Titlebook: Intelligent Systems and Applications; Proceedings of the 2 Kohei Arai Conference proceedings 2022 The Editor(s) (if applicable) and The Aut

eustachian-tube

Scope and Sense of Explainability for AI-Systems,ch in retrospect were characterized as ingenious (for example move 37 of the game 2 of AlphaGo). It will be elaborated on arguments supporting the notion that if AI-solutions were to be discarded in advance because of their not being thoroughly comprehensible, a great deal of the potentiality of intelligent systems would be wasted.

種族被根除

Machine Learning Based , Norm Minimization for Maglev Vibration Isolation Platform,n classical and modern control approaches. In this study, Q-Learning RL algorithm combined with analytic LMI method has been utilized to solve micro-scale vibration isolation problem as energy efficient as possible.

細(xì)胞學(xué)

Domain Generalization Using Ensemble Learning,spective, we build an ensemble model on top of base deep learning models trained on a single source to enhance the generalization of their collective prediction. The results achieved thus far have demonstrated promising improvements of the ensemble over any of its base learners.

Mortal

collagen

Reputation Analysis Based on Weakly-Supervised Bi-LSTM-Attention Network,introduced, which is helpful to capture the important information in the context and improve the accuracy of sentiment classification. Finally, we use TF-IDF and LDA topic models to mine the review topics and extract the consumers’ opinions on different sentiment polarities.

cultivated

Multi-GPU-based Convolutional Neural Networks Training for Text Classification, of each trained model with the use of an effective communication strategy. Our proposed model was tested on different multiple-GPUs environments. We achieved a good speedup compared to the sequential CNN training. Its accuracy is also very competitive.

懲罰

火花

,DAC–Deep Autoencoder-Based Clustering: A General Deep Learning Framework of Representation Learninghms then might not work. In this paper, we propose DAC, Deep Autoencoder-based Clustering, a generalized data-driven framework to learn clustering representations using deep neuron networks. Experiment results show that our approach could effectively boost performance of the K-Means clustering algorithm on a variety types of datasets.

CRACY

承認(rèn)