{"total":64,"rows":[{"id":"2301.12780","title":"Equivariant Architectures for Learning in Deep Weight Spaces","created":"2023-01-30","cited_by_count":107,"area":null},{"id":"2403.12143","title":"Graph Neural Networks for Learning Equivariant Representations of Neural Networks","created":"2024-03-18","cited_by_count":59,"area":null},{"id":"2402.13144","title":"Neural Network Diffusion","created":"2024-02-20","cited_by_count":42,"area":"diffusion"},{"id":"2406.09997","title":"Towards Scalable and Versatile Weight Space Learning","created":"2024-06-14","cited_by_count":41,"area":null},{"id":"2402.18153","title":"Diffusion-Based Neural Network Weights Generation","created":"2024-02-28","cited_by_count":39,"area":null},{"id":"2303.04143","title":"Can We Scale Transformers to Predict Parameters of Diverse ImageNet Models?","created":"2023-03-07","cited_by_count":29,"area":null},{"id":"2503.10633","title":"We Should Chart an Atlas of All the World's Models","created":"2025-03-13","cited_by_count":25,"area":null},{"id":"2406.10685","title":"Scale Equivariant Graph Metanetworks","created":"2024-06-15","cited_by_count":24,"area":null},{"id":"2402.14335","title":"HyperFast: Instant Classification for Tabular Data","created":"2024-02-22","cited_by_count":22,"area":null},{"id":"2306.16830","title":"Sampling weights of deep neural networks","created":"2023-06-29","cited_by_count":20,"area":null},{"id":"2409.11697","title":"Monomial Matrix Group Equivariant Neural Functional Networks","created":"2024-09-18","cited_by_count":17,"area":null},{"id":"2408.01415","title":"Conditional LoRA Parameter Generation","created":"2024-08-02","cited_by_count":16,"area":null},{"id":"2402.11922","title":"Spatio-Temporal Few-Shot Learning via Diffusive Neural Network Generation","created":"2024-02-19","cited_by_count":15,"area":"diffusion"},{"id":"2501.11587","title":"Recurrent Diffusion for Large-Scale Parameter Generation","created":"2025-01-20","cited_by_count":15,"area":null},{"id":"2407.10973","title":"Make-An-Agent: A Generalizable Policy Network Generator with Behavior-Prompted Diffusion","created":"2024-07-15","cited_by_count":14,"area":null},{"id":"2506.07998","title":"Generative Modeling of Weights: Generalization or Memorization?","created":"2025-06-09","cited_by_count":14,"area":null},{"id":"2405.18432","title":"Unsupervised Model Tree Heritage Recovery","created":"2024-05-28","cited_by_count":11,"area":null},{"id":"2210.08942","title":"Meta-Learning via Classifier(-free) Diffusion Guidance","created":"2022-10-17","cited_by_count":9,"area":"diffusion"},{"id":"2410.13569","title":"Learning on Model Weights using Tree Experts","created":"2024-10-17","cited_by_count":7,"area":null},{"id":"2502.14401","title":"MedFuncta: A Unified Framework for Learning Efficient Medical Neural Fields","created":"2025-02-20","cited_by_count":7,"area":null},{"id":"2507.01649","title":"GradMetaNet: An Equivariant Architecture for Learning on Gradients","created":"2025-07-02","cited_by_count":7,"area":null},{"id":"2503.17138","title":"Structure Is Not Enough: Leveraging Behavior for Neural Network Weight Reconstruction","created":"2025-03-21","cited_by_count":6,"area":null},{"id":"2503.24354","title":"ORAL: Prompting Your Large-Scale LoRAs via Conditional Recurrent Diffusion","created":"2025-03-31","cited_by_count":6,"area":null},{"id":"2605.18632","title":"Position: Weight Space Should Be a First-Class Generative AI Modality","created":"2026-05-18","cited_by_count":6,"area":null},{"id":"2508.13633","title":"Text2Weight: Bridging Natural Language and Neural Network Weight Spaces","created":"2025-08-19","cited_by_count":5,"area":null},{"id":"2601.05052","title":"DeepWeightFlow: Re-Basined Flow Matching for Generating Neural Network Weights","created":"2026-01-08","cited_by_count":5,"area":"diffusion"},{"id":"2304.13718","title":"Sparsified Model Zoo Twins: Investigating Populations of Sparsified Neural Network Models","created":"2023-04-26","cited_by_count":4,"area":null},{"id":"2410.11878","title":"Neural Metamorphosis","created":"2024-10-10","cited_by_count":4,"area":null},{"id":"2502.01117","title":"Learning to Learn Weight Generation via Local Consistency Diffusion","created":"2025-02-03","cited_by_count":4,"area":null},{"id":"2509.22566","title":"From Parameters to Behaviors: Unsupervised Compression of the Policy Space","created":"2025-09-26","cited_by_count":4,"area":"deeprl"},{"id":"2510.02096","title":"Learning Model Representations Using Publicly Available Model Hubs","created":"2025-10-02","cited_by_count":4,"area":null},{"id":"2504.02012","title":"Instruction-Guided Autoregressive Neural Network Parameter Generation","created":"2025-04-02","cited_by_count":3,"area":null},{"id":"2504.10231","title":"A Model Zoo of Vision Transformers","created":"2025-04-14","cited_by_count":3,"area":null},{"id":"2504.18072","title":"A