scholarly journals Comparing the Visual Representations and Performance of Humans and Deep Neural Networks

2018 ◽  
Vol 28 (1) ◽  
pp. 34-39 ◽  
Author(s):  
Robert A. Jacobs ◽  
Christopher J. Bates
Keyword(s):  
Neural Networks ◽  
Visual Cues ◽  
Deep Neural Networks ◽  
Three Dimensional ◽  
Visual Representations ◽  
Dimensional Structure ◽  
Processing Strategies ◽  
Psychological Models ◽  
Statistical Regularities ◽  
And Performance

Although deep neural networks (DNNs) are state-of-the-art artificial intelligence systems, it is unclear what insights, if any, they provide about human intelligence. We address this issue in the domain of visual perception. After briefly describing DNNs, we provide an overview of recent results comparing human visual representations and performance with those of DNNs. In many cases, DNNs acquire visual representations and processing strategies that are very different from those used by people. We conjecture that there are at least two factors preventing them from serving as better psychological models. First, DNNs are currently trained with impoverished data, such as data lacking important visual cues to three-dimensional structure, data lacking multisensory statistical regularities, and data in which stimuli are unconnected to an observer’s actions and goals. Second, DNNs typically lack adaptations to capacity limits, such as attentional mechanisms, visual working memory, and compressed mental representations biased toward preserving task-relevant abstractions.

scholarly journals Mobility-Included DNN Partition Offloading from Mobile Devices to Edge Clouds

Sensors ◽  
2021 ◽  
Vol 21 (1) ◽  
pp. 229
Author(s):  
Xianzhong Tian ◽  
Juan Zhu ◽  
Ting Xu ◽  
Yanjun Li
Keyword(s):  
Neural Networks ◽  
Energy Consumption ◽  
Mobile Devices ◽  
Wireless Network ◽  
Deep Neural Networks ◽  
Mobile User ◽  
Computation Offloading ◽  
Long Latency ◽  
Total Latency ◽  
And Performance

The latest results in Deep Neural Networks (DNNs) have greatly improved the accuracy and performance of a variety of intelligent applications. However, running such computation-intensive DNN-based applications on resource-constrained mobile devices definitely leads to long latency and huge energy consumption. The traditional way is performing DNNs in the central cloud, but it requires significant amounts of data to be transferred to the cloud over the wireless network and also results in long latency. To solve this problem, offloading partial DNN computation to edge clouds has been proposed, to realize the collaborative execution between mobile devices and edge clouds. In addition, the mobility of mobile devices is easily to cause the computation offloading failure. In this paper, we develop a mobility-included DNN partition offloading algorithm (MDPO) to adapt to user’s mobility. The objective of MDPO is minimizing the total latency of completing a DNN job when the mobile user is moving. The MDPO algorithm is suitable for both DNNs with chain topology and graphic topology. We evaluate the performance of our proposed MDPO compared to local-only execution and edge-only execution, experiments show that MDPO significantly reduces the total latency and improves the performance of DNN, and MDPO can adjust well to different network conditions.

scholarly journals Three‐dimensional dose prediction for lung IMRT patients with deep neural networks: robust learning from heterogeneous beam configurations

2019 ◽  
Vol 46 (8) ◽  
pp. 3679-3691 ◽  
Author(s):  
Ana María Barragán‐Montero ◽  
Dan Nguyen ◽  
Weiguo Lu ◽  
Mu-Han Lin ◽  
Roya Norouzi‐Kandalan ◽  
...  
Keyword(s):  
Neural Networks ◽  
Deep Neural Networks ◽  
Three Dimensional ◽  
Robust Learning ◽  
Dose Prediction

Exploring the conformations of nucleic acids

1995 ◽  
Vol 5 (3) ◽  
pp. 443-460 ◽  
Author(s):  
Marcel Turcotte ◽  
Guy Lapalme ◽  
François Major
Keyword(s):  
Nucleic Acids ◽  
Functional Programming ◽  
Three Dimensional ◽  
Performance Criteria ◽  
Dimensional Structure ◽  
Pharmaceutical Companies ◽  
Experimental Development ◽  
Macromolecular Conformation ◽  
And Performance ◽  
Research Centres

AbstractThis paper presents an application of functional programming in the field of molecular biology: exploring the conformations of nucleic acids. TheNucleic Acid three-dimensional structure determination problem(NA3D) and a constraint satisfaction algorithm are formally described. Prototyping and experimental development using the Miranda functional programming language, over the last four years, are discussed. A Prolog implementation has been developed to evaluate software engineering and performance criteria between functional and logic programming. A C++ implementation has been developed for distribution purpose and to solve large practical problems. This system, called MC-SYM for ‘Macromolecular Conformation by SYMbolic generation’, is used in more than 50 laboratories, including academic and government research centres and pharmaceutical companies.

