scholarly journals Model Free Localization with Deep Neural Architectures by Means of an Underwater WSN

Sensors ◽  
2019 ◽  
Vol 19 (16) ◽  
pp. 3530
Author(s):  
Juan Parras ◽  
Santiago Zazo ◽  
Iván A. Pérez-Álvarez ◽  
José Luis Sanz González
Keyword(s):  
Neural Networks ◽  
Deep Neural Networks ◽  
Acoustic Signals ◽  
Model Free ◽  
Underwater Environment ◽  
The Neural Network ◽  
Anchor Nodes ◽  
Localization Precision ◽  
Significant Effort ◽  
Localization Systems

In recent years, there has been a significant effort towards developing localization systems in the underwater medium, with current methods relying on anchor nodes, explicitly modeling the underwater channel or cooperation from the target. Lately, there has also been some work on using the approximation capabilities of Deep Neural Networks in order to address this problem. In this work, we study how the localization precision of using Deep Neural Networks is affected by the variability of the channel, the noise level at the receiver, the number of neurons of the neural network and the utilization of the power or the covariance of the received acoustic signals. Our study shows that using deep neural networks is a valid approach when the channel variability is low, which opens the door to further research in such localization methods for the underwater environment.

scholarly journals Deep Neural Networks Constrained by Decision Rules

2019 ◽  
Vol 33 ◽  
pp. 2496-2505
Author(s):  
Yuzuru Okajima ◽  
Kunihiko Sadamasa
Keyword(s):  
Neural Networks ◽  
Deep Neural Networks ◽  
Predictive Accuracy ◽  
Decision Rules ◽  
Hybrid Technique ◽  
Complex Data ◽  
Rule Based ◽  
Prior Probabilities ◽  
The Neural Network ◽  
Latent Representations

Deep neural networks achieve high predictive accuracy by learning latent representations of complex data. However, the reasoning behind their decisions is difficult for humans to understand. On the other hand, rule-based approaches are able to justify the decisions by showing the decision rules leading to them, but they have relatively low accuracy. To improve the interpretability of neural networks, several techniques provide post-hoc explanations of decisions made by neural networks, but they cannot guarantee that the decisions are always explained in a simple form like decision rules because their explanations are generated after the decisions are made by neural networks.In this paper, to balance the accuracy of neural networks and the interpretability of decision rules, we propose a hybrid technique called rule-constrained networks, namely, neural networks that make decisions by selecting decision rules from a given ruleset. Because the networks are forced to make decisions based on decision rules, it is guaranteed that every decision is supported by a decision rule. Furthermore, we propose a technique to jointly optimize the neural network and the ruleset from which the network select rules. The log likelihood of correct classifications is maximized under a model with hyper parameters about the ruleset size and the prior probabilities of rules being selected. This feature makes it possible to limit the ruleset size or prioritize human-made rules over automatically acquired rules for promoting the interpretability of the output. Experiments on datasets of time-series and sentiment classification showed rule-constrained networks achieved accuracy as high as that achieved by original neural networks and significantly higher than that achieved by existing rule-based models, while presenting decision rules supporting the decisions.

OPTIMIZATION PROCESS ANALYSIS FOR HYPERPARAMETERS OF NEURAL NETWORK DATA PROCESSING STRUCTURES

2020 ◽  
pp. 3-10
Author(s):  
V. N. Gridin ◽  
I. A. Evdokimov ◽  
B. R. Salem ◽  
V. I. Solodovnikov
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Deep Neural Networks ◽  
Process Analysis ◽  
Validation Process ◽  
The Neural Network ◽  
Source Data ◽  
The Neural Networks ◽  
Qualitative Characteristics ◽  
Setting Parameters

The analysis of key stages, implementation features and functioning principles of the neural networks, including deep neural networks, has been carried out. The problems of choosing the number of hidden elements, methods for the internal topology selection and setting parameters are considered. It is shown that in the training and validation process it is possible to control the capacity of a neural network and evaluate the qualitative characteristics of the constructed model. The issues of construction processes automation and hyperparameters optimization of the neural network structures are considered depending on the user's tasks and the available source data. A number of approaches based on the use of probabilistic programming, evolutionary algorithms, and recurrent neural networks are presented.

scholarly journals Graph Universal Adversarial Attacks: A Few Bad Actors Ruin Graph Learning Models

2021 ◽  
Author(s):  
Xiao Zang ◽  
Yi Xie ◽  
Jie Chen ◽  
Bo Yuan
Keyword(s):  
Neural Networks ◽  
Success Rate ◽  
Deep Neural Networks ◽  
Model Performance ◽  
Graph Model ◽  
Structured Data ◽  
Graph Structure ◽  
Learning Models ◽  
Security Threat ◽  
Anchor Nodes

