Ivan Dokmanic

ORCID

Publication Activity (10 Years)

Years Active: 2010-2024
Publications (10 Years): 99

Top Venues

CoRR

ICASSP

IEEE Trans. Signal Process.

ICLR

Publications

AmirEhsan Khorashadizadeh, Valentin Debarnot, Tianlin Liu, Ivan Dokmanic
GLIMPSE: Generalized Local Imaging with MLPs. CoRR (2024)
Tianlin Liu, Jose Antonio Lara Benitez, Florian Faucher, AmirEhsan Khorashadizadeh, Maarten V. de Hoop, Ivan Dokmanic
WaveBench: Benchmarking Data-driven Solvers for Linear Wave Propagation PDEs. Trans. Mach. Learn. Res. 2024 (2024)
Sidharth Gupta, Konik Kothari, Valentin Debarnot, Ivan Dokmanic
Differentiable Uncalibrated Imaging. IEEE Trans. Computational Imaging 10 (2024)
Liming Pan, Cheng Shi, Ivan Dokmanic
A Graph Dynamics Prior for Relational Inference. AAAI (2024)
Cheng Shi, Liming Pan, Ivan Dokmanic
A spring-block theory of feature learning in deep neural networks. CoRR (2024)
Shuai Huang, Mona Zehni, Ivan Dokmanic, Zhizhen Zhao
Orthogonal Matrix Retrieval with Spatial Consensus for 3D Unknown View Tomography. SIAM J. Imaging Sci. 16 (3) (2023)
Dalia El Badawy, Viktor Larsson, Marc Pollefeys, Ivan Dokmanic
Localizing Unsynchronized Sensors With Unknown Sources. IEEE Trans. Signal Process. 71 (2023)
Cheng Shi, Maarten V. de Hoop, Ivan Dokmanic
Harpa: High-Rate Phase Association with Travel Time Neural Fields. CoRR (2023)
Anastasis Kratsios, Chong Liu, Matti Lassas, Maarten V. de Hoop, Ivan Dokmanic
A Transfer Principle: Universal Approximators Between Metric Spaces From Euclidean Universal Approximators. CoRR (2023)
Liming Pan, Cheng Shi, Ivan Dokmanic
A Dynamical Graph Prior for Relational Inference. CoRR (2023)
Anastasis Kratsios, Valentin Debarnot, Ivan Dokmanic
Small Transformers Compute Universal Metric Embeddings. J. Mach. Learn. Res. 24 (2023)
AmirEhsan Khorashadizadeh, Anadi Chaman, Valentin Debarnot, Ivan Dokmanic
FunkNN: Neural Interpolation for Functional Generation. ICLR (2023)
AmirEhsan Khorashadizadeh, Konik Kothari, Leonardo Salsi, Ali Aghababaei Harandi, Maarten V. de Hoop, Ivan Dokmanic
Conditional Injective Flows for Bayesian Imaging. IEEE Trans. Computational Imaging 9 (2023)
Tin Sum Cheng, Aurélien Lucchi, Ivan Dokmanic, Anastasis Kratsios, David Belius
A Theoretical Analysis of the Test Error of Finite-Rank Kernel Ridge Regression. CoRR (2023)
Valentin Debarnot, Sidharth Gupta, Konik Kothari, Ivan Dokmanic
Joint Cryo-ET Alignment and Reconstruction with Neural Deformation Fields. ICASSP (2023)
Antoine Maillard, Afonso S. Bandeira, David Belius, Ivan Dokmanic, Shuta Nakajima
Injectivity of ReLU networks: perspectives from statistical physics. CoRR (2023)
AmirEhsan Khorashadizadeh, Sepehr Eskandari, Vahid Khorashadi-Zadeh, Ivan Dokmanic
Deep Injective Prior for Inverse Scattering. CoRR (2023)
Tin Sum Cheng, Aurélien Lucchi, Anastasis Kratsios, Ivan Dokmanic, David Belius
A Theoretical Analysis of the Test Error of Finite-Rank Kernel Ridge Regression. NeurIPS (2023)
Tianlin Liu, Anadi Chaman, David Belius, Ivan Dokmanic
Learning Multiscale Convolutional Dictionaries for Image Reconstruction. IEEE Trans. Computational Imaging 8 (2022)
Tin Vlasic, Hieu Nguyen, AmirEhsan Khorashadizadeh, Ivan Dokmanic
Implicit Neural Representation for Mesh-Free Inverse Obstacle Scattering. IEEECONF (2022)
AmirEhsan Khorashadizadeh, Ali Aghababaei Harandi, Tin Vlasic, Hieu Nguyen, Ivan Dokmanic
