Tijmen Blankevoort
Tijmen Blankevoort
Qualcomm AI Research
Verified email at qti.qualcomm.com
Title
Cited by
Cited by
Year
Data-free quantization through weight equalization and bias correction
M Nagel, M Baalen, T Blankevoort, M Welling
Proceedings of the IEEE/CVF International Conference on Computer Vision …, 2019
1762019
Relaxed quantization for discretized neural networks
C Louizos, M Reisser, T Blankevoort, E Gavves, M Welling
arXiv preprint arXiv:1810.01875, 2018
962018
Up or down? adaptive rounding for post-training quantization
M Nagel, RA Amjad, M Van Baalen, C Louizos, T Blankevoort
International Conference on Machine Learning, 7197-7206, 2020
432020
Conditional channel gated networks for task-aware continual learning
D Abati, J Tomczak, T Blankevoort, S Calderara, R Cucchiara, ...
Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern …, 2020
412020
Lsq+: Improving low-bit quantization through learnable offsets and better initialization
Y Bhalgat, J Lee, M Nagel, T Blankevoort, N Kwak
Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern …, 2020
312020
Batch-shaping for learning conditional channel gated networks
BE Bejnordi, T Blankevoort, M Welling
arXiv preprint arXiv:1907.06627, 2019
302019
Bayesian bits: Unifying quantization and pruning
M van Baalen, C Louizos, M Nagel, RA Amjad, Y Wang, T Blankevoort, ...
arXiv preprint arXiv:2005.07093, 2020
232020
Gradient Regularization for Quantization Robustness
M Alizadeh, A Behboodi, M van Baalen, C Louizos, T Blankevoort, ...
arXiv preprint arXiv:2002.07520, 2020
212020
Differentiable joint pruning and quantization for hardware efficiency
Y Wang, Y Lu, T Blankevoort
European Conference on Computer Vision, 259-277, 2020
112020
Taxonomy and evaluation of structured compression of convolutional neural networks
A Kuzmin, M Nagel, S Pitre, S Pendyam, T Blankevoort, M Welling
arXiv preprint arXiv:1912.09802, 2019
112019
Learned threshold pruning
K Azarian, Y Bhalgat, J Lee, T Blankevoort
arXiv preprint arXiv:2003.00075, 2020
92020
A White Paper on Neural Network Quantization
M Nagel, M Fournarakis, RA Amjad, Y Bondarenko, M van Baalen, ...
arXiv preprint arXiv:2106.08295, 2021
52021
Distilling optimal neural networks: Rapid search in diverse spaces
B Moons, P Noorzad, A Skliar, G Mariani, D Mehta, C Lott, T Blankevoort
Proceedings of the IEEE/CVF International Conference on Computer Vision …, 2021
52021
Continuous relaxation of quantization for discretized deep neural networks
C Louizos, M Reisser, TPF Blankevoort, M Welling
US Patent App. 16/413,535, 2019
32019
Understanding and overcoming the challenges of efficient transformer quantization
Y Bondarenko, M Nagel, T Blankevoort
arXiv preprint arXiv:2109.12948, 2021
12021
Analytic And Empirical Correction Of Biased Error Introduced By Approximation Methods
MW Van Baalen, TPF Blankevoort, M Nagel
US Patent App. 16/826,472, 2020
12020
Semi-structured learned threshold pruning for deep neural networks
KA Yazdi, TPF Blankevoort, JW Lee, YS Bhalgat
US Patent App. 17/168,101, 2021
2021
Conditional Computation For Continual Learning
D Abati, BE Bejnordi, JM Tomczak, TPF Blankevoort
US Patent App. 17/097,811, 2021
2021
Learned threshold pruning for deep neural networks
KA Yazdi, TPF Blankevoort, JW Lee, YS Bhalgat
US Patent App. 17/067,233, 2021
2021
Joint pruning and quantization scheme for deep neural networks
Y Lu, Y Wang, TPF Blankevoort, C Louizos, M Reisser, J Hou
US Patent App. 17/030,315, 2021
2021
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