Publication date: 2018
Licence:
CC BY-NC-ND
Editorial team
Editor-in-Chief Stanisław Migórski
Deputy Editor-in-Chief Andrzej Bielecki
Secretary Krzysztof Misztal
Schedae Informaticae, Volume 27, 2018, pp. 9 - 17
https://doi.org/10.4467/20838476SI.18.001.10406Schedae Informaticae, Volume 27, 2018, pp. 19 - 30
https://doi.org/10.4467/20838476SI.18.002.10407Schedae Informaticae, Volume 27, 2018, pp. 31 - 45
https://doi.org/10.4467/20838476SI.18.003.10408Schedae Informaticae, Volume 27, 2018, pp. 47 - 57
https://doi.org/10.4467/20838476SI.18.004.10409Schedae Informaticae, Volume 27, 2018, pp. 59 - 68
https://doi.org/10.4467/20838476SI.18.005.10410Schedae Informaticae, Volume 27, 2018, pp. 69 - 79
https://doi.org/10.4467/20838476SI.18.006.10411Schedae Informaticae, Volume 27, 2018, pp. 81 - 91
https://doi.org/10.4467/20838476SI.18.007.10412Schedae Informaticae, Volume 27, 2018, pp. 93 - 106
https://doi.org/10.4467/20838476SI.18.008.10413Schedae Informaticae, Volume 27, 2018, pp. 107 - 127
https://doi.org/10.4467/20838476SI.18.009.10414Schedae Informaticae, Volume 27, 2018, pp. 129 - 141
https://doi.org/10.4467/20838476SI.18.010.10415Schedae Informaticae, Volume 27, 2018, pp. 143 - 153
https://doi.org/10.4467/20838476SI.18.011.10416Schedae Informaticae, Volume 27, 2018, pp. 155 - 164
https://doi.org/10.4467/20838476SI.18.012.10417Schedae Informaticae, Volume 27, 2018, pp. 165 - 182
https://doi.org/10.4467/20838476SI.18.013.10418Słowa kluczowe: cutting & packing, optimization, object packing problem, phi-functions, deep learning, traffic optimization, metamodels, activation functions, genetic algorithm, gradient descent, neural network, generalization, gradient regularization, spectral norm, Frobenius norm, gradient descent, optimization methods, adaptive step size, dynamic learning rate, neural networks, Goodness of fit test (for normality), optimal transport distance, Wasserstein distance, autoencoder-based generative model., Generative model, AutoEncoder, Wasserstein distances, word embeddings, temporal expressions, recognition, TimeML, CRF, LSTM, BiLSTM, KGR10, FastText, reinforcement learning, value predictors, exploration, entopry, stiching, optimization, natural language processing, natural language inference, representation learning, word embeddings, machine learning, deep learning., adversarial samples, convolutional neural networks, classification, sensors, information quality, railway data, spatial confirmation