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ORCIDDavid W. Romero
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2020 – today
- 2025
[i20]Jerome Ku, Eric Nguyen, David W. Romero, Garyk Brixi, Brandon Yang, Anton Vorontsov, Ali Taghibakhshi, Amy X. Lu, Dave P. Burke, Greg Brockman, Stefano Massaroli, Christopher Ré, Patrick D. Hsu, Brian L. Hie, Stefano Ermon, Michael Poli:
Systems and Algorithms for Convolutional Multi-Hybrid Language Models at Scale. CoRR abs/2503.01868 (2025)- [i19]Alisson G. Azzolini, Hannah Brandon, Prithvijit Chattopadhyay, Huayu Chen, Jinju Chu, Yin Cui, Jenna Diamond, Yifan Ding, Francesco Ferroni, Rama Govindaraju, Jinwei Gu, Siddharth Gururani, Imad El Hanafi, Zekun Hao, Jacob Samuel Huffman, Jingyi Jin, Brendan Johnson, Rizwan Khan, George Kurian, Elena Lantz, Nayeon Lee, Zhaoshuo Li, Xuan Li, Tsung-Yi Lin, Yen-Chen Lin, Ming-Yu Liu, Alice Luo, Andrew Mathau, Yun Ni, Lindsey Pavao, Wei Ping, David W. Romero, Misha Smelyanskiy, Shuran Song, Lyne Tchapmi, Andrew Z. Wang, Boxin Wang, Haoxiang Wang, Fangyin Wei, Jiashu Xu, Yao Xu, Xiaodong Yang, Zhuolin Yang, Xiaohui Zeng, Zhe Zhang:
Cosmos-Reason1: From Physical Common Sense To Embodied Reasoning. CoRR abs/2503.15558 (2025) - 2024
- [j1]David W. Romero, Erik J. Bekkers, Jakub M. Tomczak, Mark Hoogendoorn:
Wavelet Networks: Scale-Translation Equivariant Learning From Raw Time-Series. Trans. Mach. Learn. Res. 2024 (2024) - [c13]Erik J. Bekkers, Sharvaree P. Vadgama, Rob Hesselink, Putri A. van der Linden, David W. Romero:
Fast, Expressive SE(n) Equivariant Networks through Weight-Sharing in Position-Orientation Space. ICLR 2024 - [i18]David W. Romero:
The Good, The Efficient and the Inductive Biases: Exploring Efficiency in Deep Learning Through the Use of Inductive Biases. CoRR abs/2411.09827 (2024) - [i17]Zekun Hao, David W. Romero, Tsung-Yi Lin, Ming-Yu Liu:
Meshtron: High-Fidelity, Artist-Like 3D Mesh Generation at Scale. CoRR abs/2412.09548 (2024) - 2023
- [c12]David M. Knigge, David W. Romero, Albert Gu, Efstratios Gavves, Erik J. Bekkers
, Jakub Mikolaj Tomczak, Mark Hoogendoorn, Jan-Jakob Sonke:
Modelling Long Range Dependencies in $N$D: From Task-Specific to a General Purpose CNN. ICLR 2023 - [c11]Stefano Massaroli, Michael Poli, Daniel Y. Fu, Hermann Kumbong, Rom N. Parnichkun, David W. Romero, Aman Timalsina, Quinn McIntyre, Beidi Chen, Atri Rudra, Ce Zhang, Christopher Ré, Stefano Ermon, Yoshua Bengio:
Laughing Hyena Distillery: Extracting Compact Recurrences From Convolutions. NeurIPS 2023 - [c10]Putri A. van der Linden, David W. Romero, Erik J. Bekkers:
Learned Gridification for Efficient Point Cloud Processing. TAG-ML 2023: 9-20 - [i16]David M. Knigge, David W. Romero, Albert Gu, Efstratios Gavves, Erik J. Bekkers, Jakub M. Tomczak, Mark Hoogendoorn, Jan-Jakob Sonke:
Modelling Long Range Dependencies in N-D: From Task-Specific to a General Purpose CNN. CoRR abs/2301.10540 (2023) - [i15]David W. Romero, Neil Zeghidour:
DNArch: Learning Convolutional Neural Architectures by Backpropagation. CoRR abs/2302.05400 (2023) - [i14]Putri A. van der Linden, David W. Romero, Erik J. Bekkers:
Learned Gridification for Efficient Point Cloud Processing. CoRR abs/2307.14354 (2023) - [i13]Erik J. Bekkers, Sharvaree P. Vadgama, Rob D. Hesselink, Putri A. van der Linden, David W. Romero:
Fast, Expressive SE(n) Equivariant Networks through Weight-Sharing in Position-Orientation Space. CoRR abs/2310.02970 (2023) - [i12]Stefano Massaroli, Michael Poli, Daniel Y. Fu, Hermann Kumbong, Rom N. Parnichkun, Aman Timalsina, David W. Romero, Quinn McIntyre, Beidi Chen, Atri Rudra, Ce Zhang, Christopher Ré, Stefano Ermon, Yoshua Bengio:
