Signals, Waves, and Learning: A Data-Driven Paradigm for Wave-Based Inverse Problems

Universal Joint Approximation of Manifolds and Densities by Simple Injective Flows

Autori: Michael Puthawala, Matti Lassas, Ivan Dokmanic, Maarten De Hoop
Pubblicato in: Proceedings of the 39th International Conference on Machine Learning, 2022
Editore: PMLR

Neural Link Prediction with Walk Pooling

Autori: Liming Pan, Cheng Shi, Ivan Dokmanic
Pubblicato in: International Conference on Learning Representations, 2022
Editore: Tenth International Conference on Learning Representations

Universal Approximation Under Constraints is Possible with Transformers

Autori: Anastasis Kratsios, Behnoosh Zamanlooy, Tianlin Liu, Ivan Dokmanic
Pubblicato in: International Conference on Learning Representations, 2022
Editore: ICLR

Truly Shift-Invariant Convolutional Neural Networks

Autori: Anadi Chaman, Ivan Dokmanic
Pubblicato in: IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2021
Editore: IEEE

Trumpets: Injective Flows for Inference and Inverse Problems

Autori: Kothari, Konik; Khorashadizadeh, AmirEhsan; de Hoop, Maarten; Dokmanić, Ivan
Pubblicato in: Conference on Uncertainty in Artificial Intelligence (UAI), 2021
Editore: NA

Implicit Neural Representation for Mesh-Free Inverse Obstacle Scattering

Autori: Tin Vlašić, Hieu Nguyen, Ivan Dokmanić
Pubblicato in: Asilomar Conference on Signals, Systems, and Computers, 2022
Editore: NA

Truly shift-equivariant convolutional neural networks with adaptive polyphase upsampling

Autori: Anadi Chaman, Ivan Dokmanić
Pubblicato in: 2021 55th Asilomar Conference on Signals, Systems, and Computers, 2021
Editore: IEEE

Manifold Rewiring for Unlabeled Imaging

Autori: Valentin Debarnot, Vinith Kishore, Cheng Shi, Ivan Dokmanic
Pubblicato in: APSIPA, 2022
Editore: IEEE

Learning the Geometry of Wave-Based Imaging

Autori: Konik Kothari, Maarten de Hoop, Ivan Dokmanić
Pubblicato in: Advances in Neural Information Processing Systems, Numero 33, 2020
Editore: Advances in Neural Information Processing Systems

Learning Multiscale Convolutional Dictionaries for Image Reconstruction

Autori: Tianlin Liu, Anadi Chaman, David Belius, Ivan Dokmanić
Pubblicato in: IEEE Transactions on Computational Imaging, 2022, ISSN 2333-9403
Editore: IEEE

Learning sub-patterns in piecewise continuous functions

Autori: Kratsios, Anastasis; Zamanlooy, Behnoosh
Pubblicato in: Neurocomputing, Numero 09252312, 2022, ISSN 0925-2312
Editore: Elsevier BV
DOI: 10.1016/j.neucom.2022.01.036

Globally Injective ReLU Networks

Autori: Michael Puthawala, Konik Kothari, Matti Lassas, Ivan Dokmanić, Maarten de Hoop
Pubblicato in: Journal of Machine Learning Research, 2022, ISSN 1532-4435
Editore: MIT Press

Total Least Squares Phase Retrieval

Autori: Sidharth Gupta, Ivan Dokmanić
Pubblicato in: IEEE Transactions on Signal Processing, 2021, ISSN 1941-0476
Editore: IEEE

Conditional Injective Flows for Bayesian Imaging

Autori: AmirEhsan Khorashadizadeh, Konik Kothari, Leonardo Salsi, Ali Aghababaei Harandi, Maarten de Hoop, Ivan Dokmanić
Pubblicato in: 2022
Editore: NA

Orthogonal Matrix Retrieval with Spatial Consensus for 3D Unknown-View Tomography

Autori: Shuai Huang, Mona Zehni, Ivan Dokmanic, Zhizhen Zhao
Pubblicato in: arXiv, 2022
Editore: NA

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