6 citations · 11 across the 12 of their papers we have counts for
12 papers
Incorporating Expert Rules into Neural Networks in the Framework of Concept-Based Learning
Andrei V. Konstantinov, Lev V. Utkin
A problem of incorporating the expert rules into machine learning models for extending the concept-based learning is formulated in the paper. It is proposed how to combine logical…
Generating Survival Interpretable Trajectories and Data
Andrei V. Konstantinov, Stanislav R. Kirpichenko, Lev V. Utkin
A new model for generating survival trajectories and data based on applying an autoencoder of a specific structure is proposed. It solves three tasks. First, it provides prediction…
Dual feature-based and example-based explanation methods
Andrei V. Konstantinov, Boris V. Kozlov, Stanislav R. Kirpichenko +1
A new approach to the local and global explanation is proposed. It is based on selecting a convex hull constructed for the finite number of points around an explained instance. The…
SurvBeX: An explanation method of the machine learning survival models based on the Beran estimator
Lev V. Utkin, Danila Y. Eremenko, Andrei V. Konstantinov
An explanation method called SurvBeX is proposed to interpret predictions of the machine learning survival black-box models. The main idea behind the method is to use the modified…
A New Computationally Simple Approach for Implementing Neural Networks with Output Hard Constraints
Andrei V. Konstantinov, Lev V. Utkin
A new computationally simple method of imposing hard convex constraints on the neural network output values is proposed. The key idea behind the method is to map a vector of hidden…
Neural Attention Forests: Transformer-Based Forest Improvement
Andrei V. Konstantinov, Lev V. Utkin, Alexey A. Lukashin +1
A new approach called NAF (the Neural Attention Forest) for solving regression and classification tasks under tabular training data is proposed. The main idea behind the proposed N…