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Discriminative reconstruction via simultaneous dense and sparse coding A. Tasissa, E. Theodosis, B. Tolooshams, and D. Ba Submitted to TMLR 2024 [paper] [code] -
Interpretable deep learning for deconvolutional analysis of neural signals B. Tolooshams*, S. Matias*, H. Wu, S. Temereanca, N. Uchida, V. N. Murthy, P. Masset, and D. Ba Submitted to Neuron 2024 [paper] -
Unrolled compressed blind-deconvolution B. Tolooshams*, S. Mulleti*, D. Ba, and Y. C. Eldar IEEE Transactions on Signal Processing 2023 [paper] -
Probabilistic unrolling: scalable, inverse-free maximum likelihood estimation of latent Gaussian models A. Lin, B. Tolooshams, Y. Atchadé, and D. Ba ICML 2023 [paper] -
Stable and interpretable unrolled dictionary learning B. Tolooshams and D. Ba TMLR 2022 [paper] [code] -
A training framework for stereo-aware speech enhancement using deep neural networks B. Tolooshams and K. Koishida IEEE ICASSP 2022 [paper] [slides] [poster] -
On the convergence of group-sparse autoencoders E. Theodosis, B. Tolooshams*, P. Tankala*, A. Tasissa, and D. Ba arXiv 2021 [paper] -
Gaussian process convolutional dictionary learning A. H. Song, B. Tolooshams, and D. Ba IEEE Signal Processing Letters 2021 [paper] -
Unfolding neural networks for compressive multichannel blind deconvolution B. Tolooshams*, S. Mulleti*, D. Ba, and Y. C. Eldar IEEE ICASSP 2021 [paper] [slides] [poster] -
Deep residual autoencoders for expectation maximization-inspired dictionary learning B. Tolooshams, S. Dey, and D. Ba IEEE Transactions on Neural Networks and Learning Systems 2021 [paper] [code] -
Convolutional dictionary learning based auto-encoders for natural exponential-family distributions B. Tolooshams*, A. H. Song*, S. Temereanca, and D. Ba ICML 2020 [paper] [code] [slides] -
Channel-attention dense u-net for multichannel speech enhancement B. Tolooshams, R. Giri, A. H. Song, U. Isik, and A. Krishnaswamy IEEE ICASSP 2020 [paper] -
Convolutional dictionary learning in hierarchical networks J. Zazo, B. Tolooshams, and D. Ba IEEE CAMSAP 2019 [paper] -
RandNet: deep learning with compressed measurements of images T. Chang*, B. Tolooshams*, and D. Ba IEEE MLSP 2019 [paper] [code] [poster] -
Scalable convolutional dictionary learning with constrained recurrent sparse auto-encoders B. Tolooshams, S. Dey, and D. Ba IEEE MLSP 2018 [paper] [code] -
Robustness of frequency division technique for online myoelectric pattern recognition against contraction-level variation B. Tolooshams and N. Jiang Frontiers in Bioengineering and Biotechnology 2017 [paper]
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Interpretable deep learning for deconvolution of multiplexed neural signals B. Tolooshams, S. Matias, H. Wu, N. Uchida, V. N. Murthy, P. Masset, and D. Ba COSYNE 2023 [paper] -
Interpretable unrolled dictionary learning networks B. Tolooshams and D. Ba DeepMath 2022 [paper] [slides] -
Unsupervised sparse deconvolutional learning of features driving neural activity B. Tolooshams, H. Wu, N. Uchida, V. N. Murthy, P. Masset, and D. Ba COSYNE 2022 [paper] [poster] -
Unsupervised learning of a dictionary of neural impulse responses from spiking data B. Tolooshams, H. Wu, P. Masset, V. N. Murthy, and D. Ba COSYNE 2021 [paper] [poster] -
Convolutional dictionary learning of stimulus from spiking data A. H. Song*, B. Tolooshams*, S. Temereanca, and D. Ba COSYNE 2020 [paper] [poster]