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Preface
2021
Journal of Computer Science and Technology
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The authors propose a deep cascade framework to incorporate multi-scale signal coding with deep cascade coding. ...
These contributions show advanced technologies on learning from small samples, including disambiguation-based partial label learning, source-free domain adaptation, and deep cascade models. ...
The authors propose a deep cascade framework to incorporate multi-scale signal coding with deep cascade coding. ...
doi:10.1007/s11390-021-0004-1
fatcat:fvcyxvkw2reohg7t2feotxonfe
Deep Cascade Multi-task Learning for Slot Filling in Online Shopping Assistant
[article]
2019
arXiv
pre-print
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In this work, inspired by the unique structure of E-commerce knowledge base, we propose a novel multi-task model with cascade and residual connections, which jointly learns segment tagging, named entity ...
While these models work relatively well on standard benchmark datasets, they face challenges in the context of E-commerce where the slot labels are more informative and carry richer expressions. ...
We proposed a deep multi-task sequence learning framework with cascade and residual connection. ...
arXiv:1803.11326v4
fatcat:ivmfc6lo4nbzzcwlf6biedhmje
Deep Cascade Multi-Task Learning for Slot Filling in Online Shopping Assistant
2019
PROCEEDINGS OF THE THIRTIETH AAAI CONFERENCE ON ARTIFICIAL INTELLIGENCE AND THE TWENTY-EIGHTH INNOVATIVE APPLICATIONS OF ARTIFICIAL INTELLIGENCE CONFERENCE
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In this work, inspired by the unique structure of E-commerce knowledge base, we propose a novel multi-task model with cascade and residual connections, which jointly learns segment tagging, named entity ...
While these models work relatively well on standard benchmark datasets, they face challenges in the context of E-commerce where the slot labels are more informative and carry richer expressions. ...
We proposed a deep multi-task sequence learning framework with cascade and residual connection. ...
doi:10.1609/aaai.v33i01.33016465
fatcat:a5hmxyguu5ht5ghkhgjr36oj5u
A Deep Cascade Model for Multi-Document Reading Comprehension
[article]
2018
arXiv
pre-print
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To address this problem, we develop a novel deep cascade learning model, which progressively evolves from the document-level and paragraph-level ranking of candidate texts to more precise answer extraction ...
Given the complexity of the real-world multi-document MRC scenario, it is difficult to jointly optimize both in an end-to-end system. ...
multi-task deep MRC model on the remaining content. ...
arXiv:1811.11374v1
fatcat:3jtulmdoyjfd3ltxjrnyoy7liu
A Deep Cascade Model for Multi-Document Reading Comprehension
2019
PROCEEDINGS OF THE THIRTIETH AAAI CONFERENCE ON ARTIFICIAL INTELLIGENCE AND THE TWENTY-EIGHTH INNOVATIVE APPLICATIONS OF ARTIFICIAL INTELLIGENCE CONFERENCE
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To address this problem, we develop a novel deep cascade learning model, which progressively evolves from the documentlevel and paragraph-level ranking of candidate texts to more precise answer extraction ...
Given the complexity of the real-world multi-document MRC scenario, it is difficult to jointly optimize both in an end-to-end system. ...
multi-task deep MRC model on the remaining content. ...
doi:10.1609/aaai.v33i01.33017354
fatcat:lo52vbvoaffjrbbcqloic2kxny
Fast, Accurate, and Lightweight Super-Resolution with Cascading Residual Network
[article]
2018
arXiv
pre-print
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In recent years, deep learning methods have been successfully applied to single-image super-resolution tasks. ...
We also present variant models of the proposed cascading residual network to further improve efficiency. ...
Deep Learning Based Image Super-Resolution Recently, deep learning based models have shown dramatic improvements in the SISR task. Dong et al. ...
arXiv:1803.08664v5
fatcat:mflcl63ueng43huqzc46j26v7u
GLAC Net: GLocal Attention Cascading Networks for Multi-image Cued Story Generation
[article]
2019
arXiv
pre-print
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Here we propose a deep learning network model, GLAC Net, that generates visual stories by combining global-local (glocal) attention and context cascading mechanisms. ...
The task of multi-image cued story generation, such as visual storytelling dataset (VIST) challenge, is to compose multiple coherent sentences from a given sequence of images. ...
Here we propose a deep learning network model that generates visual stories by combining globallocal (glocal) attention and context cascading mechanisms. ...
arXiv:1805.10973v3
fatcat:prg2t6q6y5c7noshoct4fsqigy
Fast, Accurate, and Lightweight Super-Resolution with Cascading Residual Network
[chapter]
2018
Lecture Notes in Computer Science
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In recent years, deep learning methods have been successfully applied to single-image super-resolution tasks. ...
We also present variant models of the proposed cascading residual network to further improve efficiency. ...
