{"total":56,"rows":[{"id":"2111.11426","title":"Neural Fields in Visual Computing and Beyond","created":"2021-11-22","cited_by_count":540,"area":null},{"id":"2103.10380","title":"FastNeRF: High-Fidelity Neural Rendering at 200FPS","created":"2021-03-18","cited_by_count":511,"area":null},{"id":"2110.04126","title":"3D Infomax improves GNNs for Molecular Property Prediction","created":"2021-10-08","cited_by_count":146,"area":null},{"id":"2404.07771","title":"An Overview of Diffusion Models: Applications, Guided Generation, Statistical Rates and Optimization","created":"2024-04-11","cited_by_count":73,"area":"diffusion"},{"id":"2111.04006","title":"A Review of Location Encoding for GeoAI: Methods and Applications","created":"2021-11-07","cited_by_count":54,"area":null},{"id":"2211.13223","title":"Generalizable Implicit Neural Representations via Instance Pattern Composers","created":"2022-11-23","cited_by_count":39,"area":null},{"id":"2306.17624","title":"Sphere2Vec: A General-Purpose Location Representation Learning over a Spherical Surface for Large-Scale Geospatial Predictions","created":"2023-06-30","cited_by_count":39,"area":null},{"id":"2107.02561","title":"Rethinking Positional Encoding","created":"2021-07-06","cited_by_count":37,"area":null},{"id":"2401.07402","title":"Improved Implicit Neural Representation with Fourier Reparameterized Training","created":"2024-01-15","cited_by_count":36,"area":null},{"id":"2406.15658","title":"TorchSpatial: A Location Encoding Framework and Benchmark for Spatial Representation Learning","created":"2024-06-21","cited_by_count":31,"area":null},{"id":"2309.03160","title":"ResFields: Residual Neural Fields for Spatiotemporal Signals","created":"2023-09-06","cited_by_count":27,"area":null},{"id":"2203.08138","title":"CryoAI: Amortized Inference of Poses for Ab Initio Reconstruction of 3D Molecular Volumes from Real Cryo-EM Images","created":"2022-03-15","cited_by_count":24,"area":null},{"id":"2210.07387","title":"Amortized Inference for Heterogeneous Reconstruction in Cryo-EM","created":"2022-10-13","cited_by_count":23,"area":null},{"id":"2206.14373","title":"Theoretical Perspectives on Deep Learning Methods in Inverse Problems","created":"2022-06-29","cited_by_count":22,"area":null},{"id":"2106.01553","title":"Spline Positional Encoding for Learning 3D Implicit Signed Distance Fields","created":"2021-06-03","cited_by_count":21,"area":null},{"id":"2101.04844","title":"Reproducing Activation Function for Deep Learning","created":"2021-01-13","cited_by_count":19,"area":null},{"id":"2202.04713","title":"PINs: Progressive Implicit Networks for Multi-Scale Neural Representations","created":"2022-02-09","cited_by_count":19,"area":null},{"id":"2205.08987","title":"Trading Positional Complexity vs. Deepness in Coordinate Networks","created":"2022-05-18","cited_by_count":16,"area":null},{"id":"2406.03873","title":"Quantum Implicit Neural Representations","created":"2024-06-06","cited_by_count":16,"area":null},{"id":"2408.05526","title":"CryoBench: Diverse and challenging datasets for the heterogeneity problem in cryo-EM","created":"2024-08-10","cited_by_count":15,"area":null},{"id":"2211.06689","title":"TINC: Tree-structured Implicit Neural Compression","created":"2022-11-12","cited_by_count":14,"area":null},{"id":"2207.09663","title":"Streamable Neural Fields","created":"2022-07-20","cited_by_count":13,"area":null},{"id":"2209.15180","title":"SCI: A Spectrum Concentrated Implicit Neural Compression for Biomedical Data","created":"2022-09-30","cited_by_count":13,"area":null},{"id":"2408.14806","title":"Poly2Vec: Polymorphic Fourier-Based Encoding of Geospatial Objects for GeoAI Applications","created":"2024-08-27","cited_by_count":13,"area":null},{"id":"2302.12835","title":"Implicit neural representations for unsupervised super-resolution and denoising of 4D flow MRI","created":"2023-02-24","cited_by_count":9,"area":null},{"id":"2210.09455","title":"Track Targets by Dense Spatio-Temporal Position Encoding","created":"2022-10-17","cited_by_count":8,"area":null},{"id":"2504.00904","title":"Explorable INR: An Implicit Neural Representation for Ensemble Simulation Enabling Efficient Spatial and Parameter Exploration","created":"2025-04-01","cited_by_count":8,"area":null},{"id":"2112.11577","title":"Learning Positional Embeddings for Coordinate-MLPs","created":"2021-12-21","cited_by_count":7,"area":null},{"id":"2211.14169","title":"Latent Space Diffusion Models of Cryo-EM