A copy of this work was available on the public web and has been preserved in the Wayback Machine. The capture dates from 2017; you can also visit the original URL.
The file type is application/pdf.
Filters
Special issue on "visual semantic analysis with weak supervision"
2017
Multimedia Systems
Preserved Fulltext
Finally, we would like to thank all the authors who have contributed to this special issue. Thanks to all the people who help us to make this special issue a successful one. ...
1 National University of Singapore, Singapore, Singapore Acknowledgments We also thank the reviewers for their efforts to guarantee the high quality of this special issue. ...
This special issue will target the most recent technical progresses on learning techniques for visual semantic understanding with weak supervision, such as weakly labeled representative views. ...
doi:10.1007/s00530-016-0527-4
fatcat:72hcjiiwfzbk7mdrsumuia7rzy
Foreword: special issue for the journal track of the 9th Asian Conference on Machine Learning (ACML 2017)
2017
Machine Learning
Preserved Fulltext
A copy of this work was available on the public web and has been preserved in the Wayback Machine. The capture dates from 2018; you can also visit the original URL.
The file type is application/pdf.
Unlike typical semi-supervised learning method, the proposed method does not depend on strong distributional assumptions. ...
The paper "Semi-Supervised AUC Optimization based on Positive-Unlabeled Learning", by Tomoya Sakai, Gang Niu, and Masashi Sugiyama provides a semi-supervised learning method for maximizing the area under ...
doi:10.1007/s10994-017-5691-z
fatcat:wzshsay22jbsxnqfq2wqjj66ka
Special Issue on Deep Learning in Biological Image and Signal Processing
2020
IEEE Signal Processing Magazine
Preserved Fulltext
A copy of this work was available on the public web and has been preserved in the Wayback Machine. The capture dates from 2020; you can also visit the original URL.
The file type is application/pdf.
analysis • Unsupervised and weakly-supervised deep learning • Deep learning strategies in imaging genetics • Graph neural networks for connectivity analysis • Privacy-preserving distributed deep learning ...
Fostering crosspollination between data-driven and model-driven approaches, the Special Issue aims to inspire researchers in developing novel solutions to current challenges of deep learning in biological ...
analysis • Unsupervised and weakly-supervised deep learning • Deep learning strategies in imaging genetics • Graph neural networks for connectivity analysis • Privacy-preserving distributed deep learning ...
doi:10.1109/msp.2020.3028720
fatcat:an3exd7lmjadrhv75kzc4ds6vm
Special Issue on Deep Learning in Biological Image and Signal Processing
2021
IEEE Signal Processing Magazine
Preserved Fulltext
A copy of this work was available on the public web and has been preserved in the Wayback Machine. The capture dates from 2021; you can also visit the original URL.
The file type is application/pdf.
analysis • Unsupervised and weakly supervised deep learning • Deep learning strategies in imaging genetics • Graph neural networks for connectivity analysis • Privacy-preserving distributed deep learning ...
This special issue of IEEE Signal Processing Magazine provides a venue for a wide and diverse audience to survey recent research advances in deep learning for applications in biological image and signal ...
analysis • Unsupervised and weakly supervised deep learning • Deep learning strategies in imaging genetics • Graph neural networks for connectivity analysis • Privacy-preserving distributed deep learning ...
doi:10.1109/msp.2020.3040489
fatcat:qys3d64ajncqpbyq2bl4lnoofm
Preface to the Special Issue on Robust Machine Learning for Open Scenarios
2022
International Journal of Software and Informatics
Preserved Fulltext
A copy of this work was available on the public web and has been preserved in the Wayback Machine. The capture dates from 2022; you can also visit the original URL.
The file type is application/pdf.
Specifically, this special issue encompasses machine learning for the cases of distribution changes, weakly supervised learning, model reuse, representation learning, reinforcement learning, adversarial ...
We hope that this special issue will boost the research on robust machine learning in open scenarios in China. ...
