Results 51 to 60 of about 9,534,538 (365)

Deep Learning for Person Re-Identification: A Survey and Outlook [PDF]

IEEE Transactions on Pattern Analysis and Machine Intelligence, 2020
Person re-identification (Re-ID) aims at retrieving a person of interest across multiple non-overlapping cameras. With the advancement of deep neural networks and increasing demand of intelligent video surveillance, it has gained significantly increased ...
Mang Ye, Jianbing Shen, Gaojie Lin, T. Xiang, Ling Shao, S. Hoi   +5 more
semanticscholar   +1 more source

Deep learning for graphs

ESANN 2021 proceedings, 2021
Deep learning for graphs encompasses all those neural models endowed with multiple layers of computation operating on data represented as graphs. The most common building blocks of these models are graph encoding layers, which compute a vector embedding for each node in a graph using message-passing operators.
Bacciu, Davide, Bianchi, Filippo Maria, Paassen, Benjamin, Alippi, CesareC   +3 more
openaire   +3 more sources

Impact of flexible noise control (FNC) image processing parameters on portable chest radiography

Journal of Applied Clinical Medical Physics, Volume 23, Issue 12, December 2022., 2022
Abstract There is a lack of understanding in the performance of flexible noise control (FNC) processing, which is used in digital radiography on a scanner vendor and has four parameters each involving multiple options. The aim of this study was to investigate the impact of FNC on portable chest imaging. An anthropomorphic chest phantom was imaged using
Krystal M. Kirby, Liqiang Ren, Timothy R. Daly, Yasmeen K. Tandon, Brian J. Bartholmai, Beth A. Schueler, Zaiyang Long   +6 more
wiley   +1 more source

A Deep Learning Enabled Multi-Class Plant Disease Detection Model Based on Computer Vision

AI, 2021
In this paper, a deep learning enabled object detection model for multi-class plant disease has been proposed based on a state-of-the-art computer vision algorithm. While most existing models are limited to disease detection on a large scale, the current
Arunabha M. Roy, Jayabrata Bhaduri
doaj   +1 more source

Distributed Deep Reinforcement Learning: A Survey and A Multi-Player Multi-Agent Learning Toolbox [PDF]

Machine Intelligence Research, 2024 (https://link.springer.com/article/10.1007/s11633-023-1454-4), 2022
With the breakthrough of AlphaGo, deep reinforcement learning becomes a recognized technique for solving sequential decision-making problems. Despite its reputation, data inefficiency caused by its trial and error learning mechanism makes deep reinforcement learning hard to be practical in a wide range of areas.
arxiv   +1 more source

Building Program Vector Representations for Deep Learning [PDF]

, 2014
Deep learning has made significant breakthroughs in various fields of artificial intelligence. Advantages of deep learning include the ability to capture highly complicated features, weak involvement of human engineering, etc.
Jin, Zhi, Li, Ge, Liu, Yuxuan, Mou, Lili, Peng, Hao, Xu, Yan, Zhang, Lu   +6 more
core   +1 more source

Energy and Policy Considerations for Deep Learning in NLP [PDF]

Annual Meeting of the Association for Computational Linguistics, 2019
Recent progress in hardware and methodology for training neural networks has ushered in a new generation of large networks trained on abundant data. These models have obtained notable gains in accuracy across many NLP tasks.
Emma Strubell, Ananya Ganesh, A. McCallum   +2 more
semanticscholar   +1 more source

Scaling deep learning for materials discovery

Nature, 2023
Novel functional materials enable fundamental breakthroughs across technological applications from clean energy to information processing^ 1 – 11 . From microchips to batteries and photovoltaics, discovery of inorganic crystals has been bottlenecked by ...
Amil Merchant, Simon Batzner, S. Schoenholz, Muratahan Aykol, Gowoon Cheon, E. D. Cubuk   +5 more
semanticscholar   +1 more source

Effect of different noise reduction techniques and template matching parameters on markerless tumor tracking using dual‐energy imaging

Journal of Applied Clinical Medical Physics, Volume 23, Issue 12, December 2022., 2022
Abstract Purpose To evaluate the impact of various noise reduction algorithms and template matching parameters on the accuracy of markerless tumor tracking (MTT) using dual‐energy (DE) imaging. Methods A Varian TrueBeam linear accelerator was used to acquire a series of alternating 60 and 120 kVp images (over a 180° arc) using fast kV switching, on ...
Mandeep Kaur, Peter Wagstaff, Hassan Mostafavi, Mathias Lehmann, Daniel Morf, Liangjia Zhu, Hyejoo Kang, Michal Walczak, Matthew M. Harkenrider, John C. Roeske   +9 more
wiley   +1 more source

Prediction for Manufacturing Factors in a Steel Plate Rolling Smart Factory Using Data Clustering-Based Machine Learning

IEEE Access, 2020
A Steel Plate Rolling Mill (SPM) is a milling machine that uses rollers to press hot slab inputs to produce ferrous or non-ferrous metal plates. To produce high-quality steel plates, it is important to precisely detect and sense values of manufacturing ...
Cheol Young Park, Jin Woog Kim, Bosung Kim, Joongyoon Lee   +3 more
doaj   +1 more source