discrete data can be represented as a parse tree from a context-free grammar. We propose a variational autoencoder which encodes and decodes directly to and from these parse trees, ensuring the generated outputs are always valid. Surprisingly, we show that not only does our model more often generate valid outputs, it also learns a more coherent latent space in which nearby points decode to similar discrete outputs. We demonstrate the effectiveness of our learned models by showing their improved performance in Bayesian optimization for symbolic regression and molecular synthesis

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

Depth uncertainty in neural networks

Existing methods for estimating uncertainty in deep learning tend to require multiple forward passes, making them unsuitable for applications where computational resources are limited. To solve this, we perform probabilistic reasoning over the depth of neural networks. Different depths correspond to subnetworks which share weights and whose predictions are combined via marginalisation, yielding model uncertainty. By exploiting the sequential structure of feed-forward networks, we are able to both evaluate our training objective and make predictions with a single forward pass. We validate our approach on real-world regression and image classification tasks. Our approach provides uncertainty calibration, robustness to dataset shift, and accuracies competitive with more computationally expensive baselines

On the impact of covariance functions in multi-objective Bayesian optimization for engineering design

This is the author accepted manuscript. The final version is available from the publisher via the DOI in this recordMulti-objective Bayesian optimization (BO) is a highly useful class of methods that can effectively solve computationally expensive engineering design optimization problems with multiple objectives. However, the impact of covariance function, which is an important part of multi-objective BO, is rarely studied in the context of engineering optimization. We aim to shed light on this issue by performing numerical experiments on engineering design optimization problems, primarily low-fidelity problems so that we are able to statistically evaluate the performance of BO methods with various covariance functions. In this paper, we performed the study using a set of subsonic airfoil optimization cases as benchmark problems. Expected hypervolume improvement was used as the acquisition function to enrich the experimental design. Results show that the choice of the covariance function give a notable impact on the performance of multi-objective BO. In this regard, Kriging models with Matern-3/2 is the most robust method in terms of the diversity and convergence to the Pareto front that can handle problems with various complexities.Natural Environment Research Council (NERC

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

Barking up the right tree: An approach to search over molecule synthesis DAGs

When designing new molecules with particular properties, it is not only important what to make but crucially how to make it. These instructions form a synthesis directed acyclic graph (DAG), describing how a large vocabulary of simple building blocks can be recursively combined through chemical reactions to create more complicated molecules of interest. In contrast, many current deep generative models for molecules ignore synthesizability. We therefore propose a deep generative model that better represents the real world process, by directly outputting molecule synthesis DAGs. We argue that this provides sensible inductive biases, ensuring that our model searches over the same chemical space that chemists would also have access to, as well as interpretability. We show that our approach is able to model chemical space well, producing a wide range of diverse molecules, and allows for unconstrained optimization of an inherently constrained problem: maximize certain chemical properties such that discovered molecules are synthesizable

A generative model for electron paths

Chemical reactions can be described as the stepwise redistribution of electrons in molecules. As such, reactions are often depicted using “arrow-pushing” diagrams which show this movement as a sequence of arrows. We propose an electron path prediction model (ELECTRO) to learn these sequences directly from raw reaction data. Instead of predicting product molecules directly from reactant molecules in one shot, learning a model of electron movement has the benefits of (a) being easy for chemists to interpret, (b) incorporating constraints of chemistry, such as balanced atom counts before and after the reaction, and (c) naturally encoding the sparsity of chemical reactions, which usually involve changes in only a small number of atoms in the reactants. We design a method to extract approximate reaction paths from any dataset of atom-mapped reaction SMILES strings. Our model achieves excellent performance on an important subset of the USPTO reaction dataset, comparing favorably to the strongest baselines. Furthermore, we show that our model recovers a basic knowledge of chemistry without being explicitly trained to do so.EPSR

The Importance of Sustainability in the Loyalty to a Tourist Destination through the Management of Expectations and Experiences

Sustainability has become one of the key factors for the development of tourism both nowadays and in the future. The need to integrate environmental, socio-cultural and economic factors is a consequence of the evolution of society itself, the introduction of new information and communication technologies (ICTs) and a new way of understanding tourism and the world in general. Tourists increasingly seek a unique quality in their travels and are better informed before deciding on a tourist destination to spend their holidays or leisure time. They want to have unique, memorable experiences, and because of that, they are willing to look for those destinations that can o er them something di erent. The generation of expectations is no longer the sole responsibility of companies and public and private organizations in destinations, since information may be in the hands of the individuals themselves who can share it in social networks, blogs, or on platforms such as Booking or TripAdvisor, among others. This forces companies and public and private organizations to rethink the way in which and when they relate to tourists in general. With all these considerations, one of the objectives of this study was to analyse the way in which sustainability interrelates with the generation of expectations, experiences and perceptions and the e ect on the possibilities of returning to a tourist destination and even recommending it in social networks to friends and acquaintances. For this reason, the destination of Acapulco, Guerrero, Mexico, was chosen, a mature destination of sun and beach that, in recent years, has been immersed in a process of change where one of the axes is sustainability. This study used a convenience survey with 310 valid questionnaires with tourists who stayed more than three days in Acapulco during the months of December 2016 to February 2017. The questionnaires were completed at di erent points of the destination and by participants over 18 years of age. We used SEM (Structural Equations Modeling) and EQS (Structural Equation Modeling Software) for statistical analysis. The results of the study showed how expectations influenced experiences and the intention to return to the destination and recommend it (WOM), thus, we proposed a series of recommendations for public and private agents that manage this tourist destination

¿Cómo se percibe la aplicación de procesos de calidad de la ingeniería, en la administración pública?

This research focuses on the quality processes applied in the Port Administration of Veracruz (APIVER). Responsible for providing infrastructure and port services with an efficient logistics model that favors the optimization of costs in the goods value chains, which generating greater profitability for the port and its business partners. We carried out a study among the APIVER command staffand theexternalstaff working in the portarea. To do this, wasapplied the questionnaire through a Google app, in order to find out the level of knowledge they have regarding the quality processes.El objeto de esta investigación son los procesos de calidad aplicados en la Administración Portuaria de Veracruz, misma que es la encargada de proporcionar infraestructura y servicios portuarios, con un modelo logístico eficiente, que favorezca la optimización de los costos en las cadenas de valor de las mercancías, generando mayor rentabilidad para el puerto y sus socios comerciales. Se realizó una investigación entre el personal de mando de la APIVER y el personal externo que labora en el recinto portuario, en el cual se aplicó un cuestionario mediante una aplicación de Google, para darnos cuenta del nivel de conocimiento que cuentan con los procesos de calidad