Structures","created":"2022-11-25","cited_by_count":6,"area":"diffusion"},{"id":"2307.03704","title":"Equivariant Single View Pose Prediction Via Induced and Restricted Representations","created":"2023-07-07","cited_by_count":6,"area":null},{"id":"2311.12151","title":"Teaching Robots to Build Simulations of Themselves","created":"2023-11-20","cited_by_count":6,"area":null},{"id":"2403.10815","title":"MicroDiffusion: Implicit Representation-Guided Diffusion for 3D Reconstruction from Limited 2D Microscopy Projections","created":"2024-03-16","cited_by_count":6,"area":"diffusion"},{"id":"2309.00339","title":"Robust Point Cloud Processing through Positional Embedding","created":"2023-09-01","cited_by_count":5,"area":null},{"id":"2410.00053","title":"Frequency-adaptive Multi-scale Deep Neural Networks","created":"2024-09-28","cited_by_count":5,"area":null},{"id":"2411.00543","title":"3D Equivariant Pose Regression via Direct Wigner-D Harmonics Prediction","created":"2024-11-01","cited_by_count":4,"area":null},{"id":"2303.16254","title":"CryoFormer: Continuous Heterogeneous Cryo-EM Reconstruction using Transformer-based Neural Representations","created":"2023-03-28","cited_by_count":3,"area":null},{"id":"2309.03320","title":"CoNeS: Conditional neural fields with shift modulation for multi-sequence MRI translation","created":"2023-09-06","cited_by_count":3,"area":null},{"id":"2503.02978","title":"Integrating Predictive and Generative Capabilities by Latent Space Design via the DKL-VAE Model","created":"2025-03-04","cited_by_count":3,"area":null},{"id":"2201.10489","title":"Sphere2Vec: Multi-Scale Representation Learning over a Spherical Surface for Geospatial Predictions","created":"2022-01-25","cited_by_count":2,"area":null},{"id":"2308.11927","title":"Recovering a Molecule's 3D Dynamics from Liquid-phase Electron Microscopy Movies","created":"2023-08-23","cited_by_count":2,"area":null},{"id":"2310.00413","title":"SSIF: Learning Continuous Image Representation for Spatial-Spectral Super-Resolution","created":"2023-09-30","cited_by_count":2,"area":null},{"id":"2406.10212","title":"NeST: Neural Stress Tensor Tomography by leveraging 3D Photoelasticity","created":"2024-06-14","cited_by_count":2,"area":null},{"id":"2412.09420","title":"Mixture of neural fields for heterogeneous reconstruction in cryo-EM","created":"2024-12-12","cited_by_count":2,"area":null},{"id":"2504.17364","title":"I-INR: Iterative Implicit Neural Representations","created":"2025-04-24","cited_by_count":2,"area":null},{"id":"2302.06091","title":"Boosted ab initio Cryo-EM 3D Reconstruction with ACE-EM","created":"2023-02-13","cited_by_count":1,"area":null},{"id":"2308.04956","title":"Improved Cryo-EM Pose Estimation and 3D Classification through Latent-Space Disentanglement","created":"2023-08-09","cited_by_count":1,"area":null},{"id":"2312.04323","title":"Equivariant Scalar Fields for Molecular Docking with Fast Fourier Transforms","created":"2023-12-07","cited_by_count":1,"area":null},{"id":"2407.01574","title":"cryoSPHERE: Single-particle heterogeneous reconstruction from cryo EM","created":"2024-05-29","cited_by_count":1,"area":null},{"id":"2512.06332","title":"CryoHype: Reconstructing a thousand cryo-EM structures with transformer-based hypernetworks","created":"2025-12-06","cited_by_count":1,"area":null},{"id":"2311.16148","title":"Univariate Radial Basis Function Layers: Brain-inspired Deep Neural Layers for Low-Dimensional Inputs","created":"2023-11-07","cited_by_count":0,"area":null},{"id":"2312.02124","title":"VerA: Versatile Anonymization Applicable to Clinical Facial Photographs","created":"2023-12-04","cited_by_count":0,"area":null},{"id":"2410.12522","title":"MING: A Functional Approach to Learning Molecular Generative Models","created":"2024-10-16","cited_by_count":0,"area":null},{"id":"2506.21165","title":"Topology-Aware Modeling for Unsupervised Simulation-to-Reality Point Cloud Recognition","created":"2025-06-26","cited_by_count":0,"area":null},{"id":"2507.18603","title":"Demystify Protein Generation with Hierarchical Conditional Diffusion Models","created":"2025-07-24","cited_by_count":0,"area":"diffusion"},{"id":"2508.15198","title":"Frequency-adaptive tensor neural networks for high-dimensional multi-scale problems","created":"2025-08-21","cited_by_count":0,"area":null},{"id":"2509.07373","title":"SBS: Enhancing Parameter-Efficiency of Neural Representations for Neural Networks via Spectral Bias Suppression","created":"2025-09-09","cited_by_count":0,"area":null}],"next_cursor":null}