Confidence-weighted Learning for Feature Evolution put forward a confidence-weighted learning algorithm for feature evolution based on second-order information to address the replacement and disappearance ...
doi:10.21655/ijsi.1673-7288.00265
fatcat:lvhc2wd5nba7dfwq7vhx6ftvbu
Non-iterative approaches in training feed-forward neural networks and their applications
2018
Soft Computing - A Fusion of Foundations, Methodologies and Applications
Preserved Fulltext
A copy of this work was available on the public web and has been preserved in the Wayback Machine. The capture dates from 2018; you can also visit the original URL.
The file type is application/pdf.
Focusing on non-iterative approaches in training feed-forward neural networks, this special issue includes 12 papers to share the latest progress, current challenges, and potential applications of this ...
This editorial presents a background of the special issue and a brief introduction to the 12 contributions. ...
Antonio Di Nola, the Editor-in-Chief of Soft Computing, for his support to edit this special issue. ...
doi:10.1007/s00500-018-3203-0
fatcat:snmmbo3ys5a2plmpsyxowzye6a
Weakly Supervised Object Localization and Detection: A Survey
[article]
2021
arXiv
pre-print
Preserved Fulltext
A copy of this work was available on the public web and has been preserved in the Wayback Machine. The capture dates from 2021; you can also visit the original URL.
The file type is application/pdf.
In this work, we review (1) classic models, (2) approaches with feature representations from off-the-shelf deep networks, (3) approaches solely based on deep learning, and (4) publicly available datasets ...
As an emerging and challenging problem in the computer vision community, weakly supervised object localization and detection plays an important role for developing new generation computer vision systems ...
Robust Learning Theory To address the learning under uncertainty issue that is inherently existed in the weakly supervised learning process, robust learning strategy will become one of the key techniques ...
arXiv:2104.07918v1
fatcat:dwl6sjfzibdilnvjnrbifp4uke
Guest Editorial Annotation-Efficient Deep Learning: The Holy Grail of Medical Imaging
2021
IEEE Transactions on Medical Imaging
Preserved Fulltext
A copy of this work was available on the public web and has been preserved in the Wayback Machine. The capture dates from 2022; you can also visit the original URL.
The file type is application/pdf.
Overview of the topics covered in this Special Issue on annotationefficient deep learning.
Fig. 2 . 2 Fig. 2. ...
The Special Issue offers only four articles on this topic, leaving it under-investigated. ...
doi:10.1109/tmi.2021.3089292
pmid:34795461
pmcid:PMC8594751
fatcat:t7kufjbdyfgazng3gcuyuhawxu
Guided Learning Convolution System for DCASE 2019 Task 4
[article]
2019
arXiv
pre-print
Preserved Fulltext
A copy of this work was available on the public web and has been preserved in the Wayback Machine. The capture dates from 2020; you can also visit the original URL.
The file type is application/pdf.
To take advantage of the unlabeled data, we adopt Guided Learning for semi-supervised learning. ...
We also analyze the effect of the synthetic data on the performance of the model and finally achieve an event-based F-measure of 45.43% on the validation set and an event-based F-measure of 42.7% on the ...
its effects on weakly-supervised learning and unsupervised learning
model and finally achieve an event-based F-measure of 45.43% on separately. ...
arXiv:1909.06178v1
fatcat:zuoodch5rrgt7ls73hzyihp32u
KDETM at NTCIR-12 Temporalia Task: Combining a Rule-based Classifier with Weakly Supervised Learning for Temporal Intent Disambiguation
2016
NTCIR Conference on Evaluation of Information Access Technologies
Preserved Fulltext
A copy of this work was available on the public web and has been preserved in the Wayback Machine. The capture dates from 2017; you can also visit the original URL.
The file type is application/pdf.
applied to train the weakly supervised classifier. ...
In our approach, we combine a rule-based classifier with weakly supervised classifier. ...
We submitted three runs based on a rule-based classifier and two different machine learning classifiers (Naive-Bayes and SVM). ...
dblp:conf/ntcir/ChyUSA16
fatcat:cxhs4kv25nglvl2nvy76swwb2a
Special Issue on Generating Realistic Visual Data of Human Behavior
2020
International Journal of Computer Vision
Preserved Fulltext
A copy of this work was available on the public web and has been preserved in the Wayback Machine. The capture dates from 2020; you can also visit the original URL.
