Recent Submissions

  • Algorithmic Differentiation of the Open CASCADE Technology CAD Kernel and its coupling with an Adjoint CFD Solver 

    MUELLER, J; banovic, M; MYKHASKIV, O; AURIEMMA, S; Walther, A; Legrand, H (Taylor and Francis, 2018-02)
    Computer-aided design (CAD) tools are extensively used to design industrial components, however, contrary to e.g. computational fluid dynamics (CFD) solvers, shape sensitivities for gradient-based optimization of ...
  • Threshold Saturation of Spatially Coupled Sparse Superposition Codes for All Memoryless Channels 

    BARBIER, JFE; Dia, M; Macris, N; Information Theory Worshop (ITW) (IEEE, 2016-10)
    We recently proved threshold saturation for spatially coupled sparse superposition codes on the additive white Gaussian noise channel [1]. Here we generalize our analysis to a much broader setting. We show for any memoryless ...
  • The Mutual Information in Random Linear Estimation 

    BARBIER, JFE; Dia, M; Macris, N; Krzakala, F; 54th Annual Allerton Conference on Communication, Control, and Computing (IEEE, 2017-02)
    We consider the estimation of a signal from the knowledge of its noisy linear random Gaussian projections, a problem relevant in compressed sensing, sparse superposition codes or code division multiple access just to cite ...
  • The Layered Structure of Tensor Estimation and its Mutual Information 

    BARBIER, JFE; Macris, N; Miolane, L; 55th Annual Allerton Conference on Communication, Control, and Computing (arXiv, 2017-10)
    We consider rank-one non-symmetric tensor esti- mation and derive simple formulas for the mutual information. We start by the order 2 problem, namely matrix factorization. We treat it completely in a simpler fashion than ...
  • Mutual information for symmetric rank-one matrix estimation: A proof of the replica formula 

    BARBIER, JFE; Macris, N; Dia, M; Krzakala, F; Zdeborova, L; Lesieur, T; Neural Information Processing conference (NIPS) (Neural Information Processing conference (NIPS), 2016)
    Factorizing low-rank matrices has many applications in machine learning and statistics. For probabilistic models in the Bayes optimal setting, a general expression for the mutual information has been proposed using heuristic ...
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