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

Attentive Group Equivariant Convolutional Networks. - David W. Romero; Erik J. Bekkers; Jakub M. Tomczak; Mark Hoogendoorn (ICML, 2020)

Generative Fourier-Based Auto-encoders: Preliminary Results. - Alessandro Zonta; Ali el Hassouni; David W. Romero; Jakub M. Tomczak (LOD (2), 2020)

Wavelet Networks: Scale Equivariant Learning From Raw Waveforms. - David W. Romero; Erik J. Bekkers; Jakub M. Tomczak; Mark Hoogendoorn (CoRR, 2020)

Group Equivariant Stand-Alone Self-Attention For Vision. - David W. Romero; Jean-Baptiste Cordonnier (ICLR, 2021)

Learning Equivariances and Partial Equivariances from Data. - David W. Romero; Suhas Lohit (CoRR, 2021)

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 (ICLR, 2022)

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

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

Relaxing Equivariance Constraints with Non-stationary Continuous Filters. - Tycho F. A. van der Ouderaa; David W. Romero; Mark van der Wilk (NeurIPS, 2022)

Learning Partial Equivariances From Data. - David W. Romero; Suhas Lohit (NeurIPS, 2022)

Towards a General Purpose CNN for Long Range Dependencies in ND. - David W. Romero; David M. Knigge; Albert Gu; Erik J. Bekkers; Efstratios Gavves; Jakub M. Tomczak; Mark Hoogendoorn (CoRR, 2022)

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, 2023)

Laughing Hyena Distillery: Extracting Compact Recurrences From Convolutions. - 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 001; Christopher Ré; et al. (NeurIPS, 2023)

Learned Gridification for Efficient Point Cloud Processing. - Putri A. van der Linden; David W. Romero; Erik J. Bekkers (TAG-ML, 2023)

DNArch: Learning Convolutional Neural Architectures by Backpropagation. - David W. Romero; Neil Zeghidour (CoRR, 2023)

Self-Supervised Detection of Perfect and Partial Input-Dependent Symmetries. - Alonso Urbano; David W. Romero (CoRR, 2023)

Fast, Expressive SE(n) Equivariant Networks through Weight-Sharing in Position-Orientation Space. - Erik J. Bekkers; Sharvaree P. Vadgama; Rob Hesselink; Putri A. van der Linden; David W. Romero (ICLR, 2024)

The Good, The Efficient and the Inductive Biases: Exploring Efficiency in Deep Learning Through the Use of Inductive Biases. - David W. Romero (CoRR, 2024)

Meshtron: High-Fidelity, Artist-Like 3D Mesh Generation at Scale. - Zekun Hao; David W. Romero; Tsung-Yi Lin; Ming-Yu Liu 001 (CoRR, 2024)

Wavelet Networks: Scale-Translation Equivariant Learning From Raw Time-Series. - David W. Romero; Erik J. Bekkers; Jakub M. Tomczak; Mark Hoogendoorn (Trans. Mach. Learn. Res., 2024)

HMAR: Efficient Hierarchical Masked Auto-Regressive Image Generation. - Hermann Kumbong; Xian Liu; Tsung-Yi Lin; Ming-Yu Liu 001; Xihui Liu; Ziwei Liu; Daniel Y. Fu; Christopher Ré; David W. Romero (CVPR, 2025)

Systems and Algorithms for Convolutional Multi-Hybrid Language Models at Scale. - 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; et al. (CoRR, 2025)

Cosmos-Reason1: From Physical Common Sense To Embodied Reasoning. - 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; et al. (CoRR, 2025)

RECON: Robust symmetry discovery via Explicit Canonical Orientation Normalization. - Alonso Urbano; David W. Romero; Max Zimmer; Sebastian Pokutta (CoRR, 2025)