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2020 – today
- 2024
[j2]Paul Hueber
, Guangzhi Tang, Manolis Sifalakis, Hua-Peng Liaw, Aurora Micheli, Nergis Tomen, Yao-Hong Liu:
Benchmarking of hardware-efficient real-time neural decoding in brain-computer interfaces. Neuromorph. Comput. Eng. 4(2): 24008 (2024)- [i13]Nergis Tomen, Silvia L. Pintea, Jan C. van Gemert:
Deep Continuous Networks. CoRR abs/2402.01557 (2024) - [i12]Robert-Jan Bruintjes, Attila Lengyel, Marcos Baptista-Ríos, Osman Semih Kayhan, Davide Zambrano, Nergis Tomen, Jan van Gemert:
VIPriors 4: Visual Inductive Priors for Data-Efficient Deep Learning Challenges. CoRR abs/2406.18176 (2024) - [i11]Hesam Araghi, Jan van Gemert, Nergis Tomen:
Pushing the boundaries of event subsampling in event-based video classification using CNNs. CoRR abs/2409.08953 (2024) - [i10]Aurora Micheli, Olaf Booij, Jan van Gemert, Nergis Tömen:
Deep activity propagation via weight initialization in spiking neural networks. CoRR abs/2410.00580 (2024) - [i9]Alejandro Castañeda Garcia, Jan van Gemert, Daan Brinks, Nergis Tömen:
Learning Physics From Video: Unsupervised Physical Parameter Estimation for Continuous Dynamical Systems. CoRR abs/2410.01376 (2024) - 2023
- [i8]Robert-Jan Bruintjes, Attila Lengyel, Marcos Baptista-Ríos, Osman Semih Kayhan, Davide Zambrano, Nergis Tomen, Jan van Gemert:
VIPriors 3: Visual Inductive Priors for Data-Efficient Deep Learning Challenges. CoRR abs/2305.19688 (2023) - 2022
- [c7]Liang Zeng, Attila Lengyel, Nergis Tomen, Jan C. van Gemert:
Copy-Pasting Coherent Depth Regions Improves Contrastive Learning for Urban-Scene Segmentation. BMVC 2022: 893 - [c6]Yeshwanth Napolean, Anwesh Marwade, Nergis Tomen, Puck Alkemade, Thijs Eijsvogels, Jan C. van Gemert:
Heart rate estimation in intense exercise videos. ICIP 2022: 3933-3937 - [i7]Attila Lengyel, Robert-Jan Bruintjes, Marcos Baptista-Ríos, Osman Semih Kayhan, Davide Zambrano, Nergis Tomen, Jan van Gemert:
VIPriors 2: Visual Inductive Priors for Data-Efficient Deep Learning Challenges. CoRR abs/2201.08625 (2022) - [i6]Yeshwanth Napolean, Anwesh Marwade, Nergis Tomen, Puck Alkemade, Thijs Eijsvogels, Jan van Gemert:
Heart rate estimation in intense exercise videos. CoRR abs/2208.02509 (2022) - [i5]Liang Zeng, Attila Lengyel, Nergis Tömen, Jan van Gemert:
Copy-Pasting Coherent Depth Regions Improves Contrastive Learning for Urban-Scene Segmentation. CoRR abs/2211.14074 (2022) - 2021
- [j1]Silvia L. Pintea, Nergis Tomen, Stanley F. Goes, Marco Loog, Jan C. van Gemert:
Resolution Learning in Deep Convolutional Networks Using Scale-Space Theory. IEEE Trans. Image Process. 30: 8342-8353 (2021) - [c5]Nikhil Saldanha, Silvia-Laura Pintea, Jan C. van Gemert, Nergis Tomen:
Frequency learning for structured CNN filters with Gaussian fractional derivatives. BMVC 2021: 210 - [c4]Nergis Tomen, Jan C. van Gemert:
Spectral Leakage and Rethinking the Kernel Size in CNNs. ICCV 2021: 5118-5127 - [c3]Nergis Tomen, Silvia-Laura Pintea, Jan van Gemert:
Deep Continuous Networks. ICML 2021: 10324-10335 - [i4]Nergis Tomen, Jan van Gemert:
Spectral Leakage and Rethinking the Kernel Size in CNNs. CoRR abs/2101.10143 (2021) - [i3]Silvia L. Pintea, Nergis Tomen, Stanley F. Goes, Marco Loog, Jan C. van Gemert:
Resolution learning in deep convolutional networks using scale-space theory. CoRR abs/2106.03412 (2021) - [i2]Nikhil Saldanha, Silvia L. Pintea, Jan C. van Gemert, Nergis Tomen:
Frequency learning for structured CNN filters with Gaussian fractional derivatives. CoRR abs/2111.06660 (2021) - 2020
- [c2]Ioannis Lelekas, Nergis Tomen, Silvia L. Pintea, Jan C. van Gemert:
Top-Down Networks: A coarse-to-fine reimagination of CNNs. CVPR Workshops 2020: 3244-3253 - [i1]Ioannis Lelekas, Nergis Tomen, Silvia L. Pintea, Jan C. van Gemert:
Top-Down Networks: A coarse-to-fine reimagination of CNNs. CoRR abs/2004.07629 (2020)
2010 – 2019
- 2012
- [c1]Nergis Tomen, Makoto Takemiya, Takeshi Matsuo, Isao Hasegawa, Yukiyasu Kamitani:
Feature Space Quantization for Data-Driven Search. PRNI 2012: 41-44
Coauthor Index
aka: Jan van Gemert
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last updated on 2024-11-11 21:25 CET by the dblp team
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