scholarly journals Large-scale prediction of disulphide bridges using kernel methods, two-dimensional recursive neural networks, and weighted graph matching

2005 ◽  
Vol 62 (3) ◽  
pp. 617-629 ◽  
Author(s):  
Jianlin Cheng ◽  
Hiroto Saigo ◽  
Pierre Baldi
Keyword(s):  
Neural Networks ◽  
Kernel Methods ◽  
Large Scale ◽  
Graph Matching ◽  
Weighted Graph ◽  
Two Dimensional ◽  
Recursive Neural Networks

scholarly journals An Introduction to Recursive Neural Networks and Kernel Methods for Cheminformatics

2007 ◽  
Vol 13 (14) ◽  
pp. 1469-1495 ◽  
Author(s):  
Alessio Micheli ◽  
Alessandro Sperduti ◽  
Antonina Starita
Keyword(s):  
Neural Networks ◽  
Kernel Methods ◽  
Recursive Neural Networks

scholarly journals Convolutional Neural Networks for Crystal Material Property Prediction Using Hybrid Orbital-Field Matrix and Magpie Descriptors

Crystals ◽  
2019 ◽  
Vol 9 (4) ◽  
pp. 191 ◽  
Author(s):  
Zhuo Cao ◽  
Yabo Dan ◽  
Zheng Xiong ◽  
Chengcheng Niu ◽  
Xiang Li ◽  
...  
Keyword(s):  
Neural Networks ◽  
Convolutional Neural Networks ◽  
Large Scale ◽  
Feature Fusion ◽  
Computational Prediction ◽  
Support Vector ◽  
Two Dimensional ◽  
Material Informatics ◽  
Crystal Material ◽  
Vector Machines

Computational prediction of crystal materials properties can help to do large-scale insiliconscreening. Recent studies of material informatics have focused on expert design of multidimensionalinterpretable material descriptors/features. However, successes of deep learning suchas Convolutional Neural Networks (CNN) in image recognition and speech recognition havedemonstrated their automated feature extraction capability to effectively capture the characteristicsof the data and achieve superior prediction performance. Here, we propose CNN-OFM-Magpie, aCNN model with OFM (Orbital-field Matrix) and Magpie descriptors to predict the formationenergy of 4030 crystal material by exploiting the complementarity of two-dimensional OFM featuresand Magpie features. Experiments showed that our method achieves better performance thanconventional regression algorithms such as support vector machines and Random Forest. It is alsobetter than CNN models using only the OFM features, the Magpie features, or the basic one-hotencodings. This demonstrates the advantages of CNN and feature fusion for materials propertyprediction. Finally, we visualized the two-dimensional OFM descriptors and analyzed the featuresextracted by the CNN to obtain greater understanding of the CNN-OFM model.

scholarly journals Ab initio and template-based prediction of multi-class distance maps by two-dimensional recursive neural networks

2009 ◽  
Vol 9 (1) ◽  
pp. 5 ◽  
Author(s):  
Ian Walsh ◽  
Davide Baù ◽  
Alberto JM Martin ◽  
Catherine Mooney ◽  
Alessandro Vullo ◽  
...  
Keyword(s):  
Neural Networks ◽  
Ab Initio ◽  
Two Dimensional ◽  
Recursive Neural Networks

scholarly journals Learning for Graph Matching and Related Combinatorial Optimization Problems

2020 ◽  
Author(s):  
Junchi Yan ◽  
Shuang Yang ◽  
Edwin Hancock
Keyword(s):  
Machine Learning ◽  
Neural Networks ◽  
Combinatorial Optimization ◽  
Prediction Models ◽  
Graph Matching ◽  
Optimization Problems ◽  
Weighted Graph ◽  
Building Blocks ◽  
Transformative Change ◽  
Combinatorial Optimization Problems

This survey gives a selective review of recent development of machine learning (ML) for combinatorial optimization (CO), especially for graph matching. The synergy of these two well-developed areas (ML and CO) can potentially give transformative change to artificial intelligence, whose foundation relates to these two building blocks. For its representativeness and wide-applicability, this paper is more focused on the problem of weighted graph matching, especially from the learning perspective. For graph matching, we show that many learning techniques e.g. convolutional neural networks, graph neural networks, reinforcement learning can be effectively incorporated in the paradigm for extracting the node features, graph structure features, and even the matching engine. We further present outlook for the new settings for learning graph matching, and direction towards more integrated combinatorial optimization solvers with prediction models, and also the mutual embrace of traditional solver and machine learning components.

scholarly journals Address Assignment in Indoor Wireless Networks Using Deterministic Channel Simulation

2013 ◽  
Vol 2013 ◽  
pp. 1-13
Author(s):  
Edgar Holleis ◽  
Christoph Grimm
Keyword(s):  
Large Scale ◽  
Graph Matching ◽  
Test Procedure ◽  
Path Loss ◽  
Weighted Graph ◽  
Wireless Channel ◽  
Single Node ◽  
Data Set ◽  
Channel Simulation ◽  
High Level

A crucial step during commissioning of wireless sensor and automation networks is assigning high-level node addresses (e.g., floor/room/fixture) to nodes mounted at their respective location. This address assignment typically requires visiting every single node prior to, during, or after mounting. For large-scale networks it also presents a considerable logistical effort. This paper describes a new approach to automatically assign high-level addresses without visiting every node. First, the wireless channel is simulated using a deterministic channel simulation in order to obtain node-to-node estimates of path loss. Next, the channel is measured by a precommissioning test procedure on the live network. In a third step, results from measurements and simulation are condensed into graphs and matched against each other. The resulting problem, identified as weighted graph matching, is solved heuristically. Viability of the approach and its performance is demonstrated by means of a publicly available test data set, which the algorithm is able to solve flawlessly. Further points of interest are the conditions that lead to high quality address assignments.

A Developmental Method for Evolving Large-Scale Spiking Neural Networks

2012 ◽  
Vol 35 (12) ◽  
pp. 2633 ◽  
Author(s):  
Xiang-Hong LIN ◽  
Tian-Wen ZHANG ◽  
Gui-Cang ZHANG
Keyword(s):  
Neural Networks ◽  
Large Scale ◽  
Spiking Neural Networks
Sign in / Sign up

Export Citation Format

Share Document