Deep Variational Inverse Scattering. CoRR (2022)
Cheng Shi, Liming Pan, Hong Hu, Ivan Dokmanic
Statistical Mechanics of Generalization In Graph Convolution Networks. CoRR (2022)
AmirEhsan Khorashadizadeh, Konik Kothari, Leonardo Salsi, Ali Aghababaei Harandi, Maarten V. de Hoop, Ivan Dokmanic
Conditional Injective Flows for Bayesian Imaging. CoRR (2022)
Anastasis Kratsios, Behnoosh Zamanlooy, Tianlin Liu, Ivan Dokmanic
Universal Approximation Under Constraints is Possible with Transformers. ICLR (2022)
Liming Pan, Cheng Shi, Ivan Dokmanic
Neural Link Prediction with Walk Pooling. ICLR (2022)
Shuai Huang, Mona Zehni, Ivan Dokmanic, Zhizhen Zhao
Orthogonal Matrix Retrieval with Spatial Consensus for 3D Unknown-View Tomography. CoRR (2022)
Michael Puthawala, Matti Lassas, Ivan Dokmanic, Maarten V. de Hoop
Universal Joint Approximation of Manifolds and Densities by Simple Injective Flows. ICML (2022)
Sidharth Gupta, Ivan Dokmanic
Total Least Squares Phase Retrieval. IEEE Trans. Signal Process. 70 (2022)
AmirEhsan Khorashadizadeh, Anadi Chaman, Valentin Debarnot, Ivan Dokmanic
FunkNN: Neural Interpolation for Functional Generation. CoRR (2022)
Benjamín Béjar Haro, Ivan Dokmanic, René Vidal
The Fastest $\ell _{1, \infty }$ℓ1, ∞ Prox in the West. IEEE Trans. Pattern Anal. Mach. Intell. 44 (7) (2022)
Michael Puthawala, Matti Lassas, Ivan Dokmanic, Pekka Pankka, Maarten V. de Hoop
Deep Invertible Approximation of Topologically Rich Maps between Manifolds. CoRR (2022)
Anastasis Kratsios, Valentin Debarnot, Ivan Dokmanic
Small Transformers Compute Universal Metric Embeddings. CoRR (2022)
Sidharth Gupta, Konik Kothari, Valentin Debarnot, Ivan Dokmanic
Differentiable Uncalibrated Imaging. CoRR (2022)
Tin Vlasic, Hieu Nguyen, Ivan Dokmanic
Implicit Neural Representation for Mesh-Free Inverse Obstacle Scattering. CoRR (2022)
Michael Puthawala, Konik Kothari, Matti Lassas, Ivan Dokmanic, Maarten V. de Hoop
Globally Injective ReLU Networks. J. Mach. Learn. Res. 23 (2022)
Shuai Huang, Ivan Dokmanic
Reconstructing Point Sets From Distance Distributions. IEEE Trans. Signal Process. 69 (2021)
Konik Kothari, AmirEhsan Khorashadizadeh, Maarten V. de Hoop, Ivan Dokmanic
Trumpets: Injective Flows for Inference and Inverse Problems. CoRR (2021)
Liming Pan, Cheng Shi, Ivan Dokmanic
Neural Link Prediction with Walk Pooling. CoRR (2021)
Anastasis Kratsios, Behnoosh Zamanlooy, Tianlin Liu, Ivan Dokmanic
Universal Approximation Under Constraints is Possible with Transformers. CoRR (2021)
Anadi Chaman, Ivan Dokmanic
Truly shift-equivariant convolutional neural networks with adaptive polyphase upsampling. CoRR (2021)
Konik Kothari, AmirEhsan Khorashadizadeh, Maarten V. de Hoop, Ivan Dokmanic
Trumpets: Injective flows for inference and inverse problems. UAI (2021)
Anadi Chaman, Ivan Dokmanic
Truly Shift-Equivariant Convolutional Neural Networks with Adaptive Polyphase Upsampling. ACSCC (2021)
Michael Puthawala, Matti Lassas, Ivan Dokmanic, Maarten V. de Hoop
Universal Joint Approximation of Manifolds and Densities by Simple Injective Flows. CoRR (2021)
Anadi Chaman, Ivan Dokmanic
Truly Shift-Invariant Convolutional Neural Networks. CVPR (2021)
Puoya Tabaghi, Ivan Dokmanic
On Procrustes Analysis in Hyperbolic Space. IEEE Signal Process. Lett. 28 (2021)
Puoya Tabaghi, Ivan Dokmanic, Martin Vetterli
Kinetic Euclidean Distance Matrices. IEEE Trans. Signal Process. 68 (2020)
