{"total":65,"rows":[{"id":"2301.12780","title":"Equivariant Architectures for Learning in Deep Weight Spaces","created":"2023-01-30","cited_by_count":107,"area":null},{"id":"2302.14040","title":"Permutation Equivariant Neural Functionals","created":"2023-02-27","cited_by_count":70,"area":null},{"id":"2106.05961","title":"What Does Rotation Prediction Tell Us about Classifier Accuracy under Varying Testing Environments?","created":"2021-06-10","cited_by_count":63,"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":"2312.04501","title":"Graph Metanetworks for Processing Diverse Neural Architectures","created":"2023-12-07","cited_by_count":57,"area":null},{"id":"2305.13546","title":"Neural Functional Transformers","created":"2023-05-22","cited_by_count":52,"area":null},{"id":"2406.09997","title":"Towards Scalable and Versatile Weight Space Learning","created":"2024-06-14","cited_by_count":41,"area":null},{"id":"2209.14764","title":"Model Zoos: A Dataset of Diverse Populations of Neural Network Models","created":"2022-09-29","cited_by_count":39,"area":null},{"id":"2310.13397","title":"Equivariant Deep Weight Space Alignment","created":"2023-10-20","cited_by_count":35,"area":null},{"id":"2410.11163","title":"Model Swarms: Collaborative Search to Adapt LLM Experts via Swarm Intelligence","created":"2024-10-15","cited_by_count":27,"area":null},{"id":"2402.05232","title":"Universal Neural Functionals","created":"2024-02-07","cited_by_count":26,"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":"2402.04081","title":"Improved Generalization of Weight Space Networks via Augmentations","created":"2024-02-06","cited_by_count":24,"area":null},{"id":"2406.10685","title":"Scale Equivariant Graph Metanetworks","created":"2024-06-15","cited_by_count":24,"area":null},{"id":"2207.07065","title":"On the Strong Correlation Between Model Invariance and Generalization","created":"2022-07-14","cited_by_count":23,"area":null},{"id":"2302.01094","title":"Confidence and Dispersity Speak: Characterising Prediction Matrix for Unsupervised Accuracy Estimation","created":"2023-02-02","cited_by_count":22,"area":null},{"id":"2410.04207","title":"Learning on LoRAs: GL-Equivariant Processing of Low-Rank Weight Spaces for Large Finetuned Models","created":"2024-10-05","cited_by_count":22,"area":null},{"id":"2405.20231","title":"The Empirical Impact of Neural Parameter Symmetries, or Lack Thereof","created":"2024-05-30","cited_by_count":21,"area":null},{"id":"2409.11697","title":"Monomial Matrix Group Equivariant Neural Functional Networks","created":"2024-09-18","cited_by_count":17,"area":null},{"id":"2410.10811","title":"Deep Linear Probe Generators for Weight Space Learning","created":"2024-10-14","cited_by_count":17,"area":"mechinterp"},{"id":"2403.11998","title":"Learning Useful Representations of Recurrent Neural Network Weight Matrices","created":"2024-03-18","cited_by_count":15,"area":null},{"id":"2212.06925","title":"On the Relationship Between Explanation and Prediction: A Causal View","created":"2022-12-13","cited_by_count":14,"area":null},{"id":"2312.10531","title":"How to Train Neural Field Representations: A Comprehensive Study and Benchmark","created":"2023-12-16","cited_by_count":12,"area":null},{"id":"2405.18432","title":"Unsupervised Model Tree Heritage Recovery","created":"2024-05-28","cited_by_count":11,"area":null},{"id":"2110.15288","title":"Hyper-Representations: Self-Supervised Representation Learning on Neural Network Weights for Model Characteristic Prediction","created":"2021-10-28","cited_by_count":9,"area":null},{"id":"2410.09841","title":"Symmetry Discovery for Different Data Types","created":"2024-10-13","cited_by_count":8,"area":null},{"id":"2410.13569","title":"Learning on Model Weights using Tree Experts","created":"2024-10-17","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":"2406.18630","title":"Improving Hyperparameter Optimization with Checkpointed Model