Towards a universal neural network encoder for time series
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Alexandros Karatzoglou | Joan Serrà | Santiago Pascual | Alexandros Karatzoglou | Santiago Pascual | J. Serrà
[1] Hongyi Zhang,et al. mixup: Beyond Empirical Risk Minimization , 2017, ICLR.
[2] Lin-Shan Lee,et al. Audio Word2Vec: Unsupervised Learning of Audio Segment Representations Using Sequence-to-Sequence Autoencoder , 2016, INTERSPEECH.
[3] Jimmy Ba,et al. Adam: A Method for Stochastic Optimization , 2014, ICLR.
[4] VARUN CHANDOLA,et al. Anomaly detection: A survey , 2009, CSUR.
[5] Nan Hua,et al. Universal Sentence Encoder , 2018, ArXiv.
[6] Rich Caruana,et al. Multitask Learning , 1997, Machine Learning.
[7] Nima Hatami,et al. Classification of time-series images using deep convolutional neural networks , 2017, International Conference on Machine Vision.
[8] Lovekesh Vig,et al. TimeNet: Pre-trained deep recurrent neural network for time series classification , 2017, ESANN.
[9] T. Warren Liao,et al. Clustering of time series data - a survey , 2005, Pattern Recognit..
[10] Lorien Y. Pratt,et al. Discriminability-Based Transfer between Neural Networks , 1992, NIPS.
[11] Holger Schwenk,et al. Supervised Learning of Universal Sentence Representations from Natural Language Inference Data , 2017, EMNLP.
[12] Jason Lines,et al. HIVE-COTE: The Hierarchical Vote Collective of Transformation-Based Ensembles for Time Series Classification , 2016, 2016 IEEE 16th International Conference on Data Mining (ICDM).
[13] Oriol Vinyals,et al. Matching Networks for One Shot Learning , 2016, NIPS.
[14] Andrea Vedaldi,et al. Instance Normalization: The Missing Ingredient for Fast Stylization , 2016, ArXiv.
[15] Tim Oates,et al. Time series classification from scratch with deep neural networks: A strong baseline , 2016, 2017 International Joint Conference on Neural Networks (IJCNN).
[16] Tom Heskes,et al. Task Clustering and Gating for Bayesian Multitask Learning , 2003, J. Mach. Learn. Res..
[17] Richard S. Zemel,et al. Prototypical Networks for Few-shot Learning , 2017, NIPS.
[18] Heiga Zen,et al. WaveNet: A Generative Model for Raw Audio , 2016, SSW.
[19] Quoc V. Le,et al. Sequence to Sequence Learning with Neural Networks , 2014, NIPS.
[20] Oriol Vinyals,et al. Neural Discrete Representation Learning , 2017, NIPS.
[21] Gaël Varoquaux,et al. Scikit-learn: Machine Learning in Python , 2011, J. Mach. Learn. Res..
[22] Eamonn J. Keogh,et al. Segmenting Time Series: A Survey and Novel Approach , 2002 .
[23] Jason Lines,et al. Time-Series Classification with COTE: The Collective of Transformation-Based Ensembles , 2015, IEEE Transactions on Knowledge and Data Engineering.
[24] Luca Antiga,et al. Automatic differentiation in PyTorch , 2017 .
[25] Eamonn J. Keogh,et al. The great time series classification bake off: a review and experimental evaluation of recent algorithmic advances , 2016, Data Mining and Knowledge Discovery.
[26] Yixin Chen,et al. Multi-Scale Convolutional Neural Networks for Time Series Classification , 2016, ArXiv.
[27] Sergey Levine,et al. Model-Agnostic Meta-Learning for Fast Adaptation of Deep Networks , 2017, ICML.
[28] George Athanasopoulos,et al. Forecasting: principles and practice , 2013 .
[29] Sergey Ioffe,et al. Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift , 2015, ICML.
[30] Jian Sun,et al. Delving Deep into Rectifiers: Surpassing Human-Level Performance on ImageNet Classification , 2015, 2015 IEEE International Conference on Computer Vision (ICCV).
[31] Sebastian Thrun,et al. Lifelong robot learning , 1993, Robotics Auton. Syst..
[32] Michael McCloskey,et al. Catastrophic Interference in Connectionist Networks: The Sequential Learning Problem , 1989 .
[33] Jan Niehues,et al. Toward Multilingual Neural Machine Translation with Universal Encoder and Decoder , 2016, IWSLT.
[34] Abdullah Mueen,et al. Time series motif discovery: dimensions and applications , 2014, WIREs Data Mining Knowl. Discov..
[35] R. Suganya,et al. Data Mining Concepts and Techniques , 2010 .