Model Zoo on Phase Transitions in Neural Networks","created":"2025-04-25","cited_by_count":3,"area":null},{"id":"2405.12521","title":"Unleash Graph Neural Networks from Heavy Tuning","created":"2024-05-21","cited_by_count":2,"area":null},{"id":"2501.01601","title":"Few-shot Implicit Function Generation via Equivariance","created":"2025-01-03","cited_by_count":2,"area":null},{"id":"2504.15323","title":"HyperFlow: Gradient-Free Emulation of Few-Shot Fine-Tuning","created":"2025-04-21","cited_by_count":2,"area":null},{"id":"2508.12673","title":"Deploying Models to Non-participating Clients in Federated Learning without Fine-tuning: A Hypernetwork-based Approach","created":"2025-08-18","cited_by_count":2,"area":null},{"id":"2602.16316","title":"A Graph Meta-Network for Learning on Kolmogorov-Arnold Networks","created":"2026-02-18","cited_by_count":2,"area":null},{"id":"2603.07506","title":"A Unified Framework for Knowledge Transfer in Bidirectional Model Scaling","created":"2026-03-08","cited_by_count":2,"area":null},{"id":"2603.23408","title":"GeoSANE: Learning Geospatial Representations from Models, Not Data","created":"2026-03-24","cited_by_count":2,"area":null},{"id":"2605.23410","title":"What Linear Probes Miss: Multi-View Probing for Weight-Space Learning","created":"2026-05-22","cited_by_count":2,"area":"mechinterp"},{"id":"2410.05697","title":"Diffusing to the Top: Boost Graph Neural Networks with Minimal Hyperparameter Tuning","created":"2024-10-08","cited_by_count":1,"area":null},{"id":"2504.10141","title":"The Impact of Model Zoo Size and Composition on Weight Space Learning","created":"2025-04-14","cited_by_count":1,"area":null},{"id":"2511.15941","title":"iLTM: Integrated Large Tabular Model","created":"2025-11-20","cited_by_count":1,"area":null},{"id":"2601.22157","title":"Discovering Hidden Gems in Model Repositories","created":"2026-01-29","cited_by_count":1,"area":null},{"id":"2601.23181","title":"Ensuring Semantics in Weights of Implicit Neural Representations through the Implicit Function Theorem","created":"2026-01-30","cited_by_count":1,"area":null},{"id":"2603.27044","title":"Unsupervised Behavioral Compression: Learning Low-Dimensional Policy Manifolds through State-Occupancy Matching","created":"2026-03-27","cited_by_count":1,"area":"deeprl"},{"id":"2604.12096","title":"LLM-HYPER: Generative CTR Modeling for Cold-Start Ad Personalization via LLM-Based Hypernetworks","created":"2026-04-13","cited_by_count":1,"area":null},{"id":"2604.23720","title":"Quasi-Equivariant Metanetworks","created":"2026-04-26","cited_by_count":1,"area":null},{"id":"2605.07075","title":"ModelLens: Finding the Best for Your Task from Myriads of Models","created":"2026-05-08","cited_by_count":1,"area":null},{"id":"2606.07217","title":"Robotic Policy Adaptation via Weight-Space Meta-Learning","created":"2026-06-05","cited_by_count":1,"area":"vla"},{"id":"2607.02166","title":"Dynamic Neural Graph Encoding of Inference Processes in Deep Weight Space","created":"2026-07-02","cited_by_count":1,"area":null},{"id":"2410.05107","title":"Hyper-Representations: Learning from Populations of Neural Networks","created":"2024-10-07","cited_by_count":0,"area":null},{"id":"2507.15869","title":"An open dataset of neural networks for hypernetwork research","created":"2025-07-15","cited_by_count":0,"area":null},{"id":"2512.15469","title":"Metanetworks as Regulatory Operators: Learning to Edit for Requirement Compliance","created":"2025-12-17","cited_by_count":0,"area":null},{"id":"2605.08281","title":"Is Class Signal Clustered or Routed in Task-Induced Implicit Neural Representation Weight Spaces?","created":"2026-05-08","cited_by_count":0,"area":null},{"id":"2606.04754","title":"Beyond Structural Symmetries: Linear Mode Connectivity via Neuron Identifiability","created":"2026-06-03","cited_by_count":0,"area":null},{"id":"2607.03551","title":"WeightCLIP: Aligning Datasets and Models for Weight Space Learning","created":"2026-07-03","cited_by_count":0,"area":null},{"id":"2607.07743","title":"Architecture Generalization with MetaNCA","created":"2026-07-08","cited_by_count":0,"area":null},{"id":"2609.03495","title":"Spectral characteristics of autoencoder parameters as a vector representation of data","created":"2026-09-03","cited_by_count":0,"area":null},{"id":"2609.32833","title":"Permutation-Equivariant Flow Matching for Alignment-Free Neural Weight Generation","created":"2026-09-26","cited_by_count":0,"area":"diffusion"},{"id":"2610.01649","title":"CrossGMN: Graph Metanetworks for Cross-Architecture Weight-Space Transformations","created":"2026-10-01","cited_by_count":0,"area":null},{"id":"2610.16484","title":"SLEM: Spectrum-Aware Latent Expert Merging","created":"2026-10-12","cited_by_count":0,"area":"moe"}],"next_cursor":null}