Deep Learning Based Image Super-Resolution Recently, deep learning based models have shown dramatic improvements in the SISR task. Dong et al. ...
doi:10.1007/978-3-030-01249-6_16
fatcat:4ibhd6um5jahji3czpqsss2apy
A Tetrahedron-Based Heat Flux Signature for Cortical Thickness Morphometry Analysis
[chapter]
2018
Lecture Notes in Computer Science
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Learning in Limited Angle Tomography 554 Normative Modeling of Neuroimaging Data using Scalable Multi-Task Gaussian Processes 556 Deep Multi-Structural Shape Analysis: Application to Neuroanatomy 557 ...
via Coarse to Fine Context Memory 295 Deep Learning from Label Proportions for Emphysema Quantification 296 Semi-Automatic RECIST Labeling on CT Scans with Cascaded Convolutional Neural Networks 306 A ...
doi:10.1007/978-3-030-00931-1_48
pmid:30338317
pmcid:PMC6191198
fatcat:dqhvpm5xzrdqhglrfftig3qejq
Improved Deep Forest Mode for Detection of Fraudulent Online Transaction
2020
Computing and informatics
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In addition, the autoencoder model is introduced into the detection model to enhance the representation learning ability. ...
compared to the random forest model, and a beyond 5 % improvement compared to the original deep forest model. ...
Deep forest (multi-Grained Cascade Forest, gcForest) is a novel decision tree ensemble method, which may open the door towards an alternative to deep neural networks for many tasks [11] . ...
doi:10.31577/cai_2020_5_1082
fatcat:4udkvbwobvc75mrnxioi3ztaku
Image Super-Resolution via Progressive Cascading Residual Network
2018
2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW)
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In this paper, we address this issue by adapting a progressive learning scheme to the deep convolutional neural network. ...
The problem of enhancing the resolution of a single lowresolution image has been popularly addressed by recent deep learning techniques. ...
With the advancement of deep learning technologies in various computer vision tasks [10, 22, 20] , many deep learning-based models have been proposed to tackle the SISR task [7, 13, 26, 19] . ...
doi:10.1109/cvprw.2018.00123
dblp:conf/cvpr/AhnKS18
fatcat:meh2kbmzpvhw5d7aeqzdah7uqy
Learning from Weak-Label Data: A Deep Forest Expedition
2020
PROCEEDINGS OF THE THIRTIETH AAAI CONFERENCE ON ARTIFICIAL INTELLIGENCE AND THE TWENTY-EIGHTH INNOVATIVE APPLICATIONS OF ARTIFICIAL INTELLIGENCE CONFERENCE
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In this paper, we propose LCForest, which is the first tree ensemble based deep learning method for weak-label learning. ...
Rather than formulating the problem as a regularized framework, we employ the recently proposed cascade forest structure, which processes information layer-by-layer, and endow it with the ability of exploiting ...
The MLDF method ) makes a first step on adapting deep forest to multi-label learning tasks by designing a multi-layer structure to learn correlations among labels. ...
doi:10.1609/aaai.v34i04.6092
fatcat:42v6zgorajah3lalil7l23b2ci
SipBit: A Sensing Platform to Recognize Beverage Type, Volume, and Sugar Content Using Electrical Impedance Spectroscopy and Deep Learning
2022
CHI Conference on Human Factors in Computing Systems Extended Abstracts
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Then, a multi-task network cascade algorithm was employed to identify eight diferent beverage types in various volume levels and sugar concentrations. ...
SipBit consists of an electrical impedance measurement unit and a recognition method based on deep learning techniques. ...
Deep learning models (Multi-task network cascade). ...
doi:10.1145/3491101.3519713
fatcat:3t3ccf2dnbem3dkr57ez2uvxbu
A Big Data Analysis Framework Using Apache Spark and Deep Learning
[article]
2017
arXiv
pre-print
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using the popular concept of Cascade Learning. ...
In this paper, we propose a novel framework that combines the distributive computational abilities of Apache Spark and the advanced machine learning architecture of a deep multi-layer perceptron (MLP), ...
Work Related to Cascade Learning The two stages of the framework mentioned in this paper, namely analysis using Spark and multi-layer perceptron, is connected via cascading. ...
arXiv:1711.09279v1
fatcat:pyhyhv4pcfbzziopnluedigeae
An Automatic Recognition of Tooth-Marked Tongue Based on Tongue Region Detection and Tongue Landmark Detection via Deep Learning
2020
IEEE Access
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To address this challenging task, a two-stage method based on tongue region detection and tongue landmark detection via deep learning is proposed in this paper. ...
Moreover, to the best of our knowledge, we are the first attempt to manage the tooth-marked tongue recognition via deep learning. We conducted extensive experiments with the proposed method. ...
Inspired by the idea of Multi-task Cascaded Convolutional Networks (MTCNN) [15] , we design a cascaded CNNs with multi-task learning to manage these two tasks simultaneously. ...
doi:10.1109/access.2020.3017725
fatcat:6ou6wdmxrnfafghiazm7dnfewe
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