Deep neural networks, while generalize well, are known to be sensitive to small adversarial perturbations. This phenomenon poses severe security threat and calls for in-depth investigation of the robustness of deep learning models. With the emergence of neural networks for graph structured data, similar investigations are urged to understand their robustness. It has been found that adversarially perturbing the graph structure and/or node features may result in a significant degradation of the model performance. In this work, we show from a different angle that such fragility similarly occurs if the graph contains a few bad-actor nodes, which compromise a trained graph neural network through flipping the connections to any targeted victim. Worse, the bad actors found for one graph model severely compromise other models as well. We call the bad actors ``anchor nodes'' and propose an algorithm, named GUA, to identify them. Thorough empirical investigations suggest an interesting finding that the anchor nodes often belong to the same class; and they also corroborate the intuitive trade-off between the number of anchor nodes and the attack success rate. For the dataset Cora which contains 2708 nodes, as few as six anchor nodes will result in an attack success rate higher than 80% for GCN and other three models.

scholarly journals Bias in Deep Neural Networks in Land Use Characterization for International Development

2021 ◽  
Vol 13 (15) ◽  
pp. 2908
Author(s):  
Do-Hyung Kim ◽  
Guzmán López ◽  
Diego Kiedanski ◽  
Iyke Maduako ◽  
Braulio Ríos ◽  
...  
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Land Use ◽  
International Development ◽  
Deep Neural Networks ◽  
Satellite Image ◽  
Fine Tuning ◽  
Training Dataset ◽  
Model Accuracy ◽  
The Neural Network

Understanding the biases in Deep Neural Networks (DNN) based algorithms is gaining paramount importance due to its increased applications on many real-world problems. A known problem of DNN penalizing the underrepresented population could undermine the efficacy of development projects dependent on data produced using DNN-based models. In spite of this, the problems of biases in DNN for Land Use and Land Cover Classification (LULCC) have not been a subject of many studies. In this study, we explore ways to quantify biases in DNN for land use with an example of identifying school buildings in Colombia from satellite imagery. We implement a DNN-based model by fine-tuning an existing, pre-trained model for school building identification. The model achieved overall 84% accuracy. Then, we used socioeconomic covariates to analyze possible biases in the learned representation. The retrained deep neural network was used to extract visual features (embeddings) from satellite image tiles. The embeddings were clustered into four subtypes of schools, and the accuracy of the neural network model was assessed for each cluster. The distributions of various socioeconomic covariates by clusters were analyzed to identify the links between the model accuracy and the aforementioned covariates. Our results indicate that the model accuracy is lowest (57%) where the characteristics of the landscape are predominantly related to poverty and remoteness, which confirms our original assumption on the heterogeneous performances of Artificial Intelligence (AI) algorithms and their biases. Based on our findings, we identify possible sources of bias and present suggestions on how to prepare a balanced training dataset that would result in less biased AI algorithms. The framework used in our study to better understand biases in DNN models would be useful when Machine Learning (ML) techniques are adopted in lieu of ground-based data collection for international development programs. Because such programs aim to solve issues of social inequality, MLs are only applicable when they are transparent and accountable.

scholarly journals Segmentation of Preretinal Space in Optical Coherence Tomography Images Using Deep Neural Networks

Sensors ◽  
2021 ◽  
Vol 21 (22) ◽  
pp. 7521
Author(s):  
Agnieszka Stankiewicz ◽  
Tomasz Marciniak ◽  
Adam Dabrowski ◽  
Marcin Stopa ◽  
Elzbieta Marciniak ◽  
...  
Keyword(s):  
Neural Networks ◽  
Optical Coherence Tomography ◽  
Deep Neural Networks ◽  
Three Dimensional ◽  
Semantic Segmentation ◽  
Optical Coherence ◽  
The Neural Network ◽  
Inner Limiting Membrane ◽  
Segmentation Quality ◽  
Oct Imaging

This paper proposes an efficient segmentation of the preretinal area between the inner limiting membrane (ILM) and posterior cortical vitreous (PCV) of the human eye in an image obtained with the use of optical coherence tomography (OCT). The research was carried out using a database of three-dimensional OCT imaging scans obtained with the Optovue RTVue XR Avanti device. Various types of neural networks (UNet, Attention UNet, ReLayNet, LFUNet) were tested for semantic segmentation, their effectiveness was assessed using the Dice coefficient and compared to the graph theory techniques. Improvement in segmentation efficiency was achieved through the use of relative distance maps. We also show that selecting a larger kernel size for convolutional layers can improve segmentation quality depending on the neural network model. In the case of PVC, we obtain the effectiveness reaching up to 96.35%. The proposed solution can be widely used to diagnose vitreomacular traction changes, which is not yet available in scientific or commercial OCT imaging solutions.

scholarly journals Exploring deep neural networks via layer-peeled model: Minority collapse in imbalanced training

2021 ◽  
Vol 118 (43) ◽  
pp. e2103091118
Author(s):  
Cong Fang ◽  
Hangfeng He ◽  
Qi Long ◽  
Weijie J. Su
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Deep Learning ◽  
Deep Neural Networks ◽  
Model Minority ◽  
Tight Frame ◽  
Learning Models ◽  
The Neural Network ◽  
Long Time ◽  
Topmost Layer