Miranda Krekovic, Ivan Dokmanic, Martin Vetterli
Shapes From Echoes: Uniqueness From Point-to-Plane Distance Matrices. IEEE Trans. Signal Process. 68 (2020)
Shuai Huang, Sidharth Gupta, Ivan Dokmanic
Solving Complex Quadratic Systems With Full-Rank Random Matrices. IEEE Trans. Signal Process. 68 (2020)
Michael Puthawala, Konik Kothari, Matti Lassas, Ivan Dokmanic, Maarten V. de Hoop
Globally Injective ReLU Networks. CoRR (2020)
Puoya Tabaghi, Ivan Dokmanic
Geometry of Comparisons. CoRR (2020)
Puoya Tabaghi, Ivan Dokmanic
Hyperbolic Distance Matrices. CoRR (2020)
Sidharth Gupta, Rémi Gribonval, Laurent Daudet, Ivan Dokmanic
Fast Optical System Identification by Numerical Interferometry. ICASSP (2020)
Konik Kothari, Maarten V. de Hoop, Ivan Dokmanic
Learning the Geometry of Wave-Based Imaging. NeurIPS (2020)
Mona Zehni, Shuai Huang, Ivan Dokmanic, Zhizhen Zhao
3D Unknown View Tomography Via Rotation Invariants. ICASSP (2020)
Puoya Tabaghi, Ivan Dokmanic
Hyperbolic Distance Matrices. KDD (2020)
Tianlin Liu, Anadi Chaman, David Belius, Ivan Dokmanic
Interpreting U-Nets via Task-Driven Multiscale Dictionary Learning. CoRR (2020)
Anadi Chaman, Ivan Dokmanic
Truly shift-invariant convolutional neural networks. CoRR (2020)
Konik Kothari, Maarten V. de Hoop, Ivan Dokmanic
Learning the geometry of wave-based imaging. CoRR (2020)
Shuai Huang, Sidharth Gupta, Ivan Dokmanic
Solving Complex Quadratic Equations with Full-rank Random Gaussian Matrices. ICASSP (2019)
Shuai Huang, Sidharth Gupta, Ivan Dokmanic
Solving Complex Quadratic Systems with Full-Rank Random Matrices. CoRR (2019)
Anadi Chaman, Yu-Jeh Liu, Jonah Casebeer, Ivan Dokmanic
Multipath-enabled Private Audio with Noise. ICASSP (2019)
Sidharth Gupta, Rémi Gribonval, Laurent Daudet, Ivan Dokmanic
Don't take it lightly: Phasing optical random projections with unknown operators. CoRR (2019)
Puoya Tabaghi, Ivan Dokmanic, Martin Vetterli
On the Move: Localization with Kinetic Euclidean Distance Matrices. ICASSP (2019)
Ivan Dokmanic
Permutations Unlabeled Beyond Sampling Unknown. IEEE Signal Process. Lett. 26 (6) (2019)
Benjamín Béjar Haro, Ivan Dokmanic, René Vidal
prox in the west. CoRR (2019)
Mona Zehni, Shuai Huang, Ivan Dokmanic, Zhizhen Zhao
Distance retrieval from unknown view tomography of 2D point sources. Computational Imaging (2019)
Konik Kothari, Sidharth Gupta, Maarten V. de Hoop, Ivan Dokmanic
Random mesh projectors for inverse problems. ICLR (Poster) (2019)
Miranda Krekovic, Ivan Dokmanic, Martin Vetterli
Shapes from Echoes: Uniqueness from Point-to-Plane Distance Matrices. CoRR (2019)
Mona Zehni, Shuai Huang, Ivan Dokmanic, Zhizhen Zhao
Geometric Invariants for Sparse Unknown View Tomography. ICASSP (2019)
Puoya Tabaghi, Maarten V. de Hoop, Ivan Dokmanic
Learning Schatten-Von Neumann Operators. CoRR (2019)
Ivan Dokmanic, Rémi Gribonval
Concentration of the Frobenius Norm of Generalized Matrix Inverses. SIAM J. Matrix Anal. Appl. 40 (1) (2019)
Sidharth Gupta, Rémi Gribonval, Laurent Daudet, Ivan Dokmanic
Don't take it lightly: Phasing optical random projections with unknown operators. NeurIPS (2019)
Anadi Chaman, Yu-Jeh Liu, Jonah Casebeer, Ivan Dokmanic
Multipath-enabled private audio with noise. CoRR (2018)
Shuai Huang, Ivan Dokmanic
Reconstructing Point Sets from Distance Distributions. CoRR (2018)
Robin Scheibler, Diego Di Carlo, Antoine Deleforge, Ivan Dokmanic
