Results 31 to 40 of about 1,305,191 (158)
Comparative evaluation of CNN architectures for Image Caption Generation [PDF]
in International Journal of Advanced Computer Science and
Applications, 11(12), 2020, 2021 Aided by recent advances in Deep Learning, Image Caption Generation has seen tremendous progress over the last few years. Most methods use transfer learning to extract visual information, in the form of image features, with the help of pre-trained Convolutional Neural Network models followed by transformation of the visual information using a Caption ...
arxiv +1 more source
Brain Network Analysis and Classification Based on Convolutional Neural Network
Frontiers in Computational Neuroscience, 2018 Background: Convolution neural networks (CNN) is increasingly used in computer science and finds more and more applications in different fields. However, analyzing brain network with CNN is not trivial, due to the non-Euclidean characteristics of brain ...
Lu Meng, Jing Xiang
doaj +1 more source
DeepLab: Semantic Image Segmentation with Deep Convolutional Nets, Atrous Convolution, and Fully Connected CRFs [PDF]
, 2017 In this work we address the task of semantic image segmentation with Deep Learning and make three main contributions that are experimentally shown to have substantial practical merit.
Chen, Liang-Chieh +4 more
core +2 more sources
Designing a general library for convolutions [PDF]
arXiv, 2022 We will discuss our experiences and design decisions obtained from building a formal library for the convolution of two functions. Convolution is a fundamental concept with applications throughout mathematics. We will focus on the design decisions we made to make the convolution general and easy to use, and the incorporation of this development in Lean'
arxiv
Convolution and Concurrency [PDF]
Mathematical Structures in Computer Science, 2020 We show how concurrent quantales and concurrent Kleene algebras arise as convolution algebras $Q^X$ of functions from structures $X$ with two ternary relations that satisfy relational interchange laws into concurrent quantales or Kleene algebras $Q$. The
James Cranch, Simon Doherty, G. Struth
semanticscholar +1 more source
Experimentally Realizing Convolution Processing in the Photonic Synthetic Frequency Dimension [PDF]
arXiv, 2023 Convolution is an essential operation in signal and image processing and consumes most of the computing power in convolutional neural networks. Photonic convolution has the promise of addressing computational bottlenecks and outperforming electronic implementations.
arxiv
Relating brain structure images to personality characteristics using 3D convolution neural network
CAAI Transactions on Intelligence Technology, 2021 Zhuhai Laboratory of Key Laboratory of Symbol Computation and Knowledge Engineering of Ministry of Education, Zhuhai College of Jilin University, Zhuhai, China Key Laboratory of Symbol Computation and Knowledge Engineering of Ministry of Education ...
Lixian Cao +6 more
semanticscholar +1 more source
Convolution Algebras: Relational Convolution, Generalised Modalities and Incidence Algebras [PDF]
Logical Methods in Computer Science, Volume 17, Issue 1 (February
9, 2021) lmcs:3769, 2017 Convolution is a ubiquitous operation in mathematics and computing. The Kripke semantics for substructural and interval logics motivates its study for quantale-valued functions relative to ternary relations. The resulting notion of relational convolution leads to generalised binary and unary modal operators for qualitative and quantitative models, and ...
arxiv +1 more source
Research on OpenCL optimization for FPGA deep learning application.
PLoS ONE, 2019 In recent years, with the development of computer science, deep learning is held as competent enough to solve the problem of inference and learning in high dimensional space.
Shuo Zhang +4 more
doaj +1 more source
Investigating Machine Learning Techniques for Gesture Recognition with Low-Cost Capacitive Sensing Arrays [PDF]
, 2020 Machine learning has proven to be an effective tool for forming models to make predictions based on sample data. Supervised learning, a subset of machine learning, can be used to map input data to output labels based on pre-existing paired data. Datasets
Fahr Jr., Michael
core +2 more sources