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ORCIDDino Oglic
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Books and Theses
- 2018
[b1]Dino Oglic:
Constructive Approximation and Learning by Greedy Algorithms. University of Bonn, Germany, 2018
Journal Articles
- 2022
- [j3]Dino Oglic
, Zoran Cvetkovic, Peter Sollich, Steve Renals, Bin Yu:
Towards Robust Waveform-Based Acoustic Models. IEEE ACM Trans. Audio Speech Lang. Process. 30: 1977-1992 (2022) - 2021
- [j2]Zhu Li, Jean-Francois Ton, Dino Oglic, Dino Sejdinovic:
Towards a Unified Analysis of Random Fourier Features. J. Mach. Learn. Res. 22: 108:1-108:51 (2021) - [j1]Dino Oglic, Zoran Cvetkovic, Peter Sollich:
Learning Waveform-Based Acoustic Models Using Deep Variational Convolutional Neural Networks. IEEE ACM Trans. Audio Speech Lang. Process. 29: 2850-2863 (2021)
Conference and Workshop Papers
- 2024
- [c11]Talip Ucar, Aubin Ramon, Dino Oglic, Rebecca Croasdale-Wood, Tom Diethe, Pietro Sormanni:
Improving Antibody Humanness Prediction using Patent Data. ICML 2024 - 2022
- [c10]David Buterez, Jon Paul Janet, Steven J. Kiddle, Dino Oglic, Pietro Liò:
Graph Neural Networks with Adaptive Readouts. NeurIPS 2022 - 2020
- [c9]Dino Oglic, Zoran Cvetkovic, Peter Bell, Steve Renals:
A Deep 2D Convolutional Network for Waveform-Based Speech Recognition. INTERSPEECH 2020: 1654-1658 - [c8]Neethu M. Joy, Dino Oglic, Zoran Cvetkovic, Peter Bell, Steve Renals:
Deep Scattering Power Spectrum Features for Robust Speech Recognition. INTERSPEECH 2020: 1673-1677 - 2019
- [c7]Zhu Li, Jean-Francois Ton, Dino Oglic, Dino Sejdinovic:
Towards a Unified Analysis of Random Fourier Features. ICML 2019: 3905-3914 - [c6]Dino Oglic, Thomas Gärtner:
Scalable Learning in Reproducing Kernel Krein Spaces. ICML 2019: 4912-4921 - 2018
- [c5]Dino Oglic, Thomas Gärtner:
Learning in Reproducing Kernel Krein Spaces. ICML 2018: 3856-3864 - 2017
- [c4]Dino Oglic, Roman Garnett, Thomas Gärtner:
Active Search in Intensionally Specified Structured Spaces. AAAI 2017: 2443-2449 - [c3]Dino Oglic, Thomas Gärtner:
Nyström Method with Kernel K-means++ Samples as Landmarks. ICML 2017: 2652-2660 - 2016
- [c2]Dino Oglic, Thomas Gärtner:
Greedy Feature Construction. NIPS 2016: 3945-3953 - 2014
- [c1]Dino Oglic, Daniel Paurat, Thomas Gärtner:
Interactive Knowledge-Based Kernel PCA. ECML/PKDD (2) 2014: 501-516
Informal and Other Publications
- 2024
- [i8]Talip Ucar, Aubin Ramon, Dino Oglic, Rebecca Croasdale-Wood, Tom Diethe, Pietro Sormanni:
Improving Antibody Humanness Prediction using Patent Data. CoRR abs/2401.14442 (2024) - [i7]David Buterez, Jon Paul Janet, Dino Oglic, Pietro Lio:
Masked Attention is All You Need for Graphs. CoRR abs/2402.10793 (2024) - [i6]Alexandra Gessner, Sebastian W. Ober, Owen Vickery, Dino Oglic, Talip Uçar:
Active learning for affinity prediction of antibodies. CoRR abs/2406.07263 (2024) - 2022
- [i5]David Buterez, Jon Paul Janet, Steven J. Kiddle, Dino Oglic, Pietro Liò:
Graph Neural Networks with Adaptive Readouts. CoRR abs/2211.04952 (2022) - 2021
- [i4]Dino Oglic, Zoran Cvetkovic, Peter Sollich, Steve Renals, Bin Yu:
Towards Robust Waveform-Based Acoustic Models. CoRR abs/2110.08634 (2021) - 2019
- [i3]Dino Oglic, Zoran Cvetkovic, Peter Sollich:
Parzen Filters for Spectral Decomposition of Signals. CoRR abs/1906.09526 (2019) - 2018
- [i2]Zhu Li, Jean-Francois Ton, Dino Oglic, Dino Sejdinovic:
A Unified Analysis of Random Fourier Features. CoRR abs/1806.09178 (2018) - [i1]Dino Oglic, Thomas Gärtner:
Large Scale Learning with Kreĭn Kernels. CoRR abs/1809.02157 (2018)
Coauthor Index
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