Over the past two years, we have invested in enhancing all aspects of deep learning methods to ensure the book is comprehensive and impeccable. Taking into account feedback from our readers and audience, the author has diligently updated this book.
An efficient representation of molecular structures is crucial for the implementation of data-driven methods. Here the authors present t-SMILES, a representation that encodes molecular substructures into strings, giving more structure to the SMILES representation. Juan-Ni Wu , Tong Wang & Ru-Qin Yu...
This comprehensive and systematic study shed light on the iodine adsorption performance of MOF materials under humid conditions, providing valuable insights for the future screening and design of high-performance MOF materials. Methods Simulation method GCMC simulations were employed using RASPA software to...
Difficulty in reference input information: Generally, the most tracking methods are using two sources for detection and tracking of objects: visual features and motility information. According to recent advances in deep learning techniques, it seems that by using these methods, which system learns that...
Big Data Creates New Opportunities for Materials Research: A Review on Methods and Applications of Machine Learning for Materials Design 2Big data in materials science As illustrated inFig. 2[17], for thousands of years, science consisted of empirical observations of natural phenomena. A few centuri...
Yagawa, G., Oishi, A. (2021). Deep Learning for Computational Mechanics. In: Computational Mechanics with Neural Networks. Lecture Notes on Numerical Methods in Engineering and Sciences. Springer, Cham. https://doi.org/10.1007/978-3-030-66111-3_16 ...
efficiency. For example, how parallel the system setup is; what architecture model has(e.g. group convolution costs in MACs); what computing platform the model uses(e.g. Cudnn has GPU acceleration for deep neural networks and standard operations such as forward or normalization are highly ...
The use of machine learning and deep learning methods for solving similar nature problems has extensively been studied by the research community [31–33]. 5.2. Identification Results The traditional least-squares method paired with the Coulomb viscous friction model was used to identify the same expe...
For the construction of a programming problem recommendation algorithm, a programming problem recommendation framework based on deep reinforcement learning (DRLP) is proposed. It designs specific methods for action space, evaluation Q-network, and reward function more in line with the programming problem...
However, it is important to note that, in many problems with tabular data, other methods such as gradient-boosted decision trees often outperform fully connected neural networks, as can be seen from the results of Kaggle machine learning competitions. Nevertheless, fully connected layers constitute ...