scholarly journals ESeismic-GAN: A Generative Model for Seismic Events from Cotopaxi Volcano

2021 ◽  
pp. 1-1
Author(s):  
Felipe Grijalva ◽  
Washington Ramos ◽  
Noel Perez ◽  
Diego Benitez ◽  
Roman Lara ◽  
...  
Keyword(s):  
Generative Model ◽  
Seismic Events ◽  
Cotopaxi Volcano

Time and Frequency Feature Selection for Seismic Events from Cotopaxi Volcano

2015 ◽  
Author(s):  
Roman Lara-Cueva ◽  
Paul Bernal ◽  
Maria Gabriela Saltos ◽  
Diego S. Benitez ◽  
Jose Luis Rojo-Alvarez
Keyword(s):  
Feature Selection ◽  
Seismic Events ◽  
Cotopaxi Volcano ◽  
Selection For ◽  
Frequency Feature

Exploring the unsupervised classification of seismic events of Cotopaxi volcano

2020 ◽  
Vol 403 ◽  
pp. 107009 ◽  
Author(s):  
Adrian Duque ◽  
Kevin González ◽  
Noel Pérez ◽  
Diego Benítez ◽  
Felipe Grijalva ◽  
...  
Keyword(s):  
Unsupervised Classification ◽  
Seismic Events ◽  
Cotopaxi Volcano

AutoLinker: Automatic Fragment Linking with Deep Conditional Transformer Neural Networks

2020 ◽  
Author(s):  
Yuyao Yang ◽  
Shuangjia Zheng ◽  
Shimin Su ◽  
Jun Xu ◽  
Hongming Chen
Keyword(s):  
Neural Networks ◽  
Drug Discovery ◽  
Drug Design ◽  
State Of The Art ◽  
The State ◽  
Generative Model ◽  
Lead Optimization ◽  
Scaffold Hopping ◽  
Reference Models ◽  
Lead Generation

Fragment based drug design represents a promising drug discovery paradigm complimentary to the traditional HTS based lead generation strategy. How to link fragment structures to increase compound affinity is remaining a challenge task in this paradigm. Hereby a novel deep generative model (AutoLinker) for linking fragments is developed with the potential for applying in the fragment-based lead generation scenario. The state-of-the-art transformer architecture was employed to learn the linker grammar and generate novel linker. Our results show that, given starting fragments and user customized linker constraints, our AutoLinker model can design abundant drug-like molecules fulfilling these constraints and its performance was superior to other reference models. Moreover, several examples were showcased that AutoLinker can be useful tools for carrying out drug design tasks such as fragment linking, lead optimization and scaffold hopping.

Effective Image Auto-Annotation via Discriminative Hyperplane Tree Based Generative Model

2009 ◽  
Vol 20 (9) ◽  
pp. 2450-2461
Author(s):  
Mei WANG ◽  
Xiang-Dong ZHOU ◽  
Hong-Tao XU ◽  
Bai-Le SHI
Keyword(s):  
Generative Model
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