scholarly journals Capturing the diversity of biological tuning curves using generative adversarial networks

2017 ◽  
Author(s):  
Takafumi Arakaki ◽  
G. Barello ◽  
Yashar Ahmadian
Keyword(s):  
Machine Learning ◽  
Latent Variables ◽  
Likelihood Function ◽  
Network Models ◽  
Generative Models ◽  
Generative Adversarial Networks ◽  
Connectivity Matrix ◽  
Model Parameters ◽  
Tuning Curves ◽  
Adversarial Networks

AbstractTuning curves characterizing the response selectivities of biological neurons often exhibit large degrees of irregularity and diversity across neurons. Theoretical network models that feature heterogeneous cell populations or random connectivity also give rise to diverse tuning curves. However, a general framework for fitting such models to experimentally measured tuning curves is lacking. We address this problem by proposing to view mechanistic network models as generative models whose parameters can be optimized to fit the distribution of experimentally measured tuning curves. A major obstacle for fitting such models is that their likelihood function is not explicitly available or is highly intractable to compute. Recent advances in machine learning provide ways for fitting generative models without the need to evaluate the likelihood and its gradient. Generative Adversarial Networks (GAN) provide one such framework which has been successful in traditional machine learning tasks. We apply this approach in two separate experiments, showing how GANs can be used to fit commonly used mechanistic models in theoretical neuroscience to datasets of measured tuning curves. This fitting procedure avoids the computationally expensive step of inferring latent variables, e.g., the biophysical parameters of individual cells or the particular realization of the full synaptic connectivity matrix, and directly learns model parameters which characterize the statistics of connectivity or of single-cell properties. Another strength of this approach is that it fits the entire, joint distribution of experimental tuning curves, instead of matching a few summary statistics picked a priori by the user. More generally, this framework opens the door to fitting theoretically motivated dynamical network models directly to simultaneously or non-simultaneously recorded neural responses.

scholarly journals Stochastic Seismic Waveform Inversion Using Generative Adversarial Networks as a Geological Prior

2019 ◽  
Vol 52 (1) ◽  
pp. 53-79 ◽  
Author(s):  
Lukas Mosser ◽  
Olivier Dubrule ◽  
Martin J. Blunt
Keyword(s):  
Differential Equation ◽  
Partial Differential Equation ◽  
Latent Variables ◽  
Generative Models ◽  
Generative Model ◽  
Generative Adversarial Networks ◽  
Bayesian Inversion ◽  
Model Parameters ◽  
Partial Differential ◽  
Seismic Waveform

AbstractWe present an application of deep generative models in the context of partial differential equation constrained inverse problems. We combine a generative adversarial network representing an a priori model that generates geological heterogeneities and their petrophysical properties, with the numerical solution of the partial-differential equation governing the propagation of acoustic waves within the earth’s interior. We perform Bayesian inversion using an approximate Metropolis-adjusted Langevin algorithm to sample from the posterior distribution of earth models given seismic observations. Gradients with respect to the model parameters governing the forward problem are obtained by solving the adjoint of the acoustic wave equation. Gradients of the mismatch with respect to the latent variables are obtained by leveraging the differentiable nature of the deep neural network used to represent the generative model. We show that approximate Metropolis-adjusted Langevin sampling allows an efficient Bayesian inversion of model parameters obtained from a prior represented by a deep generative model, obtaining a diverse set of realizations that reflect the observed seismic response.

scholarly journals Recent Advancement in Generative Adversarial Network Models

2021 ◽  
pp. 28-32
Author(s):  
Rounit Agrawal ◽  
Sakshi Seth ◽  
Niti Patil
Keyword(s):  
Network Models ◽  
Generative Models ◽  
Generative Adversarial Networks ◽  
Generative Adversarial Network ◽  
Adversarial Network ◽  
Adversarial Networks ◽  
Deep Model ◽  
Conventional Machine ◽  
Zero Sum ◽  
Work Done

GANs (Generative Adversarial Networks) have recently gained a lot of attention in the research community. GANs are based on the zero-sum game theory, in which two neural networks compete for the resources. The results of deep model is capable of producing data that is close to any given data distribution. It employs an adversarial learning method and is much more efficient than conventional machine learning models as learning features. In this paper, firstly discusses the introductory detail about GAN followed by the brief literature survey of work done with GAN models and then followed by its different approaches and discusses how they differ. The analysis then goes on to list of the various applications such as computer vision, image classification and processing of language etc. before coming to a conclusion. As well as, compare this GAN model with other generative models and also mentioned the limitation of GAN.

scholarly journals A New Integrated Approach for Landslide Data Balancing and Spatial Prediction Based on Generative Adversarial Networks (GAN)