Separake: Source Separation with a Little Help from Echoes. ICASSP (2018)
Yu-Jeh Liu, Jonah Casebeer, Ivan Dokmanic
Cocktails, but no Party: Multipath-Enabled Private Audio. IWAENC (2018)
Dalia El Badawy, Ivan Dokmanic
Direction of Arrival With One Microphone, a Few LEGOs, and Non-Negative Matrix Factorization. IEEE ACM Trans. Audio Speech Lang. Process. 26 (12) (2018)
Robin Scheibler, Eric Bezzam, Ivan Dokmanic
Pyroomacoustics: A Python Package for Audio Room Simulation and Array Processing Algorithms. ICASSP (2018)
Yu-Jeh Liu, Jonah Casebeer, Ivan Dokmanic
Cocktails, but no party: multipath-enabled private audio. CoRR (2018)
Miranda Krekovic, Gilles Baechler, Ivan Dokmanic, Martin Vetterli
Structure from Sound with Incomplete Data. ICASSP (2018)
Ivan Dokmanic, Rémi Gribonval
Concentration of the Frobenius norms of pseudoinverses. CoRR (2018)
Dalia El Badawy, Ivan Dokmanic
Direction of Arrival with One Microphone, a few LEGOs, and Non-Negative Matrix Factorization. CoRR (2018)
Sidharth Gupta, Konik Kothari, Maarten V. de Hoop, Ivan Dokmanic
Deep Mesh Projectors for Inverse Problems. CoRR (2018)
Ivan Dokmanic
Permutations Unlabeled beyond Sampling Unknown. CoRR (2018)
Ivan Dokmanic, Rémi Gribonval
Beyond Moore-Penrose Part II: The Sparse Pseudoinverse. CoRR (2017)
Dalia El Badawy, Ivan Dokmanic, Martin Vetterli
Acoustic DoA Estimation by One Unsophisticated Sensor. LVA/ICA (2017)
Robin Scheibler, Diego Di Carlo, Antoine Deleforge, Ivan Dokmanic
Separake: Source Separation with a Little Help From Echoes. CoRR (2017)
Hanjie Pan, Robin Scheibler, Eric Bezzam, Ivan Dokmanic, Martin Vetterli
FRIDA: FRI-based DOA estimation for arbitrary array layouts. ICASSP (2017)
Miranda Krekovic, Ivan Dokmanic, Martin Vetterli
Omnidirectional bats, point-to-plane distances, and the price of uniqueness. ICASSP (2017)
Ivan Dokmanic, Rémi Gribonval
Beyond Moore-Penrose Part I: Generalized Inverses that Minimize Matrix Norms. CoRR (2017)
Robin Scheibler, Eric Bezzam, Ivan Dokmanic
Pyroomacoustics: A Python package for audio room simulations and array processing algorithms. CoRR (2017)
Hanjie Pan, Robin Scheibler, Eric Bezzam, Ivan Dokmanic, Martin Vetterli
FRIDA: FRI-Based DOA Estimation for Arbitrary Array Layouts. CoRR (2016)
Miranda Krekovic, Ivan Dokmanic, Martin Vetterli
EchoSLAM: Simultaneous localization and mapping with acoustic echoes. ICASSP (2016)
Gilles Baechler, Ivan Dokmanic, Loïc Baboulaz, Martin Vetterli
Accurate recovery of a specularity from a few samples of the reflectance function. ICASSP (2016)
Miranda Krekovic, Ivan Dokmanic, Martin Vetterli
Omnidirectional Bats, Point-to-Plane Distances, and the Price of Uniqueness. CoRR (2016)
Ivan Dokmanic, Joan Bruna, Stéphane Mallat, Maarten V. de Hoop
Inverse Problems with Invariant Multiscale Statistics. CoRR (2016)
Ivan Dokmanic, Yue M. Lu
Sampling Sparse Signals on the Sphere: Algorithms and Applications. IEEE Trans. Signal Process. 64 (1) (2016)
Miranda Krekovic, Ivan Dokmanic, Martin Vetterli
Look, no Beacons! Optimal All-in-One EchoSLAM. CoRR (2016)
Ivan Dokmanic, Laurent Daudet, Martin Vetterli
From acoustic room reconstruction to slam. ICASSP (2016)
Robin Scheibler, Ivan Dokmanic, Martin Vetterli
Raking echoes in the time domain. ICASSP (2015)

Ivan Dokmanic.css-eqlu8i{font-size:small;padding-left:4px;}

Publication Activity (10 Years)

Top Topics

Top Venues

Ivan Dokmanic