scholarly journals SNR: S queezing N umerical R ange Defuses Bit Error Vulnerability Surface in Deep Neural Networks

2021 ◽  
Vol 20 (5s) ◽  
pp. 1-25
Author(s):  
Elbruz Ozen ◽  
Alex Orailoglu
Keyword(s):  
Neural Networks ◽  
Deep Learning ◽  
Deep Neural Networks ◽  
Low Cost ◽  
Error Resilience ◽  
Error Rates ◽  
Training Methods ◽  
Performance Requirements ◽  
Proper Design ◽  
And Performance

As deep learning algorithms are widely adopted, an increasing number of them are positioned in embedded application domains with strict reliability constraints. The expenditure of significant resources to satisfy performance requirements in deep neural network accelerators has thinned out the margins for delivering safety in embedded deep learning applications, thus precluding the adoption of conventional fault tolerance methods. The potential of exploiting the inherent resilience characteristics of deep neural networks remains though unexplored, offering a promising low-cost path towards safety in embedded deep learning applications. This work demonstrates the possibility of such exploitation by juxtaposing the reduction of the vulnerability surface through the proper design of the quantization schemes with shaping the parameter distributions at each layer through the guidance offered by appropriate training methods, thus delivering deep neural networks of high resilience merely through algorithmic modifications. Unequaled error resilience characteristics can be thus injected into safety-critical deep learning applications to tolerate bit error rates of up to at absolutely zero hardware, energy, and performance costs while improving the error-free model accuracy even further.

scholarly journals How Many Features is an Image Worth? Multi-Channel CNN for Steering Angle Prediction in Autonomous Vehicles

2021 ◽  
Author(s):  
Jason Munger ◽  
Carlos W. Morato
Keyword(s):  
Neural Networks ◽  
Autonomous Vehicles ◽  
Deep Neural Networks ◽  
Spatial Information ◽  
Image Data ◽  
Input Image ◽  
Steering Angle ◽  
Rgb Images ◽  
And Performance ◽  
The Individual

This project explores how raw image data obtained from AV cameras can provide a model with more spatial information than can be learned from simple RGB images alone. This paper leverages the advances of deep neural networks to demonstrate steering angle predictions of autonomous vehicles through an end-to-end multi-channel CNN model using only the image data provided from an onboard camera. Image data is processed through existing neural networks to provide pixel segmentation and depth estimates and input to a new neural network along with the raw input image to provide enhanced feature signals from the environment. Various input combinations of Multi-Channel CNNs are evaluated, and their effectiveness is compared to single CNN networks using the individual data inputs. The model with the most accurate steering predictions is identified and performance compared to previous neural networks.

scholarly journals Mapping human visual representations by deep neural networks

2016 ◽  
Vol 16 (12) ◽  
pp. 373 ◽  
Author(s):  
Kandan Ramakrishnan ◽  
H. Steven Scholte ◽  
Arnold Smeulders ◽  
Sennay Ghebreab
Keyword(s):  
Neural Networks ◽  
Deep Neural Networks ◽  
Visual Representations

Bridging Finite Element and Machine Learning Modeling: Stress Prediction of Arterial Walls in Atherosclerosis

2019 ◽  
Vol 141 (8) ◽  
Author(s):  
Ali Madani ◽  
Ahmed Bakhaty ◽  
Jiwon Kim ◽  
Yara Mubarak ◽  
Mohammad R. K. Mofrad
Keyword(s):  
Machine Learning ◽  
Neural Networks ◽  
Finite Element ◽  
Deep Neural Networks ◽  
Prediction Models ◽  
Plaque Rupture ◽  
Training Data ◽  
Von Mises Stress ◽  
And Performance ◽  
Von Mises

Finite element and machine learning modeling are two predictive paradigms that have rarely been bridged. In this study, we develop a parametric model to generate arterial geometries and accumulate a database of 12,172 2D finite element simulations modeling the hyperelastic behavior and resulting stress distribution. The arterial wall composition mimics vessels in atherosclerosis–a complex cardiovascular disease and one of the leading causes of death globally. We formulate the training data to predict the maximum von Mises stress, which could indicate risk of plaque rupture. Trained deep learning models are able to accurately predict the max von Mises stress within 9.86% error on a held-out test set. The deep neural networks outperform alternative prediction models and performance scales with amount of training data. Lastly, we examine the importance of contributing features on stress value and location prediction to gain intuitions on the underlying process. Moreover, deep neural networks can capture the functional mapping described by the finite element method, which has far-reaching implications for real-time and multiscale prediction tasks in biomechanics.

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