2021 ◽  
Vol 13 (19) ◽  
pp. 4011
Author(s):  
Husam A. H. Al-Najjar ◽  
Biswajeet Pradhan ◽  
Raju Sarkar ◽  
Ghassan Beydoun ◽  
Abdullah Alamri
Keyword(s):  
Machine Learning ◽  
Spatial Prediction ◽  
Generative Models ◽  
Generative Adversarial Networks ◽  
Slope Aspect ◽  
Support Vector ◽  
Learning Models ◽  
Adversarial Networks ◽  
Landslide Data ◽  
Machine Learning Models

Landslide susceptibility mapping has significantly progressed with improvements in machine learning techniques. However, the inventory / data imbalance (DI) problem remains one of the challenges in this domain. This problem exists as a good quality landslide inventory map, including a complete record of historical data, is difficult or expensive to collect. As such, this can considerably affect one’s ability to obtain a sufficient inventory or representative samples. This research developed a new approach based on generative adversarial networks (GAN) to correct imbalanced landslide datasets. The proposed method was tested at Chukha Dzongkhag, Bhutan, one of the most frequent landslide prone areas in the Himalayan region. The proposed approach was then compared with the standard methods such as the synthetic minority oversampling technique (SMOTE), dense imbalanced sampling, and sparse sampling (i.e., producing non-landslide samples as many as landslide samples). The comparisons were based on five machine learning models, including artificial neural networks (ANN), random forests (RF), decision trees (DT), k-nearest neighbours (kNN), and the support vector machine (SVM). The model evaluation was carried out based on overall accuracy (OA), Kappa Index, F1-score, and area under receiver operating characteristic curves (AUROC). The spatial database was established with a total of 269 landslides and 10 conditioning factors, including altitude, slope, aspect, total curvature, slope length, lithology, distance from the road, distance from the stream, topographic wetness index (TWI), and sediment transport index (STI). The findings of this study have shown that both GAN and SMOTE data balancing approaches have helped to improve the accuracy of machine learning models. According to AUROC, the GAN method was able to boost the models by reaching the maximum accuracy of ANN (0.918), RF (0.933), DT (0.927), kNN (0.878), and SVM (0.907) when default parameters used. With the optimum parameters, all models performed best with GAN at their highest accuracy of ANN (0.927), RF (0.943), DT (0.923) and kNN (0.889), except SVM obtained the highest accuracy of (0.906) with SMOTE. Our finding suggests that RF balanced with GAN can provide the most reasonable criterion for landslide prediction. This research indicates that landslide data balancing may substantially affect the predictive capabilities of machine learning models. Therefore, the issue of DI in the spatial prediction of landslides should not be ignored. Future studies could explore other generative models for landslide data balancing. By using state-of-the-art GAN, the proposed model can be considered in the areas where the data are limited or imbalanced.

scholarly journals A Survey on Generative Adversarial Networks: Variants, Applications, and Training

2022 ◽  
Vol 54 (8) ◽  
pp. 1-49
Author(s):  
Abdul Jabbar ◽  
Xi Li ◽  
Bourahla Omar
Keyword(s):  
Machine Learning ◽  
Computer Vision ◽  
Nash Equilibrium ◽  
Generative Models ◽  
Generative Adversarial Networks ◽  
Data Generation ◽  
Crucial Issue ◽  
Practical Applications ◽  
Adversarial Networks ◽  
And Training

The Generative Models have gained considerable attention in unsupervised learning via a new and practical framework called Generative Adversarial Networks (GAN) due to their outstanding data generation capability. Many GAN models have been proposed, and several practical applications have emerged in various domains of computer vision and machine learning. Despite GANs excellent success, there are still obstacles to stable training. The problems are Nash equilibrium, internal covariate shift, mode collapse, vanishing gradient, and lack of proper evaluation metrics. Therefore, stable training is a crucial issue in different applications for the success of GANs. Herein, we survey several training solutions proposed by different researchers to stabilize GAN training. We discuss (I) the original GAN model and its modified versions, (II) a detailed analysis of various GAN applications in different domains, and (III) a detailed study about the various GAN training obstacles as well as training solutions. Finally, we reveal several issues as well as research outlines to the topic.

scholarly journals Image Synthesis Based On Feature Description

2021 ◽  
Vol 9 (8) ◽  
pp. 2492-2494
Author(s):  
Rohan Bolusani
Keyword(s):  
Neural Network ◽  
Machine Learning ◽  
Language Processing ◽  
Image Synthesis ◽  
Generative Models ◽  
Generative Adversarial Networks ◽  
Feature Representations ◽  
Adversarial Networks ◽  
Text Feature ◽  
Image Modelling

Abstract: Generating realistic images from text is innovative and interesting, but modern-day machine learning models are still far from this goal. With research and development in the field of natural language processing, neural network architectures have been developed to learn discriminative text feature representations. Meanwhile, in the field of machine learning, generative adversarial networks (GANs) have begun to generate extremely accurate images of especially in categories, such as faces, album covers, and room interiors. In this work, the main goal is to develop a neural network to bridge these advances in text and image modelling, by essentially translating characters to pixels the project will demonstrate the capability of generative models by taking detailed text descriptions and generate plausible images. Keywords: Deep Learning, Computer Vision, NLP, Generative Adversarial Networks

