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Multi-instance learning for structure-activity modeling for molecular properties
Zankov D.V., Shevelev M.D., Nikonenko A.V., Polishchuk P.G., Rakhimbekova A.I., Madzhidov T.I.
© Springer Nature Switzerland AG 2020. In this paper, the approach of multi-instance learning
MULTI-INSTANCE LEARNING for RHETORIC STRUCTURE PARSING
Volkov S., Devyatkin D., Shvets A.
MULTI-INSTANCE LEARNING for RHETORIC STRUCTURE PARSING
Multi-Instance Learning Approach to Predictive Modeling of Catalysts Enantioselectivity
Zankov D., Polishchuk P., Madzhidov T., Varnek A.
Here, we report an application of the multi-instance learning approach to predictive modeling
QSAR Modeling Based on Conformation Ensembles Using a Multi-Instance Learning Approach
Zankov D.V., Matveieva M., Nikonenko A.V., Nugmanov R.I., Baskin I.I., Varnek A., Polishchuk P., Madzhidov T.I.
is the choice of a putative bioactive conformation, which affects the predictive performance. The multi-instance
Supervised Learning: from the allocation algorithm to the identification of hidden interpretable patterns
Rodchenko, V. G., Родченко, В. Г.
In Supervised Learning, traditionally, training is aimed at constructing an algorithm
A multiagent approach for cost estimating in big data analytics
Azofeifa E.J.
approached in a simpler way. Processes in artificial intelligence, for instance, nowadays encounter digital
The efficiency of heuristic subject-oriented learning for students’ multi-dimensional thinking self-development
Andreyev V.
is to substantiate the essence and peculiarities of heuristic subject-oriented learning and its focus on students
The efficiency of heuristic subject-oriented learning for students’ multi-dimensional thinking self-development
Andreyev V.
is to substantiate the essence and peculiarities of heuristic subject-oriented learning and its focus on students
Exploring convolutional neural networks and topic models for user profiling from drug reviews
Tutubalina E., Nikolenko S.
single-task and multi-task learning approaches to this problem. Based on a real-world dataset mined from
Adaptation of adversarial machine learning for training agents to counter data attacks
Khajynava, N., Mutero, Z., Adam, A.
Adversarial Machine Learning (AML) has emerged as a critical field of study, focusing on enhancing

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