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Adapting RNN Sequence Prediction Model to Multi-label Set Prediction [article]

Kechen Qin, Cheng Li, Virgil Pavlu, Javed A. Aslam
2019 arXiv   pre-print
We present an adaptation of RNN sequence models to the problem of multi-label classification for text, where the target is a set of labels, not a sequence.  ...  Previous such RNN models define probabilities for sequences but not for sets; attempts to obtain a set probability are after-thoughts of the network design, including pre-specifying the label order, or  ...  Adapting RNN Sequence Prediction Model to Multi-label Set Prediction We propose a new way of adapting RNN to multilabel set prediction, which we call set-RNN.  ... 
arXiv:1904.05829v1 fatcat:7ga5knttf5anbfg4ze4gm3dog4

Adapting

Kechen Qin, Cheng Li, Virgil Pavlu, Javed Aslam
2019 Proceedings of the 2019 Conference of the North  
We present an adaptation of RNN sequence models to the problem of multi-label classification for text, where the target is a set of labels, not a sequence.  ...  Previous such RNN models define probabilities for sequences but not for sets; attempts to obtain a set probability are after-thoughts of the network design, including pre-specifying the label order, or  ...  Adapting RNN Sequence Prediction Model to Multi-label Set Prediction We propose a new way of adapting RNN to multilabel set prediction, which we call set-RNN.  ... 
doi:10.18653/v1/n19-1321 dblp:conf/naacl/QinLPA19 fatcat:vnea377afbdy3pquzmuqcs4nsi

Deep Learning with a Rethinking Structure for Multi-label Classification [article]

Yao-Yuan Yang, Yi-An Lin, Hong-Min Chu, Hsuan-Tien Lin
2019 arXiv   pre-print
When solving the MLC problems, we generally expect the learning algorithm to take the hidden correlation of the labels into account to improve the prediction performance.  ...  Furthermore, the rethinking process makes it easy to adapt to different evaluation criteria to match real-world application needs.  ...  Conclusion Classic multi-label classification (MLC) algorithms predict labels as a sequence to model the label correlation.  ... 
arXiv:1802.01697v2 fatcat:krwgwruoqjbb5dpnpgvhfhbuqa

Orderless Recurrent Models for Multi-label Classification [article]

Vacit Oguz Yazici, Abel Gonzalez-Garcia, Arnau Ramisa, Bartlomiej Twardowski, Joost van de Weijer
2020 arXiv   pre-print
Therefore, in this paper, we propose ways to dynamically order the ground truth labels with the predicted label sequence.  ...  Since RNNs produce sequential outputs, labels need to be ordered for the multi-label classification task.  ...  Method Image-to-sequence model For the task of multi-label classification we consider a CNN-RNN architecture, first proposed in [48] .  ... 
arXiv:1911.09996v3 fatcat:xr6iiecqffaypgxbypq4g4nxui

Orderless Recurrent Models for Multi-Label Classification

Vacit Oguz Yazici, Abel Gonzalez-Garcia, Arnau Ramisa, Bartlomiej Twardowski, Joost van de Weijer
2020 2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)  
Therefore, we propose ways to dynamically order the ground truth labels with the predicted label sequence. This allows for faster training of more optimal LSTM models.  ...  Since RNNs produce sequential outputs, labels need to be ordered for the multi-label classification task.  ...  Method Image-to-sequence model For the task of multi-label classification we consider a CNN-RNN architecture, first proposed in [48] .  ... 
doi:10.1109/cvpr42600.2020.01345 dblp:conf/cvpr/YaziciGRT020 fatcat:bihzzfxq3jazhhlo4dlh4jaml4

Action-Agnostic Human Pose Forecasting [article]

Hsu-kuang Chiu, Ehsan Adeli, Borui Wang, De-An Huang, Juan Carlos Niebles
2018 arXiv   pre-print
To this end, we propose a new recurrent neural network for modeling the hierarchical and multi-scale characteristics of the human dynamics, denoted by triangular-prism RNN (TP-RNN).  ...  Our model captures the latent hierarchical structure embedded in temporal human pose sequences by encoding the temporal dependencies with different time-scales.  ...  long-term predictions or methods that use action labels as inputs to their models.  ... 
arXiv:1810.09676v1 fatcat:pms3wo6iyvbsrh2vkcdqjfdgza

CNN-RNN: A Unified Framework for Multi-label Image Classification

Jiang Wang, Yi Yang, Junhua Mao, Zhiheng Huang, Chang Huang, Wei Xu
2016 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR)  
Experimental results on public benchmark datasets demonstrate that the proposed architecture achieves better performance than the state-of-the-art multi-label classification models.  ...  Combined with CNNs, the proposed CNN-RNN framework learns a joint image-label embedding to characterize the semantic label dependency as well as the image-label relevance, and it can be trained end-to-end  ...  model, we set the minimum prediction length during beam search to ensure that at least k labels are predicted.  ... 
doi:10.1109/cvpr.2016.251 dblp:conf/cvpr/WangYMHHX16 fatcat:s2tgck7esbbl5nmfxluycs6cea

