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Enabling factor analysis on thousand-subject neuroimaging datasets.

, , , , , , , , , and . IEEE BigData, page 1151-1160. IEEE Computer Society, (2016)

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A semi-supervised method for multi-subject FMRI functional alignment., , , and . ICASSP, page 1098-1102. IEEE, (2017)Fusion of ultrasound harmonic imaging with clutter removal using sparse signal separation., , , and . ICASSP, page 793-797. IEEE, (2015)Slower is Better: Revisiting the Forgetting Mechanism in LSTM for Slower Information Decay., , , , , and . CoRR, (2021)A Convolutional Autoencoder for Multi-Subject fMRI Data Aggregation., , , , , , , and . CoRR, (2016)Interpretable multi-timescale models for predicting fMRI responses to continuous natural speech., , , , , and . NeurIPS, (2020)A Zero-Positive Learning Approach for Diagnosing Software Performance Regressions., , , , , and . NeurIPS, page 11623-11635. (2019)Low-dimensional Structure in the Space of Language Representations is Reflected in Brain Responses., , , and . NeurIPS, page 8332-8344. (2021)Searching for N:M Fine-grained Sparsity of Weights and Activations in Neural Networks., , , and . ECCV Workshops (7), volume 13807 of Lecture Notes in Computer Science, page 130-143. Springer, (2022)Capturing Shared and Individual Information in fMRI Data., , , , and . ICASSP, page 826-830. IEEE, (2018)Efficient, Sparse Representation of Manifold Distance Matrices for Classical Scaling., and . CVPR, page 2850-2858. Computer Vision Foundation / IEEE Computer Society, (2018)