Multitask Air-Quality Prediction Based on LSTM-Autoencoder Modeldoi:10.1109/tcyb.2019.2945999Xinghan XuMinoru YonedaIEEE Trans Cybern
Multi-task learning is a popular machine learning approach that enables simultaneous learning of multiple related tasks, improving algorithmic efficiency and effectiveness. In the hard parameter sharing approach, an encoder shared through multiple tasks
Hori, “Speech enhancement based on deep denoising autoencoder,” in Proc. Annual Conference of the International Speech Communication Association, 2013, pp. 555–559. [8] Y. Xu, J. Du, L.-R. Dai, and C.-H. Lee, “An experimental study on speech enhancement based on deep neural ...
Autoencoder-based multi-task learning for imputation and classification of incomplete data Appl. Soft Comput. (2021) LiM. et al. Predicting future locations of moving objects with deep fuzzy-LSTM networks Transportmetr. A Transp. Sci. (2020) ElmanJ.L. Finding structure in time Cogn. Sci. ...
Relevant publications: [PDF][CODE][BIBTEX] Minmin Chen, Zhixiang (Eddie) Xu, Kilian Q. Weinberger, Fei Sha.Marginalized Stacked Denoising Autoencoders for Domain Adaptation.Proceedings of 29th International Conference on Machine Learning (ICML), Edingburgh Scotland, Omnipress, pages 767-774, 2012...
BL. Research at SemEval-2022 Task 1: deep networks for Reverse Dictionary using embeddings and LSTM autoencoders Proceedings of the 16th International Workshop on Semantic Evaluation (SemEval-2022) (2022), pp. 94-100 CrossrefView in ScopusGoogle Scholar [9] T.H.H. Tran, M. Martinc, M...
Deep Architecture for High-Speed Railway Insulator Surface Defect Detection: Denoising Autoencoder With Multitask Learning The insulator is an important catenary component that maintains the insulation between the catenary and earth. Due to the long-term impact of railway vehic... G Kang,S Gao,L ...
Multiscale Variational Autoencoder Aided Convolutional Neural Network for Pose Estimation of Tunneling Machine Using a Single Monocular Image machine in an end-to-end manner using a single monocular image, in which the multitask variational learning scheme is able to enhance the generalization ... H...
•Bimodalautoencoder –Idea:predictunseenmodalityfromobservedmodality J.Ngiam,A.Khosla,M.Kim,J.Nam,H.Lee,A.Y.Ng.Multimodaldeeplearning. ICML2011. MultimodalFeatureLearning •Visualizationoflearnedfilters •Results:AVLettersLipreadingdataset Audio(spectrogram)andVideofeatureslearnedover100mswindows Method...
Normal behavior models (by autoencoders (AE) [9], variational autoencoders (VAE) [10], deep autoencoder Gaussian mixture model (DAGMM) [11], and generative adversarial networks (GAN) [12]) are typically established, and the difference between the original data and the reconstruction is meas...