Learning Context-dependent Label Permutations for Multi-label Classification

Jinseok Nam, Young-Bum Kim, Eneldo Loza Mencía, Sunghyun Park, Ruhi Sarikaya, Johannes Fürnkranz
2019 International Conference on Machine Learning  
As a result, we obtain a powerful sequence prediction-based algorithm for multi-label classification, which is able to efficiently and explicitly exploit label dependencies.  ...  A key problem in multi-label classification is to utilize dependencies among the labels.  ...  Acknowledgements The authors would like to thank anonymous reviewers for the constructive feedback.  ... 
dblp:conf/icml/NamKMPSF19 fatcat:a2vrq7nvrrhpdnx7aexelgyit4

Deep multi-task learning with low level tasks supervised at lower layers

Anders Søgaard, Yoav Goldberg
2016 Proceedings of the 54th Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers)  
We present a multi-task learning architecture with deep bi-directional RNNs, where different tasks supervision can happen at different layers.  ...  Finally, we also show how this architecture can be used for domain adaptation.  ...  MTL in deep bi-RNNs In a multi-task learning (MTL) setting, we have several prediction tasks over the same input space.  ... 
doi:10.18653/v1/p16-2038 dblp:conf/acl/SogaardG16 fatcat:4xzuehnq3fbx5obww65x3c3fpm

Order-Free RNN with Visual Attention for Multi-Label Classification [article]

Shang-Fu Chen, Yi-Chen Chen, Chih-Kuan Yeh, Yu-Chiang Frank Wang
2017 arXiv   pre-print
In this paper, we propose the joint learning attention and recurrent neural network (RNN) models for multi-label classification.  ...  For multi-label classification, it would be desirable to have a robust inference process, so that the prediction error would not propagate and thus affect the performance.  ...  A Brief Review of CNN-RNN CNN-RNN ) is a recent deep learning based model for multi-label classification.  ... 
arXiv:1707.05495v3 fatcat:ff2ogibtpbh4flmrsevrm66gvu

Recent Advances in End-to-End Automatic Speech Recognition [article]

Jinyu Li
2022 arXiv   pre-print
Without providing excellent solutions to all these factors, it is hard for E2E models to be widely commercialized.  ...  Recently, the speech community is seeing a significant trend of moving from deep neural network based hybrid modeling to end-to-end (E2E) modeling for automatic speech recognition (ASR).  ...  This is especially useful for adapting RNN-T, in which the prediction network works similarly to an LM.  ... 
arXiv:2111.01690v2 fatcat:6pktwep34jdvjklw4gkri4yn4y

Maximizing Subset Accuracy with Recurrent Neural Networks in Multi-label Classification

Jinseok Nam, Eneldo Loza Mencía, Hyunwoo J. Kim, Johannes Fürnkranz
2017 Neural Information Processing Systems  
Multi-label classification is the task of predicting a set of labels for a given input instance.  ...  fashion, and the task is to predict a sequence of binary values for these labels.  ...  Acknowledgments The authors would like to thank anonymous reviewers for their thorough feedback.  ... 
dblp:conf/nips/NamMKF17 fatcat:dyse5hub3ndt3cz5wwgsrfskie

Order-Free RNN With Visual Attention for Multi-Label Classification

Shang-Fu Chen, Yi-Chen Chen, Chih-Kuan Yeh, Yu-Chiang Wang
2018 PROCEEDINGS OF THE THIRTIETH AAAI CONFERENCE ON ARTIFICIAL INTELLIGENCE AND THE TWENTY-EIGHTH INNOVATIVE APPLICATIONS OF ARTIFICIAL INTELLIGENCE CONFERENCE  
We propose a recurrent neural network (RNN) based model for image multi-label classification.  ...  Different from existing approaches utilize either model in their network architectures, training of our model does not require pre-defined label orders.  ...  A Brief Review of CNN-RNN CNN-RNN (Wang et al. 2016 ) is a recent deep learning based model for multi-label classification.  ... 
doi:10.1609/aaai.v32i1.12230 fatcat:pxegwjeqk5ga3gbvr4ykghglfy

Medi-Care AI: Predicting Medications From Billing Codes via Robust Recurrent Neural Networks [article]

Deyin Liu, Lin Wu, Xue Li
2019 arXiv   pre-print
In this paper, we present an effective deep prediction framework based on robust recurrent neural networks (RNNs) to predict the likely therapeutic classes of medications a patient is taking, given a sequence  ...  to improved RNNs robustness towards data variability in terms of missing values and multiple errors.  ...  They used a GRU model in a multi-label context to predict the medications, billing codes, and time of the next patient visit from a sequence of that same information for previous visits.  ... 
arXiv:2001.10065v1 fatcat:py6eteeyvvdibllyk2rufk5iqm

Context Matters: Refining Object Detection in Video with Recurrent Neural Networks [article]

Subarna Tripathi and Zachary C. Lipton and Serge Belongie and Truong Nguyen
2016 arXiv   pre-print
First, we train a pseudo-labeler, that is, a domain-adapted convolutional neural network for object detection.  ...  The pseudo-labeler is first trained individually on the subset of labeled frames, and then subsequently applied to all frames.  ...  In our method, we adapt YOLO to generate pseudo-labels for all video frames, feeding them as inputs to the refinement RNN.  ... 
arXiv:1607.04648v2 fatcat:zewwdxh54vbyxkbwqmjhddyhkm
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