The file type is application/pdf.
The second set of four papers in the special issue deal with generating faces. ...
The special issue was preceded by the 9th International Workshop on Human Behavior Understanding, held at ECCV (September 2018 in Munich), organized by the guest editors with the focus theme of the special ...
doi:10.1007/s11263-020-01319-w
fatcat:jky5wzgw4bbc3aluxnjq5vy6nm
Machine Learning Methods with Noisy, Incomplete or Small Datasets
2021
Applied Sciences
Preserved Fulltext
A copy of this work was available on the public web and has been preserved in the Wayback Machine. The capture dates from 2021; you can also visit the original URL.
The file type is application/pdf.
In this article, we present a collection of fifteen novel contributions on machine learning methods with low-quality or imperfect datasets, which were accepted for publication in the special issue "Machine ...
We believe that this special issue will bring new ideas for solving this challenging problem, and will provide clear examples of application in real-world scenarios. ...
The algorithm considers learning from strongly and weakly labeled data. On the other side, in [10] , Gil et al. ...
doi:10.3390/app11094132
doaj:b756026d4f1b45e89f158fe4378f7e8c
fatcat:zpqxuxf5ora2xk3zpmge73tyl4
WSL-DS: Weakly Supervised Learning with Distant Supervision for Query Focused Multi-Document Abstractive Summarization
[article]
2020
arXiv
pre-print
Preserved Fulltext
A copy of this work was available on the public web and has been preserved in the Wayback Machine. The capture dates from 2020; you can also visit the original URL.
The file type is application/pdf.
To overcome this issue, in this paper, we propose a novel weakly supervised learning approach via utilizing distant supervision. ...
Then, we iteratively train our summarization model on each single-document to alleviate the computational complexity issue that occurs while training neural summarization models in multiple documents ( ...
From now on, we denote ROUGE as R. (-4.24%) 41.01 (-4.27%) No, based on paired t-test (p ≤ .05) without Weakly Supervised Learning 40.01 (-6 .37%) 40.12 (-6.35%) Yes, based on paired t-test (p ...
arXiv:2011.01421v1
fatcat:fth4hcqwonafpcdptgrp3kwm6e
Webly Supervised Learning Meets Zero-shot Learning: A Hybrid Approach for Fine-Grained Classification
2018
2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition
Preserved Fulltext
A copy of this work was available on the public web and has been preserved in the Wayback Machine. The capture dates from 2020; you can also visit the original URL.
The file type is application/pdf.
For the second direction, the performance gap between ZSL and traditional supervised learning is still very large. ...
Comprehensive experiments on three benchmark datasets demonstrate the effectiveness of our proposed framework. ...
In this way, we can learn a more robust dictionary D t on the weakly-supervised categories. ...
doi:10.1109/cvpr.2018.00749
dblp:conf/cvpr/0002VS18
fatcat:cbm5njx6e5bd3h5oijexnzyhqm
KnowMAN: Weakly Supervised Multinomial Adversarial Networks
[article]
2021
arXiv
pre-print
Preserved Fulltext
A copy of this work was available on the public web and has been preserved in the Wayback Machine. The capture dates from 2021; you can also visit the original URL.
The file type is application/pdf.
KnowMAN strongly improves results compared to direct weakly supervised learning with a pre-trained transformer language model and a feature-based baseline. ...
This process of weakly supervised training may result in an over-reliance on the signals captured by the labeling functions and hinder models to exploit other signals or to generalize well. ...
Acknowledgements This research was funded by the WWTF through theproject "Knowledge-infused Deep Learning for Nat-ural Language Processing" (WWTF Vienna ResearchGroup VRG19-008), by the Deutsche Forschungs-gemeinschaft ...
arXiv:2109.07994v1
fatcat:2t4ijbiihvghbjaeriqbaqcpqe
« Previous
Showing results 1 — 15 out of 16,740 results