Embedded Files
Prepublications
Prepublications
I. Martinović, T. Vlašić, Y. Li, B. Hooi, Y. Zhang and M. Šikić, "A Comparative Review of Deep Learning Methods for RNA Tertiary Structure Prediction," in bioRxiv:2024.11.27.625779, 2024.
Journal papers
Journal papers
RiNALMo: general-purpose RNA language models can generalize well on structure prediction tasks
RiNALMo: general-purpose RNA language models can generalize well on structure prediction tasks
RiNALMo, a large-scale RNA language model trained on non-coding RNA sequences, captures structural information and achieves state-of-the-art performance on multiple tasks, notably generalizing to unseen RNA families in secondary structure prediction.
R.J. Penić, T. Vlašić, R.G. Huber, Y. Wan and M. Šikić, "RiNALMo: general-purpose RNA language models can generalize well on structure prediction tasks," Nature Communications, vol. 16, article no. 5671, Jul. 2025, doi:10.1038/s41467-025-60872-5.
Reinterpretation of the acquisition procedure of the single-pixel camera.
Shift-invariant signal model for single-pixel compressive imaging.
A link between the theories of generalized sampling, wavelets and compressive sensing.
A matrix-free implementation that fits with the memory-efficient solvers for compressive sensing.
T. Vlašić and D. Seršić, "Single-pixel compressive imaging in shift-invariant spaces via exact wavelet frames," Signal Processing: Image Communication, vol. 105, article no. 116702, Jul. 2022, doi:10.1016/j.image.2022.116702.
Reinterpretation of the acquisition procedure of the random demodulator.
The inherently continuous inverse problem is exactly discretized into a finite-dimensional CS problem.
The signal is recovered by combining CS and the generalized reconstruction procedure in SI spaces.
A generalization to other compactly supported sampling kernels that span an SI subspace.
T. Vlašić and D. Seršić, "Sampling and reconstruction of sparse signals in shift-invariant spaces: Generalized Shannon's theorem meets compressive sensing," IEEE Transactions on Signal Processing, vol. 70, pp. 438-451, Jan. 2022, doi:10.1109/TSP.2022.3141009.
Spline-like Chebyshev Polynomial Model for Compressive Imaging
Spline-like Chebyshev Polynomial Model for Compressive Imaging
Block-based sparse polynomial approximation from compressed sensing measurements.
The spline-like model provides a desired level of smoothness.
The spline-like model avoids blocking artifacts and mitigates the Runge phenomenon.
T. Vlašić, I. Ralašić, A. Tafro and D. Seršić, "Spline-like Chebyshev polynomial model for compressive imaging," Journal of visual communication and image representation, vol. 66, article no. 102731, Jan. 2020, doi:10.1016/j.jvcir.2019.102731.
Conference papers
Conference papers
Full papers
Full papers
T. Vlašić, T. Matulić, and D. Seršić, "Estimating Uncertainty in PET Image Reconstruction via Deep Posterior Sampling," in 2023 International Conference on Medical Imaging with Deep Learning (MIDL), PMLR 227, Jul. 2023, pp. 1875-1894, paper.
A. Khorashadizadeh, A. Aghababaei, T. Vlašić, H. Nguyen, and I. Dokmanić, "Deep Variational Inverse Scattering," in Proceedings of the 2023 17th European Conference on Antennas and Propagation (EuCAP), Mar. 2023, pp. 1-5, doi:10.23919/EuCAP57121.2023.10133365.
T. Vlašić, H. Nguyen, A. Khorashadizadeh, and I. Dokmanić, "Implicit Neural Representation for Mesh-Free Inverse Obstacle Scattering," in Proceedings of the 2022 56th Asilomar Conference on Signals, Systems, and Computers, Oct. 2022, pp. 947-952, doi:10.1109/IEEECONF56349.2022.10052055.
T. Vlašić and D. Seršić, ''Multilevel Subsampling of Principal Component Projections for Adaptive Compressive Sensing," in Proceedings of the 2021 12th International Symposium on Image and Signal Processing and Analysis (ISPA), Sep. 2021, pp. 29-35, doi:10.1109/ISPA52656.2021.9552127.
T. Vlašić and D. Seršić, "Sub-Nyquist Sampling in Shift-Invariant Spaces," in Proceedings of the 2020 28th European Signal Processing Conference (EUSIPCO), Jan. 2021, pp. 2284-2288, doi:10.23919/Eusipco47968.2020.9287712.
K. Sever, T. Vlašić and D. Seršić, ''A Realization of Adaptive Compressive Sensing System," in Proceedings of the 2020 43rd International Convention on Information and Communication Technology, Electronics and Microelectronics (MIPRO), Oct. 2020, pp. 152-157, doi:10.23919/MIPRO48935.2020.9245389.
T. Vlašić, J. Ivanković, A. Tafro and D. Seršić, "Spline-Like Chebyshev Polynomial Representation for Compressed Sensing," in Proceedings of the 2019 11th International Symposium on Image and Signal Processing and Analysis (ISPA), Sep. 2019, pp. 135-140, doi:10.1109/ISPA.2019.8868926.
T. Vlašić and D. Seršić, "Image Representation and Analysis by Continuous Chebyshev Polynomials," in Proceedings of the 2019 Signal Processing Symposium (SPSympo), Sep. 2019, pp. 300-305, doi:10.1109/SPS.2019.8882089.
Extended abstracts
Extended abstracts
T. Vlašić and D. Seršić, "Statistical Compressive Sensing of Analog Signals in B-Spline Function Spaces," in Abstract Book of the 2020 5th International Workshop on Data Science (IWDS), Nov. 2020, pp. 28-31.
T. Vlašić, I. Pavić, K. Sever, L. Oštrić, V. Papa and D. Seršić, "A system for compressive sensing of analog signals," in Abstract Book of the 2019 4th International Workshop on Data Science (IWDS), Oct. 2019, pp. 16-19.
T. Vlašić, J. Ivanković, A. Tafro and D. Seršić, "Spline-Like Chebyshev Polynomial Representation for Compressed Sensing," in Abstract Book of the 2018 3rd International Workshop on Data Science (IWDS), Oct. 2018, pp. 17-19.
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