Advances in Neural Information Processing Systems 22 PDF Download
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Author: Yoshua Bengio
Publisher:
ISBN:
Category :
Languages : en
Pages :
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Author: Yoshua Bengio
Publisher:
ISBN:
Category :
Languages : en
Pages :
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Author: Neural Information Processing Systems Foundation
Publisher:
ISBN:
Category :
Languages : en
Pages : 23
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Author: Henry Markram
Publisher: Frontiers E-books
ISBN: 2889190439
Category :
Languages : en
Pages : 575
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Book Description
Hebb's postulate provided a crucial framework to understand synaptic alterations underlying learning and memory. Hebb's theory proposed that neurons that fire together, also wire together, which provided the logical framework for the strengthening of synapses. Weakening of synapses was however addressed by "not being strengthened", and it was only later that the active decrease of synaptic strength was introduced through the discovery of long-term depression caused by low frequency stimulation of the presynaptic neuron. In 1994, it was found that the precise relative timing of pre and postynaptic spikes determined not only the magnitude, but also the direction of synaptic alterations when two neurons are active together. Neurons that fire together may therefore not necessarily wire together if the precise timing of the spikes involved are not tighly correlated. In the subsequent 15 years, Spike Timing Dependent Plasticity (STDP) has been found in multiple brain brain regions and in many different species. The size and shape of the time windows in which positive and negative changes can be made vary for different brain regions, but the core principle of spike timing dependent changes remain. A large number of theoretical studies have also been conducted during this period that explore the computational function of this driving principle and STDP algorithms have become the main learning algorithm when modeling neural networks. This Research Topic will bring together all the key experimental and theoretical research on STDP.
Author: A.C.C. Coolen
Publisher: OUP Oxford
ISBN: 9780191583001
Category : Neural networks (Computer science)
Languages : en
Pages : 596
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Book Description
Theory of Neural Information Processing Systems provides an explicit, coherent, and up-to-date account of the modern theory of neural information processing systems. It has been carefully developed for graduate students from any quantitative discipline, including mathematics, computer science, physics, engineering or biology, and has been thoroughly class-tested by the authors over a period of some 8 years. Exercises are presented throughout the text and notes on historical background and further reading guide the student into the literature. All mathematical details are included and appendices provide further background material, including probability theory, linear algebra and stochastic processes, making this textbook accessible to a wide audience.
Author: Neural Information Processing Systems
Publisher: Curran Associates Incorporated
ISBN: 9781615679119
Category : Computers
Languages : en
Pages : 2348
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Author: Daphne Koller
Publisher:
ISBN:
Category :
Languages : en
Pages : 664
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Author: Grégoire Montavon
Publisher: Springer
ISBN: 3642352898
Category : Computers
Languages : en
Pages : 753
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Book Description
The twenty last years have been marked by an increase in available data and computing power. In parallel to this trend, the focus of neural network research and the practice of training neural networks has undergone a number of important changes, for example, use of deep learning machines. The second edition of the book augments the first edition with more tricks, which have resulted from 14 years of theory and experimentation by some of the world's most prominent neural network researchers. These tricks can make a substantial difference (in terms of speed, ease of implementation, and accuracy) when it comes to putting algorithms to work on real problems.
Author: Monica Bianchini
Publisher: Springer Science & Business Media
ISBN: 3642366570
Category : Technology & Engineering
Languages : en
Pages : 547
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Book Description
This handbook presents some of the most recent topics in neural information processing, covering both theoretical concepts and practical applications. The contributions include: Deep architectures Recurrent, recursive, and graph neural networks Cellular neural networks Bayesian networks Approximation capabilities of neural networks Semi-supervised learning Statistical relational learning Kernel methods for structured data Multiple classifier systems Self organisation and modal learning Applications to content-based image retrieval, text mining in large document collections, and bioinformatics This book is thought particularly for graduate students, researchers and practitioners, willing to deepen their knowledge on more advanced connectionist models and related learning paradigms.
Author: Selçuk Candan
Publisher: Springer
ISBN: 331955753X
Category : Computers
Languages : en
Pages : 695
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Book Description
This two volume set LNCS 10177 and 10178 constitutes the refereed proceedings of the 22nd International Conference on Database Systems for Advanced Applications, DASFAA 2017, held in Suzhou, China, in March 2017. The 73 full papers, 9 industry papers, 4 demo papers and 3 tutorials were carefully selected from a total of 300 submissions. The papers are organized around the following topics: semantic web and knowledge management; indexing and distributed systems; network embedding; trajectory and time series data processing; data mining; query processing and optimization; text mining; recommendation; security, privacy, senor and cloud; social network analytics; map matching and spatial keywords; query processing and optimization; search and information retrieval; string and sequence processing; stream date processing; graph and network data processing; spatial databases; real time data processing; big data; social networks and graphs.
Author: Haiqin Yang
Publisher: Springer
ISBN: 9783030638221
Category : Computers
Languages : en
Pages : 844
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Book Description
The two-volume set CCIS 1332 and 1333 constitutes thoroughly refereed contributions presented at the 27th International Conference on Neural Information Processing, ICONIP 2020, held in Bangkok, Thailand, in November 2020.* For ICONIP 2020 a total of 378 papers was carefully reviewed and selected for publication out of 618 submissions. The 191 papers included in this volume set were organized in topical sections as follows: data mining; healthcare analytics-improving healthcare outcomes using big data analytics; human activity recognition; image processing and computer vision; natural language processing; recommender systems; the 13th international workshop on artificial intelligence and cybersecurity; computational intelligence; machine learning; neural network models; robotics and control; and time series analysis. * The conference was held virtually due to the COVID-19 pandemic.
