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Qualitatively characterizing neural network optimization problems [article]

Ian J. Goodfellow, Oriol Vinyals, Andrew M. Saxe
2015 arXiv   pre-print
Training neural networks involves solving large-scale non-convex optimization problems.  ...  However, modern neural networks are able to achieve negligible training error on complex tasks, using only direct training with stochastic gradient descent.  ...  INTRODUCTION Neural networks are generally regarded as difficult to optimize.  ... 
arXiv:1412.6544v6 fatcat:mmnfbguy2zh3bf3ogkrm4hhxl4

Neural Reduction of Image Data in Order to Determine the Quality of Malting Barley

Piotr Boniecki, Barbara Raba, Agnieszka A. Pilarska, Agnieszka Sujak, Maciej Zaborowicz, Krzysztof Pilarski, Dawid Wojcieszak
2021 Sensors  
A properly conducted learning process of artificial neural network (ANN) allows the classification of new, unknown data, which helps to increase the efficiency of the generated models in practice.  ...  Currently, a qualitative assessment of kernels is carried by malthouse-certified employees acting as experts.  ...  in neural analysis based on discrete optimization methods [19, 20] .  ... 
doi:10.3390/s21175696 pmid:34502597 fatcat:hycdy27s3fdzjpcexm7kenwxwy

Road Detection with EOSResUNet and Post Vectorizing Algorithm

Oleksandr Filin, Anton Zapara, Serhii Panchenko
2018 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW)  
In this article, we want to present you the combination of work of neural network and postprocessing algorithm, due to which we get not only the coverage mask but also the vectors of all of the individual  ...  Object recognition on the satellite images is one of the most relevant and popular topics in the problem of pattern recognition.  ...  This causes a large number of problems and significantly worsens the result of the work of the neural network.  ... 
doi:10.1109/cvprw.2018.00036 dblp:conf/cvpr/FilinZP18 fatcat:23lfaqzzlvcstdg5owjqp5eyve

Neural networks

Michael I. Jordan, Christopher M. Bishop
1996 ACM Computing Surveys  
Real-world problems are often characterized by complexities such as missing data, mixtures of qualitative and quantitative variables, regimes of qualitatively different functional relationships, and highly  ...  to this problem is again based on neural networks.  ... 
doi:10.1145/234313.234348 fatcat:ogdd64dnbbfqlez2h57lkryafy

Page 1209 of Mathematical Reviews Vol. , Issue 97B [page]

1997 Mathematical Reviews  
1209 the optimal variable ordering for a given obdd and the optimal linear arrangement problem on graphs.”  ...  Summary: “A qualitative analysis is developed for continuous-time neural networks subjected to random pure structural variations.  ... 

Prediction of Ore Quantity Based on GA-BP Neural Network

Li Guo, Qiong Wu, Qinghua Gu
2017 Geo-Resources Environment and Engineering  
In order to obtain the global optimal solution, and to improve the defects of BP neural network, this paper proposes combination optimization algorithm of genetic algorithm (GA) and BP neural network to  ...  On this basis, the GA-BP neural network model is constructed and applied to optimize the initial weights and threshold value of BP neural network.  ...  The GA-BP neural network algorithm Geological phenomena are often characterized by some qualitative features, such as the structure environment, formation conditions, rock properties and rock-magma system  ... 
doi:10.15273/gree.2017.02.015 fatcat:jwfxlot3tvdwrp3ucrd3cx4ura

DEVELOPMENT OF A METHODOLOGY FOR FORMALIZING THE INVESTMENT DECISION-MAKING PROCESS BASED ON THE HOPFIELD NEURAL NETWORK

Olga RUZAKOVA
2019 "EСONOMY. FINANСES. MANAGEMENT: Topical issues of science and practical activity"  
with minimal money and time expenses – one of the standards of the Hopfield network, which is most similar to the one that characterizes the activity of the enterprise.  ...  The article presents a methodological approach to assessing the investment attractiveness of an enterprise based on the Hopfield neural network mathematical apparatus.  ...  Intellectual economic systems based on artificial neural networks can successfully solve problems of classification of economic objects, optimization of associative memory and management of business entities  ... 
doi:10.37128/2411-4413-2019-6-7 fatcat:hqbid3ucdrg5hbcnpkyjljy3b4

Stuck in a What? Adventures in Weight Space [article]

