scholarly journals Sparsity Increases Uncertainty Estimation in Deep Ensemble

Computers ◽  
2021 ◽  
Vol 10 (4) ◽  
pp. 54
Author(s):  
Uyanga Dorjsembe ◽  
Ju Hong Lee ◽  
Bumghi Choi ◽  
Jae Won Song
Keyword(s):  
Artificial Intelligence ◽  
Neural Networks ◽  
Deep Learning ◽  
Energy Efficient ◽  
Deep Neural Networks ◽  
Black Box ◽  
Uncertainty Estimation ◽  
Point Estimation ◽  
On Demand

Deep neural networks have achieved almost human-level results in various tasks and have become popular in the broad artificial intelligence domains. Uncertainty estimation is an on-demand task caused by the black-box point estimation behavior of deep learning. The deep ensemble provides increased accuracy and estimated uncertainty; however, linearly increasing the size makes the deep ensemble unfeasible for memory-intensive tasks. To address this problem, we used model pruning and quantization with a deep ensemble and analyzed the effect in the context of uncertainty metrics. We empirically showed that the ensemble members’ disagreement increases with pruning, making models sparser by zeroing irrelevant parameters. Increased disagreement im-plies increased uncertainty, which helps in making more robust predictions. Accordingly, an energy-efficient compressed deep ensemble is appropriate for memory-intensive and uncertainty-aware tasks.

A Review of Recent Deep Learning Models in COVID-19 Diagnosis

2021 ◽  
Vol 6 (5) ◽  
pp. 10-15
Author(s):  
Ela Bhattacharya ◽  
D. Bhattacharya
Keyword(s):  
Artificial Intelligence ◽  
Neural Networks ◽  
Deep Learning ◽  
Deep Neural Networks ◽  
Test Results ◽  
Learning Models ◽  
Future Directions ◽  
Human Contact ◽  
The World ◽  
Short Span

COVID-19 has emerged as the latest worrisome pandemic, which is reported to have its outbreak in Wuhan, China. The infection spreads by means of human contact, as a result, it has caused massive infections across 200 countries around the world. Artificial intelligence has likewise contributed to managing the COVID-19 pandemic in various aspects within a short span of time. Deep Neural Networks that are explored in this paper have contributed to the detection of COVID-19 from imaging sources. The datasets, pre-processing, segmentation, feature extraction, classification and test results which can be useful for discovering future directions in the domain of automatic diagnosis of the disease, utilizing artificial intelligence-based frameworks, have been investigated in this paper.

scholarly journals Deep learning, past present and future: An odyssey

2021 ◽  
Author(s):  
Anwaar Ulhaq
Keyword(s):  
Artificial Intelligence ◽  
Machine Learning ◽  
Neural Networks ◽  
Deep Learning ◽  
Deep Neural Networks ◽  
Review Paper ◽  
Complex Problems ◽  
Current Trends

Machine learning has grown in popularity and effectiveness over the last decade. It has become possible to solve complex problems, especially in artificial intelligence, due to the effectiveness of deep neural networks. While numerous books and countless papers have been written on deep learning, new researchers want to understand the field's history, current trends and envision future possibilities. This review paper will summarise the recorded work that resulted in such success and address patterns and prospects.

scholarly journals Chatbots Employing Deep Learning for Big Data

2019 ◽  
Vol 8 (11) ◽  
pp. 1005-1010
Keyword(s):  
Artificial Intelligence ◽  
Neural Networks ◽  
Big Data ◽  
Deep Learning ◽  
Natural Language Processing ◽  
Language Processing ◽  
Deep Neural Networks ◽  
Heterogeneous Data ◽  
Instructive Feedback ◽  
Technical Specifications

With the evolution of artificial intelligence to deep learning, the age of perspicacious machines has pioneered that can even mimic as a human. A Conversational software agent is one of the best-suited examples of such intuitive machines which are also commonly known as chatbot actuated with natural language processing. The paper enlisted some existing popular chatbots along with their details, technical specifications, and functionalities. Research shows that most of the customers have experienced penurious service. Also, the inception of meaningful cum instructive feedback endure a demanding and exigent assignment as enactment for chatbots builtout reckon mostly upon templates and hand-written rules. Current chatbot models lack in generating required responses and thus contradict the quality conversation. So involving deep learning amongst these models can overcome this lack and can fill up the paucity with deep neural networks. Some of the deep Neural networks utilized for this till now are Stacked Auto-Encoder, sparse auto-encoders, predictive sparse and denoising auto-encoders. But these DNN are unable to handle big data involving large amounts of heterogeneous data. While Tensor Auto Encoder which overcomes this drawback is time-consuming. This paper has proposed the Chatbot to handle the big data in a manageable time.