In this paper, we introduce the Layer-Peeled Model, a nonconvex, yet analytically tractable, optimization program, in a quest to better understand deep neural networks that are trained for a sufficiently long time. As the name suggests, this model is derived by isolating the topmost layer from the remainder of the neural network, followed by imposing certain constraints separately on the two parts of the network. We demonstrate that the Layer-Peeled Model, albeit simple, inherits many characteristics of well-trained neural networks, thereby offering an effective tool for explaining and predicting common empirical patterns of deep-learning training. First, when working on class-balanced datasets, we prove that any solution to this model forms a simplex equiangular tight frame, which, in part, explains the recently discovered phenomenon of neural collapse [V. Papyan, X. Y. Han, D. L. Donoho, Proc. Natl. Acad. Sci. U.S.A. 117, 24652–24663 (2020)]. More importantly, when moving to the imbalanced case, our analysis of the Layer-Peeled Model reveals a hitherto-unknown phenomenon that we term Minority Collapse, which fundamentally limits the performance of deep-learning models on the minority classes. In addition, we use the Layer-Peeled Model to gain insights into how to mitigate Minority Collapse. Interestingly, this phenomenon is first predicted by the Layer-Peeled Model before being confirmed by our computational experiments.

scholarly journals Construction of a neural network energy function for protein physics

2021 ◽  
Author(s):  
Huan Yang ◽  
Zhaoping Xiong ◽  
Francesco Zonta
Keyword(s):  
Neural Network ◽  
Experimental Data ◽  
Neural Networks ◽  
Energy Function ◽  
Protein Sequence ◽  
Deep Neural Networks ◽  
Protein Structures ◽  
Learning Method ◽  
Local Structures ◽  
The Neural Network

AbstractClassical potentials are widely used to describe protein physics, due to their simplicity and accuracy, but they are continuously challenged as real applications become more demanding with time. Deep neural networks could help generating alternative ways of describing protein physics. Here we propose an unsupervised learning method to derive a neural network energy function for proteins. The energy function is a probability density model learned from plenty of 3D local structures which have been extensively explored by evolution. We tested this model on a few applications (assessment of protein structures, protein dynamics and protein sequence design), showing that the neural network can correctly recognize patterns in protein structures. In other words, the neural network learned some aspects of protein physics from experimental data.

Domain Randomization Using Deep Neural Networks for Estimating Positions of Bolts

2020 ◽  
Vol 20 (5) ◽  
Author(s):  
Ezra Ameperosa ◽  
Pranav A. Bhounsule
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Convolutional Neural Network ◽  
Deep Neural Networks ◽  
Single Object ◽  
Automated Method ◽  
Bounding Box ◽  
The Neural Network ◽  
Camera Position ◽  
Position And Orientation

Abstract Current manual practices of replacing bolts on structures are time-consuming and costly, especially because of numerous bolts. Thus, an automated method that can visually detect and localize bolt positions would be highly beneficial. We demonstrate the use of deep neural networks using domain randomization for detecting and localizing bolts on a workpiece. In contrast to previous approaches that require training on real images, the use of domain randomization enables all training in simulation. The key idea is to create a wide variety of computer-generated synthetic images by varying the texture, color, camera position and orientation, distractor objects, and noise, and train the neural network on these images such that the neural network is robust to scene variability and hence provides accurate results when deployed on real images. Using domain randomization, we train two neural networks, a faster regional convolutional neural network for detecting the bolt and placing a bounding box, and a regression convolutional neural network for estimating the x- and y-position of the bolts relative to the coordinates fixed to the workpiece. Our results indicate that in the best case, we can detect bolts with 85% accuracy and can predict 75% of bolts within 1.27 cm accuracy. The novelty of this work is in using domain randomization to detect and localize: (1) multiples of a single object and (2) small-sized objects (0.6 cm × 2.5 cm).

scholarly journals Mutual Information Based Learning Rate Decay for Stochastic Gradient Descent Training of Deep Neural Networks

Entropy ◽  
2020 ◽  
Vol 22 (5) ◽  
pp. 560
Author(s):  
Shrihari Vasudevan
Keyword(s):  
Neural Networks ◽  
Mutual Information ◽  
Gradient Descent ◽  
Deep Neural Networks ◽  
Learning Rate ◽  
Stochastic Gradient ◽  
Stochastic Gradient Descent ◽  
Novel Approach ◽  
The Neural Network ◽  
Gradient Based

This paper demonstrates a novel approach to training deep neural networks using a Mutual Information (MI)-driven, decaying Learning Rate (LR), Stochastic Gradient Descent (SGD) algorithm. MI between the output of the neural network and true outcomes is used to adaptively set the LR for the network, in every epoch of the training cycle. This idea is extended to layer-wise setting of LR, as MI naturally provides a layer-wise performance metric. A LR range test determining the operating LR range is also proposed. Experiments compared this approach with popular alternatives such as gradient-based adaptive LR algorithms like Adam, RMSprop, and LARS. Competitive to better accuracy outcomes obtained in competitive to better time, demonstrate the feasibility of the metric and approach.

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