Laughing Hyena Distillery: Extracting Compact Recurrences From Convolutions. CoRR abs/2310.18780 (2023) - [i11]Alonso Urbano, David W. Romero:
Self-Supervised Detection of Perfect and Partial Input-Dependent Symmetries. CoRR abs/2312.12223 (2023) - 2022
- [c9]David W. Romero, Robert-Jan Bruintjes, Jakub Mikolaj Tomczak, Erik J. Bekkers, Mark Hoogendoorn, Jan van Gemert:
FlexConv: Continuous Kernel Convolutions With Differentiable Kernel Sizes. ICLR 2022 - [c8]David W. Romero, Anna Kuzina, Erik J. Bekkers, Jakub Mikolaj Tomczak, Mark Hoogendoorn:
CKConv: Continuous Kernel Convolution For Sequential Data. ICLR 2022 - [c7]David M. Knigge, David W. Romero, Erik J. Bekkers:
Exploiting Redundancy: Separable Group Convolutional Networks on Lie Groups. ICML 2022: 11359-11386 - [c6]Tycho F. A. van der Ouderaa, David W. Romero, Mark van der Wilk:
Relaxing Equivariance Constraints with Non-stationary Continuous Filters. NeurIPS 2022 - [c5]David W. Romero, Suhas Lohit:
Learning Partial Equivariances From Data. NeurIPS 2022 - [i10]Tycho F. A. van der Ouderaa, David W. Romero, Mark van der Wilk:
Relaxing Equivariance Constraints with Non-stationary Continuous Filters. CoRR abs/2204.07178 (2022) - [i9]David W. Romero, David M. Knigge, Albert Gu, Erik J. Bekkers, Efstratios Gavves, Jakub M. Tomczak, Mark Hoogendoorn:
Towards a General Purpose CNN for Long Range Dependencies in ND. CoRR abs/2206.03398 (2022) - 2021
- [c4]David W. Romero, Jean-Baptiste Cordonnier:
Group Equivariant Stand-Alone Self-Attention For Vision. ICLR 2021 - [i8]David W. Romero, Anna Kuzina, Erik J. Bekkers, Jakub M. Tomczak, Mark Hoogendoorn:
CKConv: Continuous Kernel Convolution For Sequential Data. CoRR abs/2102.02611 (2021) - [i7]David W. Romero, Robert-Jan Bruintjes, Jakub M. Tomczak, Erik J. Bekkers, Mark Hoogendoorn, Jan C. van Gemert:
FlexConv: Continuous Kernel Convolutions with Differentiable Kernel Sizes. CoRR abs/2110.08059 (2021) - [i6]David W. Romero, Suhas Lohit:
Learning Equivariances and Partial Equivariances from Data. CoRR abs/2110.10211 (2021) - [i5]David M. Knigge, David W. Romero, Erik J. Bekkers:
Exploiting Redundancy: Separable Group Convolutional Networks on Lie Groups. CoRR abs/2110.13059 (2021) - 2020
- [c3]David W. Romero, Mark Hoogendoorn:
Co-Attentive Equivariant Neural Networks: Focusing Equivariance On Transformations Co-Occurring in Data. ICLR 2020 - [c2]David W. Romero, Erik J. Bekkers, Jakub M. Tomczak, Mark Hoogendoorn:
Attentive Group Equivariant Convolutional Networks. ICML 2020: 8188-8199 - [c1]Alessandro Zonta, Ali el Hassouni, David W. Romero, Jakub M. Tomczak:
Generative Fourier-Based Auto-encoders: Preliminary Results. LOD (2) 2020: 12-15 - [i4]David W. Romero, Erik J. Bekkers, Jakub M. Tomczak, Mark Hoogendoorn:
Attentive Group Equivariant Convolutional Networks. CoRR abs/2002.03830 (2020) - [i3]David W. Romero, Erik J. Bekkers, Jakub M. Tomczak, Mark Hoogendoorn:
Wavelet Networks: Scale Equivariant Learning From Raw Waveforms. CoRR abs/2006.05259 (2020) - [i2]David W. Romero, Jean-Baptiste Cordonnier:
Group Equivariant Stand-Alone Self-Attention For Vision. CoRR abs/2010.00977 (2020)
2010 – 2019
- 2019
- [i1]David W. Romero, Mark Hoogendoorn:
Co-Attentive Equivariant Neural Networks: Focusing Equivariance On Transformations Co-Occurring In Data. CoRR abs/1911.07849 (2019)
Coauthor Index
aka: Jakub Mikolaj Tomczak
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