DeepOBS: A Deep Learning Optimizer Benchmark Suite

Because the choice and tuning of the optimizer affects the speed, and ultimately the performance of deep learning, there is significant past and recent research in this area. Yet, perhaps surprisingly, there is no generally agreed-upon protocol for the quantitative and reproducible evaluation of optimization strategies for deep learning. We suggest routines and benchmarks for stochastic optimization, with special focus on the unique aspects of deep learning, such as stochasticity, tunability and generalization. As the primary contribution, we present DeepOBS, a Python package of deep learning optimization benchmarks. The package addresses key challenges in the quantitative assessment of stochastic optimizers, and automates most steps of benchmarking. The library includes a wide and extensible set of ready-to-use realistic optimization problems, such as training Residual Networks for image classification on ImageNet or character-level language prediction models, as well as popular classics like MNIST and CIFAR-10. The package also provides realistic baseline results for the most popular optimizers on these test problems, ensuring a fair comparison to the competition when benchmarking new optimizers, and without having to run costly experiments. It comes with output back-ends that directly produce LaTeX code for inclusion in academic publications. It supports TensorFlow and is available open source.Comment: Accepted at ICLR 2019. 9 pages, 3 figures, 2 table

Sparsity Invariant CNNs

In this paper, we consider convolutional neural networks operating on sparse inputs with an application to depth upsampling from sparse laser scan data. First, we show that traditional convolutional networks perform poorly when applied to sparse data even when the location of missing data is provided to the network. To overcome this problem, we propose a simple yet effective sparse convolution layer which explicitly considers the location of missing data during the convolution operation. We demonstrate the benefits of the proposed network architecture in synthetic and real experiments with respect to various baseline approaches. Compared to dense baselines, the proposed sparse convolution network generalizes well to novel datasets and is invariant to the level of sparsity in the data. For our evaluation, we derive a novel dataset from the KITTI benchmark, comprising 93k depth annotated RGB images. Our dataset allows for training and evaluating depth upsampling and depth prediction techniques in challenging real-world settings and will be made available upon publication

Sensory Experiences and Expectations of Organic Food. Results of Focus Group Discussions

This executive summary describes the main objectives and findings from a qualitative survey on consumers’ sensory experiences, expectations and preferences with respect to organic food. The survey was conducted in the frame of the European Commission funded project ECROPOLIS in 2009 in Germany (DE), France (FR), Italy (IT), Netherlands (NL), Poland (PL) and Switzerland (CH). The objectives of this research were to explore: - the range of experiences, expectations and preferences for specific sensory properties of organic food. - words that are used by consumers to differentiate the taste of organic products amongst themselves and compared to conventional ones. - symbolic’ meanings and images which participants relate to sensory characteristics of organic food. - consumers’ sensory expectations and preferences related to the variability and standardisation of organic food. - consumers’ experiences to marketing of sensory characteristics of organic food. - possible differences in consumers’ sensory expectations and preferences between the participating countries

Decoding consumer preferences in wine : predictive analytics and machine learning in analyzing Portuguese wine consumer ratings