A Survey on Adversarial Recommender Systems

2021 ◽  
Vol 54 (2) ◽  
pp. 1-38
Author(s):  
Yashar Deldjoo ◽  
Tommaso Di Noia ◽  
Felice Antonio Merra
Keyword(s):  
Machine Learning ◽  
Recommender Systems ◽  
Generative Models ◽  
Generative Adversarial Networks ◽  
Excellent Performance ◽  
Adversarial Networks ◽  
Adversarial Examples ◽  
Latent Factor Models ◽  
Recommendation Accuracy ◽  
Applications Of Machine Learning

Latent-factor models (LFM) based on collaborative filtering (CF), such as matrix factorization (MF) and deep CF methods, are widely used in modern recommender systems (RS) due to their excellent performance and recommendation accuracy. However, success has been accompanied with a major new arising challenge: Many applications of machine learning (ML) are adversarial in nature [146]. In recent years, it has been shown that these methods are vulnerable to adversarial examples, i.e., subtle but non-random perturbations designed to force recommendation models to produce erroneous outputs. The goal of this survey is two-fold: (i) to present recent advances on adversarial machine learning (AML) for the security of RS (i.e., attacking and defense recommendation models) and (ii) to show another successful application of AML in generative adversarial networks (GANs) for generative applications, thanks to their ability for learning (high-dimensional) data distributions. In this survey, we provide an exhaustive literature review of 76 articles published in major RS and ML journals and conferences. This review serves as a reference for the RS community working on the security of RS or on generative models using GANs to improve their quality.

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.

Generative Adversarial Networks (GANs)

2021 ◽  
Vol 54 (3) ◽  
pp. 1-42
Author(s):  
Divya Saxena ◽  
Jiannong Cao
Keyword(s):  
Optimization Technique ◽  
Generative Models ◽  
Generative Adversarial Networks ◽  
Network Architectures ◽  
Research Directions ◽  
Research Issues ◽  
Design And Optimization ◽  
Adversarial Networks ◽  
Comprehensive Survey ◽  
Selection Of

Generative Adversarial Networks (GANs) is a novel class of deep generative models that has recently gained significant attention. GANs learn complex and high-dimensional distributions implicitly over images, audio, and data. However, there exist major challenges in training of GANs, i.e., mode collapse, non-convergence, and instability, due to inappropriate design of network architectre, use of objective function, and selection of optimization algorithm. Recently, to address these challenges, several solutions for better design and optimization of GANs have been investigated based on techniques of re-engineered network architectures, new objective functions, and alternative optimization algorithms. To the best of our knowledge, there is no existing survey that has particularly focused on the broad and systematic developments of these solutions. In this study, we perform a comprehensive survey of the advancements in GANs design and optimization solutions proposed to handle GANs challenges. We first identify key research issues within each design and optimization technique and then propose a new taxonomy to structure solutions by key research issues. In accordance with the taxonomy, we provide a detailed discussion on different GANs variants proposed within each solution and their relationships. Finally, based on the insights gained, we present promising research directions in this rapidly growing field.

Machine Learning Data Center Workloads Using Generative Adversarial Networks

2020 ◽  
Vol 48 (2) ◽  
pp. 21-23
Author(s):  
Boudewijn R. Haverkort ◽  
Felix Finkbeiner ◽  
Pieter-Tjerk de Boer
Keyword(s):  
Machine Learning ◽  
Data Center ◽  
Generative Adversarial Networks ◽  
Adversarial Networks ◽  
Learning Data

scholarly journals Conditional Wasserstein Generative Adversarial Networks for Fast Detector Simulation

2021 ◽  
Vol 251 ◽  
pp. 03055
Author(s):  
John Blue ◽  
Braden Kronheim ◽  
Michelle Kuchera ◽  
Raghuram Ramanujan
Keyword(s):  
High Energy Physics ◽  
High Energy ◽  
Generative Models ◽  
Generative Adversarial Networks ◽  
Detector Response ◽  
Event Simulation ◽  
Simulation Process ◽  
Adversarial Networks ◽  
Wide Range ◽  
Detector Simulation

Detector simulation in high energy physics experiments is a key yet computationally expensive step in the event simulation process. There has been much recent interest in using deep generative models as a faster alternative to the full Monte Carlo simulation process in situations in which the utmost accuracy is not necessary. In this work we investigate the use of conditional Wasserstein Generative Adversarial Networks to simulate both hadronization and the detector response to jets. Our model takes the 4-momenta of jets formed from partons post-showering and pre-hadronization as inputs and predicts the 4-momenta of the corresponding reconstructed jet. Our model is trained on fully simulated tt events using the publicly available GEANT-based simulation of the CMS Collaboration. We demonstrate that the model produces accurate conditional reconstructed jet transverse momentum (pT) distributions over a wide range of pT for the input parton jet. Our model takes only a fraction of the time necessary for conventional detector simulation methods, running on a CPU in less than a millisecond per event.

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