Recent Developments in Generative Adversarial Networks: A Review (Workshop Paper)

2020 ◽  
Author(s):  
Ankit Yadav ◽  
Dinesh Kumar Vishwakarma
Keyword(s):  
Generative Adversarial Networks ◽  
Adversarial Networks ◽  
Recent Developments

scholarly journals Next Generation Generative Neural Networks for HEP

2019 ◽  
Vol 214 ◽  
pp. 09005 ◽  
Author(s):  
Steven Farrell ◽  
Wahid Bhimji ◽  
Thorsten Kurth ◽  
Mustafa Mustafa ◽  
Deborah Bard ◽  
...  
Keyword(s):  
Particle Physics ◽  
Large Scale ◽  
Mass Parameter ◽  
Parameter Tuning ◽  
Generative Adversarial Networks ◽  
Distributed Resources ◽  
Distributed Training ◽  
Adversarial Networks ◽  
Recent Developments ◽  
Better Than

Initial studies have suggested generative adversarial networks (GANs) have promise as fast simulations within HEP. These studies, while promising, have been insufficiently precise and also, like GANs in general, suffer from stability issues.We apply GANs to to generate full particle physics events (not individual physics objects), explore conditioning of generated events based on physics theory parameters and evaluate the precision and generalization of the produced datasets. We apply this to SUSY mass parameter interpolation and pileup generation. We also discuss recent developments in convergence and representations that match the structure of the detector better than images.In addition we describe on-going work making use of large-scale distributed resources on the Cori supercomputer at NERSC, and developments to control distributed training via interactive jupyter notebook sessions. This will allow tackling high-resolution detector data; model selection and hyper-parameter tuning in a productive yet scalable deep learning environment.

scholarly journals Color-Patterns to Architecture Conversion through Conditional Generative Adversarial Networks

Biomimetics ◽  
2021 ◽  
Vol 6 (1) ◽  
pp. 16
Author(s):  
Diego Navarro-Mateu ◽  
Oriol Carrasco ◽  
Pedro Cortes Cortes Nieves
Keyword(s):  
Machine Learning ◽  
Neural Networks ◽  
Color Pattern ◽  
Generative Adversarial Networks ◽  
Architecture Design ◽  
Learning Tools ◽  
Color Patterns ◽  
Adversarial Networks ◽  
Recent Developments ◽  
Evo Devo

Often an apparent complex reality can be extrapolated into certain patterns that in turn are evidenced in natural behaviors (whether biological, chemical or physical). The Architecture Design field has manifested these patterns as a conscious (inspired designs) or unconscious manner (emerging organizations). If such patterns exist and can be recognized, can we therefore use them as genotypic DNA? Can we be capable of generating a phenotypic architecture that is manifestly more complex than the original pattern? Recent developments in the field of Evo-Devo around gene regulators patterns or the explosive development of Machine Learning tools could be combined to set the basis for developing new, disruptive workflows for both design and analysis. This study will test the feasibility of using conditional Generative Adversarial Networks (cGANs) as a tool for coding architecture into color pattern-based images and translating them into 2D architectural representations. A series of scaled tests are performed to check the feasibility of the hypothesis. A second test assesses the flexibility of the trained neural networks against cases outside the database.

ORGANIC (1).pdf

2017 ◽  
Author(s):  
Benjamin Sanchez-Lengeling ◽  
Carlos Outeiral ◽  
Gabriel L. Guimaraes ◽  
Alan Aspuru-Guzik
Keyword(s):  
Machine Learning ◽  
Learning Community ◽  
Chemical Species ◽  
Material Design ◽  
Organic Photovoltaic ◽  
Generative Adversarial Networks ◽  
Generative Adversarial Network ◽  
Adversarial Network ◽  
Adversarial Networks ◽  
Photovoltaic Material

Molecular discovery seeks to generate chemical species tailored to very specific needs. In this paper, we present ORGANIC, a framework based on Objective-Reinforced Generative Adversarial Networks (ORGAN), capable of producing a distribution over molecular space that matches with a certain set of desirable metrics. This methodology combines two successful techniques from the machine learning community: a Generative Adversarial Network (GAN), to create non-repetitive sensible molecular species, and Reinforcement Learning (RL), to bias this generative distribution towards certain attributes. We explore several applications, from optimization of random physicochemical properties to candidates for drug discovery and organic photovoltaic material design.

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