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Multi-timescale Representation Learning in LSTM Language Models.

, , , и . ICLR, OpenReview.net, (2021)

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Capturing Shared and Individual Information in fMRI Data., , , , и . ICASSP, стр. 826-830. IEEE, (2018)Efficient, Sparse Representation of Manifold Distance Matrices for Classical Scaling., и . CVPR, стр. 2850-2858. Computer Vision Foundation / IEEE Computer Society, (2018)Multi-timescale Representation Learning in LSTM Language Models., , , и . ICLR, OpenReview.net, (2021)A Multilevel Framework for Sparse Optimization with Application to Inverse Covariance Estimation and Logistic Regression., , и . SIAM J. Sci. Comput., (2016)A Convolutional Autoencoder for Multi-Subject fMRI Data Aggregation., , , , , , , и . CoRR, (2016)Clinically Deployed Distributed Magnetic Resonance Imaging Reconstruction: Application to Pediatric Knee Imaging., , , , , , и . CoRR, (2018)Slower is Better: Revisiting the Forgetting Mechanism in LSTM for Slower Information Decay., , , , , и . CoRR, (2021)Interpretable multi-timescale models for predicting fMRI responses to continuous natural speech., , , , , и . NeurIPS, (2020)A Zero-Positive Learning Approach for Diagnosing Software Performance Regressions., , , , , и . NeurIPS, стр. 11623-11635. (2019)A single-layer RNN can approximate stacked and bidirectional RNNs, and topologies in between., , , , и . CoRR, (2019)