Proceedings of International Joint Conference on Neural Networks 2020 (IJCNN 2020)

Piscataway : IEEE, 2020.

Under the general editorship: A. Roy

2020 International Joint Conference on Neural Networks (IJCNN) held virtually, as part of the IEEE World Congress on Computational Intelligence (IEEE WCCI) 2020. IJCNN 2020 is jointly organized by the IEEE Computational Intelligence Society (CIS) and the International Neural Network Society (INNS). For IJCNN 2020 (and when WCCI is organized in even-numbered years) IEEE CIS is the lead society and financial sponsor. IJCNN 2020 is the major event in the field of neural networks and learning systems, covering all topics in the field from theory to applications. IJCNN provides a forum for researchers, students and professionals in the field of Neural Network and Learning Systems. The meeting is a unique opportunity to present our research to other colleagues and exchange the latest advances in theories, technologies and practices. It is tremendous opportunity also to know what the trending topics are, the current state-of-the-art and the main applications of Neural Networks and Learning Systems. IJCNN 2020 accepted 1134 papers for inclusion in the conference program at an acceptance rate of 57%. Out of this, 608 papers are being presented in oral sessions and 526 in poster sessions. The largest contributors by country are China (29.7%), USA (15.7%), UK (15.2%), Brazil (10.1%), Australia (8.8%), Japan (7.8%) and India (7.1%). The country assigned to a paper was the country from which its first author came. The program of IJCNN 2020 reflects a rich variety of topics: Deep Learning, Extreme Learning Machines, Feed forward NNs and Supervised Learning, Online and Incremental Learning, Spiking Neural Networks, Unsupervised Learning and Clustering, ADP and Reinforcement Learning, Recurrent NNs and Reservoir Networks, Concept Drift, ML Methods Robust to Large Outliers, Complex Valued NNs, Neural Models and Computation, Memory and Sensory Systems, Semi-supervised Learning and Neuromorphic Hardware. Likewise, a large number of papers deal with a great variety of applications.

Chapters

Sequential Analysis with Specified Confidence Level and Adaptive Convolutional Neural Networks in Image Recognition

Savchenko A., , in: Proceedings of International Joint Conference on Neural Networks 2020 (IJCNN 2020).: Piscataway: IEEE, 2020. P. 1–8.

In this paper the problem of high computational complexity of deep convolutional nets in image recognition is considered. An existing framework of adaptive neural networks is extended by appending the separate classifier to intermediate layers. The hierarchical representations of the input image are sequentially analyzed. If the first classifier returns rather high confidence score, the ...

Added: October 15, 2020

Event Recognition with Automatic Album Detection based on Sequential Grouping of Confidence Scores and Neural Attention

Savchenko A., , in: Proceedings of International Joint Conference on Neural Networks 2020 (IJCNN 2020).: Piscataway: IEEE, 2020. P. 1–8.

In this paper a new formulation of event recognition task is examined: it is required to predict event categories given a gallery of images, for which albums (groups of photos corresponding to a single event) are unknown. The novel two-stage approach is proposed. At first, features are extracted in each photo using the pre-trained convolutional ...

Added: October 15, 2020

Keywords: машинное обучение, компьютерное зрение, machine learning, искусственные нейронные сети, computer vision, deep learning, глубокое обучение, Artificial Neural Network (ANN)