This book introduces approaches to generalize the benefits of equivariant deep learning to a broader set of learned structures through learned homomorphisms. In the field of machinelearning, the idea of incorpo...
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ISBN:
(数字)9783031881114
ISBN:
(纸本)9783031881107;9783031881138
This book introduces approaches to generalize the benefits of equivariant deep learning to a broader set of learned structures through learned homomorphisms. In the field of machinelearning, the idea of incorporating knowledge of data symmetries into artificial neural networks is known as equivariant deep learning and has led to the development of cutting edge architectures for image and physical data processing. The power of these models originates from data-specific structures ingrained in them through careful engineering. To-date however, the ability for practitioners to build such a structure into models is limited to situations where the data must exactly obey specific mathematical symmetries. The authors discuss naturally inspired inductive biases, specifically those which may provide types of efficiency and generalization benefits through what are known as homomorphic representations, a new general type of structured representation inspired from techniques in physics and neuroscience. A review of some of the first attempts at building models with learned homomorphic representations are introduced. The authors demonstrate that these inductive biases improve the ability of models to represent natural transformations and ultimately pave the way to the future of efficient and effective artificial neural networks.
This book constitutes the refereed proceedings of the joint conference on machinelearning and Knowledge Discovery in databases: ECML PKDD 2009, held in Bled, Slovenia, in September 2009. The 106 papers presented in t...
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ISBN:
(数字)9783642041808
ISBN:
(纸本)9783642041792
This book constitutes the refereed proceedings of the joint conference on machinelearning and Knowledge Discovery in databases: ECML PKDD 2009, held in Bled, Slovenia, in September 2009. The 106 papers presented in two volumes, together with 5 invited talks, were carefully reviewed and selected from 422 paper submissions. In addition to the regular papers the volume contains 14 abstracts of papers appearing in full version in the machinelearning Journal and the Knowledge Discovery and databases Journal of Springer. The conference intends to provide an international forum for the discussion of the latest high quality research results in all areas related to machinelearning and knowledge discovery in databases. The topics addressed are application of machinelearning and data mining methods to real-world problems, particularly exploratory research that describes novel learning and mining tasks and applications requiring non-standard techniques.
This book constitutes the refereed proceedings of the 8th International Conference on Intelligent Computing, ICIC 2012, held in Huangshan, China, in July 2012.The 242 revised full papers presented in the three volumes...
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ISBN:
(数字)9783642318375
ISBN:
(纸本)9783642318368
This book constitutes the refereed proceedings of the 8th International Conference on Intelligent Computing, ICIC 2012, held in Huangshan, China, in July 2012.
The 242 revised full papers presented in the three volumes LNCS 7389, LNAI 7390, and CCIS 304 were carefully reviewed and selected from 753 submissions. The papers in this volume (CCIS 304) are organized in topical sections on Neural Networks; Particle Swarm Optimization and Niche Technology; Kernel Methods and Supporting Vector machines; Biology Inspired Computing and Optimization; Knowledge Discovery and data Mining; Intelligent Computing in Bioinformatics; Intelligent Computing in Pattern Recognition; Intelligent Computing in Image Processing; Intelligent Computing in computer Vision; Intelligent Control and Automation; Knowledge Representation/Reasoning and Expert Systems; Advances in Information Security; Protein and Gene Bioinformatics; Soft Computing and Bio-Inspired Techiques in Real-World Applications; Bio-Inspired Computing and Applications.
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