Zobrazeno 1 - 10
of 48 338
pro vyhledávání: '"Cerqueira AS"'
Autor:
Soares, Eduardo, Shirasuna, Victor, Brazil, Emilio Vital, Cerqueira, Renato, Zubarev, Dmitry, Schmidt, Kristin
Large-scale pre-training methodologies for chemical language models represent a breakthrough in cheminformatics. These methods excel in tasks such as property prediction and molecule generation by learning contextualized representations of input toke
Externí odkaz:
http://arxiv.org/abs/2407.20267
We present a novel approach to generate a fingerprint for crystalline materials that balances efficiency for machine processing and human interpretability, allowing its application in both machine learning inference and understanding of structure-pro
Externí odkaz:
http://arxiv.org/abs/2406.19761
Accurate evaluation of forecasting models is essential for ensuring reliable predictions. Current practices for evaluating and comparing forecasting models focus on summarising performance into a single score, using metrics such as SMAPE. We hypothes
Externí odkaz:
http://arxiv.org/abs/2406.16590
The effectiveness of univariate forecasting models is often hampered by conditions that cause them stress. A model is considered to be under stress if it shows a negative behaviour, such as higher-than-usual errors or increased uncertainty. Understan
Externí odkaz:
http://arxiv.org/abs/2406.17008
Medical image segmentation is a relevant problem, with deep learning being an exponent. However, the necessity of a high volume of fully annotated images for training massive models can be a problem, especially for applications whose images present a
Externí odkaz:
http://arxiv.org/abs/2406.03225
Most forecasting methods use recent past observations (lags) to model the future values of univariate time series. Selecting an adequate number of lags is important for training accurate forecasting models. Several approaches and heuristics have been
Externí odkaz:
http://arxiv.org/abs/2405.11237
Recent state-of-the-art forecasting methods are trained on collections of time series. These methods, often referred to as global models, can capture common patterns in different time series to improve their generalization performance. However, they
Externí odkaz:
http://arxiv.org/abs/2404.18537
Deep learning approaches are increasingly used to tackle forecasting tasks. A key factor in the successful application of these methods is a large enough training sample size, which is not always available. In these scenarios, synthetic data generati
Externí odkaz:
http://arxiv.org/abs/2404.16918
In this paper, we introduce the concept of a "von Neumann regular $\mathcal{C}^{\infty}$-ring", which is a model for a specific equational theory. We delve into the characteristics of these rings and demonstrate that each Boolean space can be effecti
Externí odkaz:
http://arxiv.org/abs/2404.08629
We employed a machine-learning assisted approach to search for superconducting hydrides under ambient pressure within an extensive dataset comprising over 150 000 compounds. Our investigation yielded around 50 systems with transition temperatures sur
Externí odkaz:
http://arxiv.org/abs/2403.13496