Weights","created":"2024-06-26","cited_by_count":5,"area":null},{"id":"2603.15990","title":"W2T: LoRA Weights Already Know What They Can Do","created":"2026-03-16","cited_by_count":5,"area":null},{"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":"2412.08010","title":"Quantum-Cognitive Neural Networks: Assessing Confidence and Uncertainty with Human Decision-Making Simulations","created":"2024-12-11","cited_by_count":4,"area":null},{"id":"2510.02096","title":"Learning Model Representations Using Publicly Available Model Hubs","created":"2025-10-02","cited_by_count":4,"area":null},{"id":"2503.15156","title":"ARC: Anchored Representation Clouds for High-Resolution INR Classification","created":"2025-03-19","cited_by_count":3,"area":null},{"id":"2503.17140","title":"Adiabatic Fine-Tuning of Neural Quantum States Enables Detection of Phase Transitions in Weight Space","created":"2025-03-21","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":"2201.11105","title":"Do You See What I See? Capabilities and Limits of Automated Multimedia Content Analysis","created":"2021-12-15","cited_by_count":2,"area":null},{"id":"2311.08851","title":"Data Augmentations in Deep Weight Spaces","created":"2023-11-15","cited_by_count":2,"area":null},{"id":"2403.02327","title":"Model Lakes","created":"2024-03-04","cited_by_count":2,"area":null},{"id":"2506.00741","title":"Data Swarms: Optimizable Generation of Synthetic Evaluation Data","created":"2025-05-31","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":"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":"2502.20314","title":"Adversarial Attacks in Weight-Space Classifiers","created":"2025-02-27","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":"2601.22157","title":"Discovering Hidden Gems in Model Repositories","created":"2026-01-29","cited_by_count":1,"area":null},{"id":"2604.23720","title":"Quasi-Equivariant Metanetworks","created":"2026-04-26","cited_by_count":1,"area":null},{"id":"2311.11717","title":"Can we infer the presence of Differential Privacy in Deep Learning models' weights? Towards more secure Deep Learning","created":"2023-11-20","cited_by_count":0,"area":null},{"id":"2503.04074","title":"Can We Optimize Deep RL Policy Weights as Trajectory Modeling?","created":"2025-03-06","cited_by_count":0,"area":"deeprl"},{"id":"2507.15869","title":"An open dataset of neural networks for hypernetwork research","created":"2025-07-15","cited_by_count":0,"area":null},{"id":"2509.26261","title":"Why is topology hard to learn?","created":"2025-09-30","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":"2604.14815","title":"Continued Pretraining of FinBERT on Finnish Histopathological Reports: Train-Time Signals and Proxy Downstream Correlations","created":"2026-04-16","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":"2606.30384","title":"Scalar Representations of Neural Network Training Dynamics","created":"2026-06-29","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":"2609.32833","title":"Permutation-Equivariant Flow Matching for Alignment-Free Neural Weight Generation","created":"2026-09-26","cited_by_count":0,"area":"diffusion"},{"id":"2609.35261","title":"Imprint Reader: From Weight-Update Readout to Behavioral Intervention","created":"2026-09-28","cited_by_count":0,"area":null},{"id":"2610.01649","title":"CrossGMN: Graph Metanetworks for Cross-Architecture Weight-Space Transformations","created":"2026-10-01","cited_by_count":0,"area":null},{"id":"2610.03000","title":"Temporal Geometry of Deep Networks: Hyperbolic Representations of Training Dynamics for Intrinsic Explainability","created":"2026-10-02","cited_by_count":0,"area":null},{"id":"2610.04489","title":"DV-Lens: Revealing the Functional Organization of Language Model Parameters","created":"2026-10-03","cited_by_count":0,"area":null},{"id":"2610.18471","title":"Graph Metanetworks for Transformer Based Language Models","created":"2026-10-13","cited_by_count":0,"area":null}],"next_cursor":null}