Romero, David W.

14 publications

CVPR 2025 HMAR: Efficient Hierarchical Masked Auto-Regressive Image Generation Hermann Kumbong, Xian Liu, Tsung-Yi Lin, Ming-Yu Liu, Xihui Liu, Ziwei Liu, Daniel Y. Fu, Christopher Re, David W. Romero

ICLR 2024 Fast, Expressive $\mathrm{SE}(n)$ Equivariant Networks Through Weight-Sharing in Position-Orientation Space Erik J Bekkers, Sharvaree Vadgama, Rob Hesselink, Putri A Van der Linden, David W. Romero

ICMLW 2024 Self-Supervised Detection of Perfect and Partial Input-Dependent Symmetries Alonso Urbano, David W. Romero

TMLR 2024 Wavelet Networks: Scale-Translation Equivariant Learning from Raw Time-Series David W. Romero, Erik J Bekkers, Jakub M. Tomczak, Mark Hoogendoorn

ICMLW 2023 DNArch: Learning Convolutional Neural Architectures by Backpropagation David W. Romero, Neil Zeghidour

ICMLW 2023 Learned Gridification for Efficient Point Cloud Processing Putri A Van der Linden, David W. Romero, Erik J Bekkers

ICLR 2023 Modelling Long Range Dependencies in $N$D: From Task-Specific to a General Purpose CNN David M Knigge, David W. Romero, Albert Gu, Efstratios Gavves, Erik J Bekkers, Jakub Mikolaj Tomczak, Mark Hoogendoorn, Jan-jakob Sonke

ICLR 2022 CKConv: Continuous Kernel Convolution for Sequential Data David W. Romero, Anna Kuzina, Erik J Bekkers, Jakub Mikolaj Tomczak, Mark Hoogendoorn

ICML 2022 Exploiting Redundancy: Separable Group Convolutional Networks on Lie Groups David M. Knigge, David W Romero, Erik J Bekkers

ICLR 2022 FlexConv: Continuous Kernel Convolutions with Differentiable Kernel Sizes David W. Romero, Robert-Jan Bruintjes, Jakub Mikolaj Tomczak, Erik J Bekkers, Mark Hoogendoorn, Jan van Gemert

NeurIPS 2022 Learning Partial Equivariances from Data David W. Romero, Suhas Lohit

NeurIPS 2022 Relaxing Equivariance Constraints with Non-Stationary Continuous Filters Tycho van der Ouderaa, David W. Romero, Mark van der Wilk

ICLR 2021 Group Equivariant Stand-Alone Self-Attention for Vision David W. Romero, Jean-Baptiste Cordonnier

ICLR 2020 Co-Attentive Equivariant Neural Networks: Focusing Equivariance on Transformations Co-Occurring in Data David W. Romero, Mark Hoogendoorn