SENSE-Lab Paper Discussion Group (Summer 2018)

Presented Papers (Updated Weekly)

1. Xin, B., Wang, Y., Gao, W., Wipf, D. and Wang, B., 2016. Maximal sparsity with deep networks?. In Advances in Neural Information Processing Systems (pp. 4340-4348). Goto Meeting 1 page here
2. Patel, A.B., Nguyen, M.T. and Baraniuk, R., 2016. A probabilistic framework for deep learning. In Advances in neural information processing systems (pp. 2558-2566). link

Tentative Paper List for SU18

1. Patel, A.B., Nguyen, M.T. and Baraniuk, R., 2016. A probabilistic framework for deep learning. In Advances in neural information processing systems (pp. 2558-2566). link
2. Mousavi, A. and Baraniuk, R.G., 2017, March. Learning to invert: Signal recovery via deep convolutional networks. In Acoustics, Speech and Signal Processing (ICASSP), 2017 IEEE International Conference on (pp. 2272-2276). IEEE link
3. Samuel, N., Diskin, T. and Wiesel, A., 2017. Deep MIMO detection. arXiv preprint arXiv:1706.01151. link
4. Chang, J.R., Li, C.L., Poczos, B., Kumar, B.V. and Sankaranarayanan, A.C., 2017. One network to solve them all—solving linear inverse problems using deep projection models. arXiv preprint. link
5. Achille, A. and Soatto, S., 2017. On the emergence of invariance and disentangling in deep representations. arXiv preprint arXiv:1706.01350. link
6. Oktay, O., Ferrante, E., Kamnitsas, K., Heinrich, M., Bai, W., Caballero, J., Cook, S.A., de Marvao, A., Dawes, T., O‘Regan, D.P. and Kainz, B., 2018. Anatomically constrained neural networks (ACNNs): application to cardiac image enhancement and segmentation. IEEE transactions on medical imaging, 37(2), pp.384-395. link
7. Schlemper, J., Caballero, J., Hajnal, J.V., Price, A.N. and Rueckert, D., 2018. A deep cascade of convolutional neural networks for dynamic MR image reconstruction. IEEE transactions on Medical Imaging, 37(2), pp.491-503. link
8. Xin, B., Wang, Y., Gao, W., Wipf, D. and Wang, B., 2016. Maximal sparsity with deep networks?. In Advances in Neural Information Processing Systems (pp. 4340-4348). - link
9. He, H., Xin, B., Ikehata, S. and Wipf, D., 2017. From Bayesian Sparsity to Gated Recurrent Nets. In Advances in Neural Information Processing Systems (pp. 5560-5570). - link
10. Giryes, R., Eldar, Y.C., Bronstein, A. and Sapiro, G., 2018. Tradeoffs between convergence speed and reconstruction accuracy in inverse problems. IEEE Transactions on Signal Processing. - link
11. Azizan, N. and Hassibi, B., 2018. Stochastic Gradient/Mirror Descent: Minimax Optimality and Implicit Regularization. arXiv preprint arXiv:1806.00952. - link
12. Jin, K.H., McCann, M.T., Froustey, E. and Unser, M., 2017. Deep convolutional neural network for inverse problems in imaging. IEEE Transactions on Image Processing, 26(9), pp.4509-4522. - link
13. Ye, J.C., Han, Y. and Cha, E., 2018. Deep convolutional framelets: A general deep learning framework for inverse problems. SIAM Journal on Imaging Sciences, 11(2), pp.991-1048. - link
14. Chung, J., Kastner, K., Dinh, L., Goel, K., Courville, A.C. and Bengio, Y., 2015. A recurrent latent variable model for sequential data. In Advances in neural information processing systems (pp. 2980-2988). - link
15. Fraccaro, M., Sønderby, S.K., Paquet, U. and Winther, O., 2016. Sequential neural models with stochastic layers. In Advances in neural information processing systems (pp. 2199-2207). - link
16. Kalchbrenner, N., Espeholt, L., Simonyan, K., Oord, A.V.D., Graves, A. and Kavukcuoglu, K., 2016. Neural machine translation in linear time. arXiv preprint arXiv:1610.10099. - link

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