{"total":17,"rows":[{"id":"2207.10758","title":"DEVIANT: Depth EquiVarIAnt NeTwork for Monocular 3D Object Detection","created":"2022-07-21","cited_by_count":48,"area":null},{"id":"2303.09803","title":"Covariance properties under natural image transformations for the generalized Gaussian derivative model for visual receptive fields","created":"2023-03-17","cited_by_count":8,"area":null},{"id":"2305.04665","title":"Riesz networks: scale invariant neural networks in a single forward pass","created":"2023-05-08","cited_by_count":8,"area":null},{"id":"2308.15461","title":"Canonical Factors for Hybrid Neural Fields","created":"2023-08-29","cited_by_count":8,"area":null},{"id":"2304.05864","title":"Scale-Equivariant Deep Learning for 3D Data","created":"2023-04-12","cited_by_count":6,"area":null},{"id":"2311.10543","title":"Unified theory for joint covariance properties under geometric image transformations for spatio-temporal receptive fields according to the generalized Gaussian derivative model for visual receptive fields","created":"2023-11-17","cited_by_count":5,"area":null},{"id":"2307.08467","title":"Riesz feature representation: scale equivariant scattering network for classification tasks","created":"2023-07-17","cited_by_count":4,"area":null},{"id":"2411.02134","title":"Optimizing Multi-Scale Representations to Detect Effect Heterogeneity Using Earth Observation and Computer Vision: Applications to Two Anti-Poverty RCTs","created":"2024-11-04","cited_by_count":4,"area":null},{"id":"2409.11140","title":"Scale generalisation properties of extended scale-covariant and scale-invariant Gaussian derivative networks on image datasets with spatial scaling variations","created":"2024-09-17","cited_by_count":3,"area":null},{"id":"2203.05006","title":"Resource-Efficient Invariant Networks: Exponential Gains by Unrolled Optimization","created":"2022-03-09","cited_by_count":2,"area":null},{"id":"2211.10288","title":"Just a Matter of Scale? Reevaluating Scale Equivariance in Convolutional Neural Networks","created":"2022-11-18","cited_by_count":2,"area":null},{"id":"2310.08803","title":"Advancing Perception in Artificial Intelligence through Principles of Cognitive Science","created":"2023-10-13","cited_by_count":1,"area":null},{"id":"2308.08884","title":"SRMAE: Masked Image Modeling for Scale-Invariant Deep Representations","created":"2023-08-17","cited_by_count":0,"area":null},{"id":"2412.03259","title":"GERD: Geometric event response data generation","created":"2024-12-04","cited_by_count":0,"area":null},{"id":"2508.00197","title":"Graph Lineages and Skeletal Graph Products","created":"2025-07-31","cited_by_count":0,"area":null},{"id":"2509.15748","title":"Hybrid Lie semi-group and cascade structures for the generalized Gaussian derivative model for visual receptive fields","created":"2025-09-19","cited_by_count":0,"area":null},{"id":"2603.02843","title":"Scale-invariant Gaussian derivative residual networks","created":"2026-03-03","cited_by_count":0,"area":null}],"next_cursor":null}