preprints & working papers

  1. Contextual Explanation Networks. Al-Shedivat, M., Dubey, A., Xing, E.P. In submission, 2017 [abstract] [arXiv] [pdf]
  2. Learning Scalable Deep Kernels with Recurrent Structure. Al-Shedivat, M., Wilson, A.G., Saatchi, Y., Hu, Z., Xing, E.P. Accepted to Journal of Machine Learning Research (JMLR), in revision, 2017 [abstract] [arXiv] [code]

conference & journal articles

2016

  1. [NIPS]
    Learning HMMs with Nonparametric Emissions via Decompositions of Continuous Matrices. Al-Shedivat, M.*, Kandasamy, K.*, Xing, E.P. In Proceedings of the Advances in Neural Information Processing Systems (NIPS) 2016 [abstract] [pdf] [poster] [code]
  2. [ICML]
    ADIOS: Architectures Deep In Output Space. Cissé, M., Al-Shedivat, M., Bengio, S. In Proceedings of The 33rd International Conference on Machine Learning, 2016 [abstract] [pdf] [supp] [poster] [code]
  3. [Frontiers]
    Stochastic Synapses Enable Efficient Brain-Inspired Learning Machines. Neftci, E.O., Pedroni, B.U., Joshi, S., Al-Shedivat, M., Cauwenberghs, G. Frontiers in Neuroscience, 2016 [abstract] [html] [pdf]

2015

  1. [TNANO]
    Stochasticity Modeling in Memristors. Naous, R., Al-Shedivat, M., Salama, K.N. IEEE Transactions on Nanotechnology, 2015 [abstract] [pdf]
  2. [JETCAS]
    Memristors Empower Spiking Neurons With Stochasticity. Al-Shedivat, M., Naous, R., Cauwenberghs, G., Salama, K. N. IEEE Journal on Emerging and Selected Topics in Circuits and Systems, 2015 [abstract] [pdf]
  3. [NER]
    Inherently Stochastic Spiking Neurons for Probabilistic Neural Computation. Al-Shedivat, M., Naous, R., Neftci, E., Cauwenberghs, G., Salama, K. N. In Proceedings of The 7th International IEEE/EMBS Conference on Neural Engineering, 2015 [abstract] [pdf] [poster]
  4. [ICLR]
    Learning Non-deterministic Representations with Energy-based Ensembles. Al-Shedivat, M., Neftci, E., Cauwenberghs, G. In Proceedings of The 3rd International Conference on Learning Representations, workshop track, 2015 [abstract] [pdf] [code]

2014

  1. [AAAI]
    Supervised Transfer Sparse Coding. Al-Shedivat, M., Wang, J. J.-Y., Alzahrani, M., Huang, J. Z., Gao, X. In Twenty-eighth AAAI conference on Artificial Intelligence, 2014 [abstract] [pdf] [poster] [code]

conference & workshop abstracts

  1. [NIPS]
    Scalable GP-LSTMs with Semi-Stochastic Gradients. Al-Shedivat, M., Wilson, A.G., Saatchi, Y., Hu, Z., Xing, E.P. In Bayesian Deep Learning workshop, NIPS, 2016 [arXiv] [poster] [code]
  2. [Cosyne]
    Neural Generative Models with Stochastic Synapses Capture Richer Representations. Al-Shedivat, M., Neftci, E., Cauwenberghs, G. In Computational and Systems Neuroscience (Cosyne), 2015 [pdf] [poster] [code]
  3. [FiO/LS]
    Shaping of Femtosecond Laser Pulses with Plasmonic Crystals. Shcherbakov, M., Vabishchevich, P., Zubjuk, V., Al-Shedivat, M., Dolgova, T., Fedyanin, A. In Frontiers in Optics, 2013

theses

  1. [M.Sc.]
    Al-Shedivat, M. “Brain-Inspired Stochastic Models and Implementations.” KAUST, 2015. [pdf]
  2. [B.Sc.]
    Аль-Шедиват, Маруан. “Фемтосекундная Динамика Преобразования Поляризации Света Хиральными Плазмонными Метаматериалами.” МГУ им. М.В. Ломоносова, 2013. [pdf]