Publications

Wiatowski T, Grohs P, Bölcskei H. Topology Reduction in Deep Convolutional Feature Extraction Networks. In Lu YM, Van De Ville D, Van De Ville D, Papadakis M, editors, Wavelets and Sparsity XVII: 6-9 August 2017, San Diego, California, United States. Bellingham, Washington: SPIE. 2017. 1039418. (Proceedings of SPIE, Vol. 10394). doi: 10.1117/12.2271761

Alaifari R, Daubechies I, Grohs P, Thakur G. Reconstructing real-valued functions from unsigned coefficients with respect to wavelet and other frames. Journal of Fourier Analysis and Applications. 2016 Dec 1;23(6):1480-1494. doi: 10.1007/s00041-016-9513-7

Grohs P, Kutyniok G, Keiper S. Cartoon Approximation with α -Curvelets. Journal of Fourier Analysis and Applications. 2016 Dec;22(6):1235-1293. doi: 10.1007/s00041-015-9446-6

Grohs P, Sprecher M. Total variation regularization on Riemannian manifolds by iteratively reweighted minimization. Information and Inference: An IMA Interdisciplinary Journal. 2016 Dec;5(4):353-378. doi: 10.1093/imaiai/iaw011

Grohs P, Obermeier A. Optimal Adaptive Ridgelet Schemes for Linear Advection Equations. Applied and Computational Harmonic Analysis. 2016 Nov;41(3):768-814. doi: 10.1016/j.acha.2015.06.003

Grohs P, Keiper S, Kutyniok G, Schaefer M. α-Molecules. Applied and Computational Harmonic Analysis. 2016 Jul;41(1):297-336. doi: 10.1016/j.acha.2015.10.009

Grohs P, Hosseini S. Nonsmooth Trust Region Algorithms for Locally Lipschitz Functions on Riemannian Manifolds. IMA Journal of Numerical Analysis. 2016 Jul;36(3):1167-1192. doi: 10.1093/imanum/drv043

Grohs P, Hosseini S. ε-subgradient algorithms for locally lipschitz functions on Riemannian manifolds. Advances in Computational Mathematics. 2016 Apr;42(2):333-360. doi: 10.1007/s10444-015-9426-z

Grohs P, Kereta Z, Wiesmann U. A Shearlet-Based Fast Thresholded Landweber Algorithm for Deconvolution. International Journal of Wavelets, Multiresolution and Information Processing. 2016;14(5):1650032. doi: 10.1142/S0219691316500326

Grohs P, Wiatowski T, Boelcskei H. Deep Convolutional Neural Networks on Cartoon Functions. 2016. Paper presented at IEEE symposium on Information Theory, Barcelona, Spain.

Wiatowski T, Tschannen M, Stanic A, Grohs P, Boelcskei H. Discrete Deep Feature Extraction: A Theory and New Architectures. Proceedings of Machine Learning Research. 2016;48:2149-2158.

Dahlke S, (ed.), DeMari F, (ed.), Grohs P, (ed.), Labate D, (ed.). Harmonic And Applied Analysis: From Groups to Signals. Cham: Springer, 2016. 256 p. (Applied and Numerical Harmonic Analysis).

Grohs P, Hardering H, Sander O. Optimal A Priori Discretization Error Bounds for Geodesic Finite Elements. Foundations of Computational Mathematics. 2015 Dec;15(6):1357-1411. doi: 10.1007/s10208-014-9230-z

Fonn E, Grohs P, Hiptmair R. Hyperbolic Cross Approximation for the Spatially Homogeneous Boltzmann Equation. IMA Journal of Numerical Analysis. 2015 Oct;35(4):1533-1567. doi: 10.1093/imanum/dru042

Grohs P. Wolfowitz's Theorem and Convergence of Consensus Algorithms in Hadamard Spaces. Proceedings of the American Mathematical Society. 2015 Oct;143(10):4403-4414. doi: 10.1090/proc/12513

Grohs P, Vigogna S. Intrinsic Localization of Anisotropic Frames II: $\alpha$-Molecules. Journal of Fourier Analysis and Applications. 2015 Feb;21(1):182-205. doi: 10.1007/s00041-014-9365-y

Etter S, Grohs P, Obermeier A. FFRT - A Fast Finite Ridgelet Transform for Radiative Transport. Multiscale Modeling & Simulation: a S I A M interdisciplinary journal. 2015;13(1):1-42. doi: 10.1137/140977722

Grohs P, Obermeier A. Ridgelet methods for linear transport equations. 2015. Paper presented at 8th International Conference: CURVES and SURFACES, Paris, France.

Grohs P, Kutyniok G. Parabolic Molecules. Foundations of Computational Mathematics. 2014 Apr;14(2):299-337. doi: 10.1007/s10208-013-9170-z

Grohs P, Keiper S, Kutyniok G, Schaefer M. Parabolic molecules: Curvelets, shearlets and beyond. In Fasshauer GE, Schumaker LL, editors, Approximation Theory XIV: San Antonio 2013. Cham: Springer. 2014. p. 141-172. (Springer Proceedings in Mathematics & Statistics, Vol. 83). doi: 10.1007/978-3-319-06404-8_9