posted by user: claudiogalicchio || 3003 views || tracked by 4 users: [display]

Random-Weights Neural Networks @ IWANN 2019 : Special Session on Random-Weights Neural Networks at IWANN 2019

FacebookTwitterLinkedInGoogle

Link: http://iwann.uma.es/?page_id=290#SS08
 
When Jun 12, 2019 - Jun 14, 2019
Where Gran Canaria, Spain
Submission Deadline Feb 1, 2019
Notification Due Mar 18, 2019
Final Version Due Mar 26, 2019
Categories    neural networks   machine learning
 

Call For Papers

Random-weights Neural Networks identify a class of artificial neural models that employ a form of randomization in both their architectural and training design. Typically, connections to the hidden layer(s) are left untrained after initialization, and only the output weights need to be adjusted through learning (typically, by means of non-iterative methods). Extreme efficiency of training algorithms, along with the ease of implementation, made the randomized approach to Neural Networks design an incredibly widespread and popular methodology among both researchers and practitioners. Besides, from a theoretical perspective, randomization enables an effective study of the inherent properties for various kinds of Neural Networks architectures, even in the absence of (or prior to) training of internal weights connections. In literature, the approach has been instantiated in several forms, both in the case of feed-forward models (e.g., Random Vector Functional Link, Extreme Learning Machine, No-prop and Stochastic Configuration Networks), and for recurrent architectures (e.g., Echo State Networks, Liquid State Machines). Moreover, the rise of the Deep Learning era in Machine Learning research has recently given a further impulse to the study of hierarchically organized neural architectures with multiple random-weights components. In this concern, the potentialities of combining the advantages of deep architectures and the efficiency of randomized Neural Networks approaches remain still largely unexplored.

This session calls for contributions in the area of random weights Neural Networks from all perspectives, from seminal works on breakthrough ideas to applications of consolidated learning methodologies. Topics of interest for this session include, but are not limited to, the following:

- Neural Networks with random weights
- Randomized algorithms for Neural Networks
- Non-iterative methods for learning
- Random Vector Functional Link, Extreme Learning Machines, No-prop, and Stochastic Configuration Networks
- Reservoir Computing, Echo State Networks, and Liquid State Machines
- Deep Neural Networks with Random Weights (e.g. Deep Extreme Learning Machines and Deep Echo State Networks)
- Theoretical analysis on advantages and downsides of randomized Neural Networks
- Comparisons with fully trained Neural Networks
- Real-world Applications

Related Resources

ICMLA 2024   23rd International Conference on Machine Learning and Applications
IEEE COINS 2024   IEEE COINS 2024 - London, UK - July 29-31 - Hybrid (In-Person & Virtual)
ICONIP 2024   31st International Conference on Neural Information Processing
ECAI 2024   27th European Conference on Artificial Intelligence
NCTA 2024   16th International Conference on Neural Computation
MLANN 2024   2024 2nd Asia Conference on Machine Learning, Algorithms and Neural Networks (MLANN 2024)
NYC-2024-NN 2024   New York Annual Conference on Neural Networks 2024
JCICE 2024   2024 International Joint Conference on Information and Communication Engineering(JCICE 2024)
IDEAL 2024   Intelligent Data Engineering and Automated Learning
CVIPPR 2024   2024 2nd Asia Conference on Computer Vision, Image Processing and Pattern Recognition