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A Tensor Decomposition-Based Approach for Detecting Dynamic Network States From EEG.

, , , and . IEEE Trans. Biomed. Eng., 64 (1): 225-237 (2017)

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Sparsity-promoting optimal control of spatially-invariant systems., , , and . ACC, page 1255-1260. IEEE, (2014)Scaling the Poisson GLM to massive neural datasets through polynomial approximations., and . NeurIPS, page 3521-3531. (2018)Low-rank tensor decomposition based dynamic network tracking., and . GlobalSIP, page 468-472. IEEE, (2014)Structured flexibility in recurrent neural networks via neuromodulation., , , and . NeurIPS, (2024)Brain-to-Text Benchmark '24: Lessons Learned., , , , , , , , , and 6 other author(s). CoRR, (2024)Neural Latents Benchmark '21: Evaluating latent variable models of neural population activity., , , , , , , , , and 6 other author(s). NeurIPS Datasets and Benchmarks, (2021)Efficient Non-conjugate Gaussian Process Factor Models for Spike Count Data using Polynomial Approximations., , , , and . ICML, volume 119 of Proceedings of Machine Learning Research, page 5177-5186. PMLR, (2020)Slice Sampling Reparameterization Gradients., , and . NeurIPS, page 23532-23544. (2021)Modeling state-dependent communication between brain regions with switching nonlinear dynamical systems., , , , , , and . ICLR, OpenReview.net, (2024)A general recurrent state space framework for modeling neural dynamics during decision-making., , and . ICML, volume 119 of Proceedings of Machine Learning Research, page 11680-11691. PMLR, (2020)