Zachary C. Lipton
2016 arXiv   pre-print
In this paper, we build on recent work empirically characterizing the error surfaces of neural networks.  ...  As neural networks are typically over-complete, it's easy to show the existence of vast continuous regions through weight space with equal loss.  ...  INTRODUCTION In the worst case, solving for the optimal weights in a neural network is an NP-Hard problem.  ... 
arXiv:1602.07320v1 fatcat:vl552ou4mba5jcsyn7htdzhtj4

Page 1402 of Mathematical Reviews Vol. , Issue 99b [page]

1991 Mathematical Reviews  
The postulates are shown to lead to a characterization of generalized qualitative probability that includes and blends both traditional qualitative probability  ...  (vii) Papers on neural networks (Fogel et al., and Michel): Fogel et al. deal with the problem of detecting breast cancer from radiographic features and patient age.  ... 

CIS Publication Spotlight [Publication Spotlight]

Haibo He, Jon Garibaldi, Kay Chen Tan, Julian Togelius, Yaochu Jin, Yew Soon Ong
2020 IEEE Computational Intelligence Magazine  
The volume, veracity, variability, and velocity of data produced from the ever increasing network of sensors connected to Internet pose challenges for power management, scalability, and sustainability  ...  We discuss why the neuromorphic architectures are useful for edge devices and show the advantages, drawbacks, and open problems in the field of neuromemristive circuits for edge computing."  ...  Selection and Optimization of Temporal Spike Encoding Methods for Spiking Neural Networks, by B. Petro, N. Kasabov and R. M.  ... 
doi:10.1109/mci.2020.2998230 fatcat:d7fztoslljfr3am4fwvaafbnfq

Graph Neural Networks for Graph Drawing [article]

Matteo Tiezzi, Gabriele Ciravegna, Marco Gori
2022 arXiv   pre-print
GND are Graph Neural Networks (GNNs) whose learning process can be driven by any provided loss function, such as the ones commonly employed in Graph Drawing.  ...  Moreover, we prove that this mechanism can be guided by loss functions computed by means of Feedforward Neural Networks, on the basis of supervision hints that express beauty properties, like the minimization  ...  by a neural network, a Neural Aesthete, even when the required aesthetic criteria cannot be directly optimized.  ... 
arXiv:2109.10061v2 fatcat:lu4he24ppbd3lj2wtrz46tdbma

Research on Credit Evaluation of Financial Enterprises Based on the Genetic Backpropagation Neural Network

Hua Peng, Mian Ahmad Jan
2021 Scientific Programming  
The potential problems of the backpropagation (BP) neural network with slothful speed of convergence and the possibility of falling into the local minimum point are solved to a convinced degree using the  ...  The hybrid technique of the genetic BP neural network is applied to a credit rating system.  ...  number is characterized by continuous parameter optimization, therefore, the steps of coding and decoding are omitted.  ... 
doi:10.1155/2021/7745920 fatcat:crycn7xyn5bzvlebogaizlyenu

Solution of matrix Riccati differential equation for nonlinear singular system using neural networks

J. Abdul Samath, N. Selvaraju
2010 International Journal of Computer Applications  
Accuracy of the neural solution to the problem is qualitatively better.  ...  The goal is to provide optimal control with reduced calculus effort by comparing the solutions of the MRDE obtained from well known traditional Runge Kutta(RK)method and nontraditional neural network method  ...  The theory of the quadratic cost control problem has been treated as a more interesting problem and the optimal feedback with minimum cost control has been characterized by the solution of a Riccati equation  ... 
doi:10.5120/575-181 fatcat:yv4tahm23vgfpgkzu25evaijyi

Neural Network Based Enveloping Model of Agricultural Tyre

Stojic B.
2019 International Journal of Engineering and Management Sciences  
Based on experimental results, neural network based model of tyre enveloping behaviour was developed.  ...  The vibration properties of agricultural tractor's tyres significantly influence its response in terms of the exposure of the human operator to mechanical vibrations, which is still one of the key problems  ...  Using these criteria, final choice of optimal neural network was made.  ... 
doi:10.21791/ijems.2019.1.27. fatcat:gq2hid4wwrbt5bmvmalh6fmlfa

Page 2039 of Mathematical Reviews Vol. , Issue 97C [page]

1997 Mathematical Reviews  
In this paper, a hybrid neural network is presented which combines, for the first time, a new self-organizing approach to optimization with a Hopfield network.  ...  Summary: “Both the Hopfield neural network and Kohonen’s principles of self-organization have been used to solve difficult optimization problems, with varying degrees of success.  ... 
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