Results 61 to 70 of about 52,973 (279)
Analysis of comparative performance of deep learning models for sentiment analysis
Proceedings of the International Florida Artificial Intelligence Research Society Conference, 2021 Sentiment analysis of text can be performed using machine learning and natural language processing methods. However, there is no single tool or method that is effective in all cases.
Mirza Murtaza
doaj +1 more source
Deep Residual Learning for Image Recognition
, 2015 Deeper neural networks are more difficult to train. We present a residual learning framework to ease the training of networks that are substantially deeper than those used previously.
He, Kaiming +3 more
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Advanced Functional Materials, EarlyView.ABSTRACT Traditional wearable exoskeletons rely on rigid structures, which limit comfort, flexibility, and everyday usability. This work introduces the fundamental technologies to create the first soft, lightweight, intelligent textile‐based exoskeletons (Texoskeletons) built using 1D sensors and actuators.
Amy Lukomiak +19 more
wiley +1 more source
Rocket Launching: A Universal and Efficient Framework for Training Well-performing Light Net
, 2018 Models applied on real time response task, like click-through rate (CTR) prediction model, require high accuracy and rigorous response time. Therefore, top-performing deep models of high depth and complexity are not well suited for these applications ...
Bian, Weijie +5 more
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Advanced Functional Materials, EarlyView.Backbone modulation in glycolated conjugated polymers governs ion accessibility to side chains, strengthes anion adsorption, and suppresses back‐diffusion. As the number of thiophene units increases, structural reorganization, retention, and synaptic plasticity are enhanced, leading to improved neuromorphic performance in electrolyte‐gated organic ...
Junho Sung +10 more
wiley +1 more source
3sG: Three‐stage guidance for indoor human action recognition
IET Image ProcessingInference using skeleton to steer RGB videos is applicable to fine‐grained activities in indoor human action recognition (IHAR). However, existing methods that explore only spatial alignment are prone to bias, resulting in limited performance.
Hai Nan, Qilang Ye, Zitong Yu, Kang An
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Point completion by a Stack‐Style Folding Network with multi‐scaled graphical features
IET Computer Vision, 2023 Point cloud completion is prevalent due to the insufficient results from current point cloud acquisition equipments, where a large number of point data failed to represent a relatively complete shape.
Yunbo Rao +3 more
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3D Depthwise Convolution: Reducing Model Parameters in 3D Vision Tasks
, 2018 Standard 3D convolution operations require much larger amounts of memory and computation cost than 2D convolution operations. The fact has hindered the development of deep neural nets in many 3D vision tasks. In this paper, we investigate the possibility
CB Choy +5 more
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DeepID-Net: Deformable deep convolutional neural networks for object detection [PDF]
2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2015 In this paper, we propose deformable deep convolutional neural networks for generic object detection. This new deep learning object detection framework has innovations in multiple aspects. In the proposed new deep architecture, a new deformation constrained pooling (def-pooling) layer models the deformation of object parts with geometric constraint and
Wanli Ouyang +10 more
openaire +2 more sources
Advanced Functional Materials, EarlyView.We use scanning nitrogen vacancy magnetometry to directly image the weak in‐plane magnetic moments in mixed phase BiFeO3 at the nanoscale and quantify the local magnetic moments to be 18.8±2.0 μB/nm2 in the rhombohedral‐like phase and 1.5±0.6 μB/nm2 in the well‐known non‐magnetic tetragonal‐like phase.
Lei Wang +14 more
wiley +1 more source