scholarly journals Explaining probabilistic Artificial Intelligence (AI) models by discretizing Deep Neural Networks

2021 ◽  
Author(s):  
Rabia Saleem ◽  
Bo Yuan ◽  
Fatih Kurugollu ◽  
Ashiq Anjum
Keyword(s):  
Artificial Intelligence ◽  
Neural Networks ◽  
Decision Making ◽  
Process Model ◽  
Deep Neural Networks ◽  
Computational Cost ◽  
Black Box ◽  
Decision Making Process ◽  
Deterministic Models ◽  
Know How

Artificial Intelligence (AI) models can learn from data and make decisions without any human intervention. However, the deployment of such models is challenging and risky because we do not know how the internal decision- making is happening in these models. Especially, the high-risk decisions such as medical diagnosis or automated navigation demand explainability and verification of the decision making process in AI algorithms. This research paper aims to explain Artificial Intelligence (AI) models by discretizing the black-box process model of deep neural networks using partial differential equations. The PDEs based deterministic models would minimize the time and computational cost of the decision-making process and reduce the chances of uncertainty that make the prediction more trustworthy.

scholarly journals COVID-19 Impact Estimation on ICU Capacity at Andalusia, Spain, Using Artificial Intelligence

2020 ◽  
Author(s):  
F. A. Prieto ◽  
N. G. Baltas ◽  
L. Rios-Pena ◽  
P. Rodriguez
Keyword(s):  
Public Health ◽  
Artificial Intelligence ◽  
Neural Networks ◽  
Deep Learning ◽  
Deep Neural Networks ◽  
Public Hospitals ◽  
Learning Tool ◽  
Infected Population ◽  
Available Resources

Abstract The objective of this article is to evaluate the spread of the virus and estimate the cases of infected population in need of urgent hospitalization, in order to provide sufficient resources to public health. To this end, a deep learning tool based on deep neural networks (DNN) was developed to predict COVID-19 infection and the need for urgent hospitalization in some of the infected patients. We associated the available resources of public hospitals and evaluated the need to increase them after the possible substantial increase caused by SARS-CoV-2 by provinces in the regions of Andalusia, Spain.

scholarly journals Explaining probabilistic Artificial Intelligence (AI) models by discretizing Deep Neural Networks

2021 ◽  
Author(s):  
Rabia Saleem ◽  
Bo Yuan ◽  
Fatih Kurugollu ◽  
Ashiq Anjum
Keyword(s):  
Artificial Intelligence ◽  
Neural Networks ◽  
Decision Making ◽  
Process Model ◽  
Deep Neural Networks ◽  
Computational Cost ◽  
Black Box ◽  
Decision Making Process ◽  
Deterministic Models ◽  
Know How

Artificial Intelligence (AI) models can learn from data and make decisions without any human intervention. However, the deployment of such models is challenging and risky because we do not know how the internal decision- making is happening in these models. Especially, the high-risk decisions such as medical diagnosis or automated navigation demand explainability and verification of the decision making process in AI algorithms. This research paper aims to explain Artificial Intelligence (AI) models by discretizing the black-box process model of deep neural networks using partial differential equations. The PDEs based deterministic models would minimize the time and computational cost of the decision-making process and reduce the chances of uncertainty that make the prediction more trustworthy.

scholarly journals Artificial Intelligence-Based Analytics for Diagnosis of Small Bowel Enteropathies and Black Box Feature Detection

2020 ◽  
Author(s):  
Sana Syed ◽  
Lubaina Ehsan ◽  
Aman Shrivastava ◽  
Saurav Sengupta ◽  
Marium Khan ◽  
...  
Keyword(s):  
Artificial Intelligence ◽  
Neural Networks ◽  
Decision Making ◽  
Image Analysis ◽  
Deep Learning ◽  
Small Bowel ◽  
Classification Accuracy ◽  
Black Box ◽  
Decision Making Process ◽  
Analysis Platform