The increasing significance of electronic markets and platforms has revolutionized how consumers interact with and purchase products such as wine. This change provides more information availability to distinguish it from others. Consequently, it is easier to convey a distinct message for characterful products. Nevertheless, it also results in drawbacks like information overload and a loss of the ability to differentiate. This thesis uses advanced analytics to decode the most important factors for consumer preferences in the Portuguese wine market. At the same time, it addresses the challenges and opportunities presented by the information-rich environment of electronic marketplaces. Specifically, I conducted a study to identify, using predictive analytics tools, the essential qualitative product features of Portuguese wine that matter for consumer satisfaction. To do this, robust predictive models were built using individual consumer review ratings and the descriptive characteristics of Portuguese wines from the electronic marketplace and platform Vivino. Ultimately, relative feature importance was determined using Random Forest, AdaBoost, Gradient Boosting, and XGBoost. Additionally, the study incorporates user comments via topic modeling into the predictive models. As a result of this analysis, the study revealed consumer participation, user engagement, sensory perception, generalization, and price description as driving factors for consumer satisfaction. In summary, all models demonstrated similar outcomes, recommending a focus on extrinsic rather than intrinsic product attributes to differentiate from other product groups. These findings can be used further for strategic market decisions and research.A crescente importância dos mercados e plataformas eletrônicos revolucionou a forma como os consumidores interagem e compram produtos, especialmente o vinho. Esta mudança oferece maior disponibilidade de informação para destacar produtos únicos, mas também traz desafios como a sobrecarga de informação e perda de diferenciação. Esta tese investiga o uso de análises avançadas para decifrar os principais fatores que influenciam as preferências dos consumidores no mercado de vinhos português, abordando os desafios e oportunidades do ambiente rico em informações desses mercados. Realizei um estudo que emprega ferramentas de análise preditiva para identificar as características qualitativas essenciais do vinho português que impactam a satisfação do consumidor. Modelos preditivos robustos foram desenvolvidos usando avaliações de consumidores e características dos vinhos portugueses em mercados eletrônicos e na plataforma Vivino. Métodos como Random Forest, AdaBoost, Gradient Boosting e XGBoost ajudaram a determinar a importância relativa dessas características, incorporando também comentários dos usuários através da modelagem de tópicos. Os resultados revelaram que a participação e envolvimento do consumidor, percepção sensorial, generalização e descrição de preço são fatores cruciais para a satisfação do cliente. Todos os modelos apontaram para a necessidade de focar em atributos extrínsecos, ao invés de intrínsecos, para se diferenciar no mercado. Essas descobertas são valiosas para estratégias de mercado e pesquisas futuras

Important aspects in the formulation of solid-fluid debris-flow models. Part I. Thermodynamic implications

This article points at some critical issues which are connected with the theoretical formulation of the thermodynamics of solid-fluid mixtures of frictional materials. It is our view that a complete thermodynamic exploitation of the second law of thermodynamics is necessary to obtain the proper parameterizations of the constitutive quantities in such theories. These issues are explained in detail in a recently published book by Schneider and Hutter (Solid-Fluid Mixtures of Frictional Materials in Geophysical and Geotechnical Context, 2009), which we wish to advertize with these notes. The model is a saturated mixture of an arbitrary number of solid and fluid constituents which may be compressible or density preserving, which exhibit visco-frictional (visco-hypoplastic) behavior, but are all subject to the same temperature. Mass exchange between the constituents may account for particle size separation and phase changes due to fragmentation and abrasion. Destabilization of a saturated soil mass from the pre- and the post-critical phases of a catastrophic motion from initiation to deposition is modeled by symmetric tensorial variables which are related to the rate independent parts of the constituent stress tensor

Important aspects in the formulation of solid-fluid debris-flow models. Part II. Constitutive modelling

This article is the continuation of Part I: ‘Thermodynamic Implications' of a article with the same title. Knowledge of the content/results of Part I, Hutter and Schneider (Continuum Mech. Thermodyn., 2009) or Schneider and Hutter (Solid-Fluid Mixtures of Frictional Materials in Geophysical and Geotechnical Context, 2009), is assumed. The intention is to see whether (i) well-known formulations of binary mixture models can be derived from the thermodynamic model, (ii) classical hypo-plasticity is deducible from the frictional evolution equation and (iii) the popular assumption of pressure equilibrium is justified. To this end, we ignore mass and volume fraction interaction rate densities, restrict considerations to isothermal processes, ignore higher order non-linearities in the constitutive relations and use the principle of phase separation. These assumptions transform the equilibrium stresses, heat flux and interaction forces to considerably simplified forms. Furthermore, the analysis shows that classical hypo-plasticity can be reconstructed with the introduction of a new objective time derivative for the stress-like variable. Non-equilibrium contributions to the stresses and interaction forces are also briefly discussed. It is, finally, shown that the assumption of pressure equilibrium precludes the application of frictional stresses in equilibrium. This unphysical assumption is, therefore, replaced by a thermodynamic closure condition that is more flexible and less restrictive. It allows for frictional stresses in thermodynamic equilibrium and, therefore, is sufficiently general for applications to mixture theorie