arXiv subjects

Rui L. Lopes

Publications and source records attributed to Rui L. Lopes.

5 recordsLinked to original sources

arXiv2024

Approaching Test Time Augmentation in the Context of Uncertainty Calibration for Deep Neural Networks

With the rise of Deep Neural Networks, machine learning systems are nowadays ubiquitous in a number of real-world applications, which bears the need for highly reliable models. This requires a thorough look not only at the accuracy of such systems, but also at their predictive uncertainty. Hence, we propose a novel technique (with two different variations, named M-ATTA and V-ATTA) based on test time augmentation, to improve the uncertainty calibration of deep models for image classification. By leveraging na adaptive weighting system, M/V-ATTA improves uncertainty calibration without affecting the model's accuracy. The performance of these techniques is evaluated by considering diverse metrics related to uncertainty calibration, demonstrating their robustness. Empirical results, obtained on CIFAR-10, CIFAR-100, Aerial Image Dataset, as well as in two different scenarios under distribution-shift, indicate that the proposed methods outperform several state-of-the-art post-hoc calibration techniques. Furthermore, the methods proposed also show improvements in terms of predictive entropy on out-of-distribution samples. Code for M/V-ATTA available at: https://github.com/pedrormconde/MV-ATTA

cs.CV↗

arXiv2024

A Theoretical and Practical Framework for Evaluating Uncertainty Calibration in Object Detection

The proliferation of Deep Neural Networks has resulted in machine learning systems becoming increasingly more present in various real-world applications. Consequently, there is a growing demand for highly reliable models in many domains, making the problem of uncertainty calibration pivotal when considering the future of deep learning. This is especially true when considering object detection systems, that are commonly present in safety-critical applications such as autonomous driving, robotics and medical diagnosis. For this reason, this work presents a novel theoretical and practical framework to evaluate object detection systems in the context of uncertainty calibration. This encompasses a new comprehensive formulation of this concept through distinct formal definitions, and also three novel evaluation metrics derived from such theoretical foundation. The robustness of the proposed uncertainty calibration metrics is shown through a series of representative experiments.

cs.CV↗

arXiv2018

Proceedings of the Workshop on Data Mining for Geophysics and Geology

Modern geosciences have to deal with large quantities and a wide variety of data, including 2-D, 3-D and 4-D seismic surveys, well logs generated by sensors, detailed lithological records, satellite images and meteorological records. These data serve important industries, such as the exploration of mineral deposits and the production of energy (Oil and Gas, Geothermal, Wind, Hydroelectric), are important in the study of the earth crust to reduce the impact of earthquakes, in land use planning, and have a fundamental role in sustainability. The volume of raw data being stored by different earth science archives today makes it impossible to rely on manual examination by scientists. The data volumes resultant of different sources, from terrestrial or aerial to satellite surveys, will reach a terabyte per day by the time all the planned satellites are flown. In particular, the oil industry has been using large quantities of data for quite a long time. Although there are published works in this area since the 70s, these days, the ubiquity of computing and sensor devices enables the collection of higher resolution data in real time, giving a new life to a mature industrial field. Understanding and finding value in this data has an impact on the efficiency of the operations in the oil and gas production chain. Efficiency gains are particularly important since the steep fall in oil prices in 2014, and represent an important opportunity for data mining and data science.

physics.geo-ph↗

arXiv2017

Proceedings of the Workshop on Data Mining for Oil and Gas

The process of exploring and exploiting Oil and Gas (O&G) generates a lot of data that can bring more efficiency to the industry. The opportunities for using data mining techniques in the "digital oil-field" remain largely unexplored or uncharted. With the high rate of data expansion, companies are scrambling to develop ways to develop near-real-time predictive analytics, data mining and machine learning capabilities, and are expanding their data storage infrastructure and resources. With these new goals, come the challenges of managing data growth, integrating intelligence tools, and analyzing the data to glean useful insights. Oil and Gas companies need data solutions to economically extract value from very large volumes of a wide variety of data generated from exploration, well drilling and production devices and sensors. Data mining for oil and gas industry throughout the lifecycle of the reservoir includes the following roles: locating hydrocarbons, managing geological data, drilling and formation evaluation, well construction, well completion, and optimizing production through the life of the oil field. For each of these phases during the lifecycle of oil field, data mining play a significant role. Based on which phase were talking about, knowledge creation through scientific models, data analytics and machine learning, a effective, productive, and on demand data insight is critical for decision making within the organization. The significant challenges posed by this complex and economically vital field justify a meeting of data scientists that are willing to share their experience and knowledge. Thus, the Worskhop on Data Mining for Oil and Gas (DM4OG) aims to provide a quality forum for researchers that work on the significant challenges arising from the synergy between data science, machine learning, and the modeling and optimization problems in the O&G industry.

cs.AI↗

arXiv2017

Mind the Gap: A Well Log Data Analysis

The main task in oil and gas exploration is to gain an understanding of the distribution and nature of rocks and fluids in the subsurface. Well logs are records of petro-physical data acquired along a borehole, providing direct information about what is in the subsurface. The data collected by logging wells can have significant economic consequences, due to the costs inherent to drilling wells, and the potential return of oil deposits. In this paper, we describe preliminary work aimed at building a general framework for well log prediction. First, we perform a descriptive and exploratory analysis of the gaps in the neutron porosity logs of more than a thousand wells in the North Sea. Then, we generate artificial gaps in the neutron logs that reflect the statistics collected before. Finally, we compare Artificial Neural Networks, Random Forests, and three algorithms of Linear Regression in the prediction of missing gaps on a well-by-well basis.

cs.AI↗