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of 271
pro vyhledávání: '"Soares, Eduardo P."'
Autor:
Priyadarsini, Indra, Takeda, Seiji, Hamada, Lisa, Brazil, Emilio Vital, Soares, Eduardo, Shinohara, Hajime
Large-scale molecular representation methods have revolutionized applications in material science, such as drug discovery, chemical modeling, and material design. With the rise of transformers, models now learn representations directly from molecular
Externí odkaz:
http://arxiv.org/abs/2410.12348
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
Autor:
Soares, Eduardo, Kishimoto, Akihiro, Brazil, Emilio Vital, Takeda, Seiji, Kajino, Hiroshi, Cerqueira, Renato
Pre-trained Language Models have emerged as promising tools for predicting molecular properties, yet their development is in its early stages, necessitating further research to enhance their efficacy and address challenges such as generalization and
Externí odkaz:
http://arxiv.org/abs/2310.13802
Autor:
Soares, Eduardo, Brazil, Emilio Vital, Gutierrez, Karen Fiorela Aquino, Cerqueira, Renato, Sanders, Dan, Schmidt, Kristin, Zubarev, Dmitry
We present a novel multimodal language model approach for predicting molecular properties by combining chemical language representation with physicochemical features. Our approach, MULTIMODAL-MOLFORMER, utilizes a causal multistage feature selection
Externí odkaz:
http://arxiv.org/abs/2306.14919
Autor:
Brazil, Emilio Vital, Soares, Eduardo, Real, Lucas Villa, Azevedo, Leonardo, Segura, Vinicius, Zerkowski, Luiz, Cerqueira, Renato
Data is a critical element in any discovery process. In the last decades, we observed exponential growth in the volume of available data and the technology to manipulate it. However, data is only practical when one can structure it for a well-defined
Externí odkaz:
http://arxiv.org/abs/2303.05545
Earth observation is fundamental for a range of human activities including flood response as it offers vital information to decision makers. Semantic segmentation plays a key role in mapping the raw hyper-spectral data coming from the satellites into
Externí odkaz:
http://arxiv.org/abs/2210.12820
Efficient textual data distributions (TDD) alignment and generation are open research problems in textual analytics and NLP. It is presently difficult to parsimoniously and methodologically confirm that two or more natural language datasets belong to
Externí odkaz:
http://arxiv.org/abs/2107.02025
Autor:
Angelov, Plamen, Soares, Eduardo
In this paper we introduce the DMR -- a prototype-based method and network architecture for deep learning which is using a decision tree (DT)-based inference and synthetic data to balance the classes. It builds upon the recently introduced xDNN metho
Externí odkaz:
http://arxiv.org/abs/2002.03776
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