Objectives: Striking histopathological overlap between distinct but related conditions poses a significant disease diagnostic challenge. There is a major clinical need to develop computational methods enabling clinicians to translate heterogeneous biomedical images into accurate and quantitative diagnostics. This need is particularly salient with small bowel enteropathies; Environmental Enteropathy (EE) and Celiac Disease (CD). We built upon our preliminary analysis by developing an artificial intelligence (AI)-based image analysis platform utilizing deep learning convolutional neural networks (CNNs) for these enteropathies. Methods: Data for secondary analysis was obtained from three primary studies at different sites. The image analysis platform for EE and CD was developed using convolutional neural networks (CNNs: ResNet and custom Shallow CNN). Gradient-weighted Class Activation Mappings (Grad-CAMs) were used to visualize the decision making process of the models. A team of medical experts simultaneously reviewed the stain color normalized images done for bias reduction and Grad-CAM visualizations to confirm structural preservation and biological relevance, respectively. Results: 461 high-resolution biopsy images from 150 children were acquired. Median age (interquartile range) was 37.5 (19.0 to 121.5) months with a roughly equal sex distribution; 77 males (51.3%). ResNet50 and Shallow CNN demonstrated 98% and 96% case-detection accuracy, respectively, which increased to 98.3% with an ensemble. Grad-CAMs demonstrated ability of the models to learn distinct microscopic morphological features. Conclusion: Our AI-based image analysis platform demonstrated high classification accuracy for small bowel enteropathies which was capable of identifying biologically relevant microscopic features, emulating human pathologist decision making process, performing in the case of suboptimal computational environment, and being modified for improving disease classification accuracy. Grad-CAMs that were employed illuminated the otherwise black box of deep learning in medicine, allowing for increased physician confidence in adopting these new technologies in clinical practice.

Deep Learning

2021 ◽  
pp. 171-196
Author(s):  
Semra Erpolat Taşabat ◽  
Olgun Aydin
Keyword(s):  
Artificial Intelligence ◽  
Machine Learning ◽  
Neural Networks ◽  
Deep Learning ◽  
Deep Neural Networks ◽  
Smart Cities ◽  
Key Technology ◽  
Smart Systems ◽  
Advantages And Disadvantages

Deep learning (DL) is a rising star of machine learning (ML) and artificial intelligence (AI) domains. Until 2006, many researchers had attempted to build deep neural networks (DNN), but most of them failed. In 2006, it was proven that deep neural networks are one of the most crucial inventions for the 21st century. Nowadays, DNN are being used as a key technology for many different domains: self-driven vehicles, smart cities, security, automated machines. In this chapter, brief information about DL theory is given, advantages and disadvantages of deep learning are discussed, most used types of DNN are mentioned, popular DL architectures and frameworks are glanced and aimed to build smart systems for the finance and real estate domains. Finally, a case study about image recognition using transfer learning is developed.

scholarly journals Analysis of Application Examples of Differential Privacy in Deep Learning

2021 ◽  
Vol 2021 ◽  
pp. 1-15
Author(s):  
Zhidong Shen ◽  
Ting Zhong
Keyword(s):  
Artificial Intelligence ◽  
Neural Networks ◽  
Deep Learning ◽  
Deep Neural Networks ◽  
Differential Privacy ◽  
Generative Adversarial Networks ◽  
Learning Models ◽  
Privacy Leakage ◽  
Adversarial Networks ◽  
Inference Attacks

Artificial Intelligence has been widely applied today, and the subsequent privacy leakage problems have also been paid attention to. Attacks such as model inference attacks on deep neural networks can easily extract user information from neural networks. Therefore, it is necessary to protect privacy in deep learning. Differential privacy, as a popular topic in privacy-preserving in recent years, which provides rigorous privacy guarantee, can also be used to preserve privacy in deep learning. Although many articles have proposed different methods to combine differential privacy and deep learning, there are no comprehensive papers to analyze and compare the differences and connections between these technologies. For this purpose, this paper is proposed to compare different differential private methods in deep learning. We comparatively analyze and classify several deep learning models under differential privacy. Meanwhile, we also pay attention to the application of differential privacy in Generative Adversarial Networks (GANs), comparing and analyzing these models. Finally, we summarize the application of differential privacy in deep neural networks.

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