{"total":25,"rows":[{"id":"2203.05482","title":"Model soups: averaging weights of multiple fine-tuned models improves accuracy without increasing inference time","created":"2022-03-10","cited_by_count":1396,"area":null},{"id":"2408.07666","title":"Model Merging in LLMs, MLLMs, and Beyond: Methods, Theories, Applications and Opportunities","created":"2024-08-14","cited_by_count":254,"area":null},{"id":"2305.03053","title":"ZipIt! Merging Models from Different Tasks without Training","created":"2023-05-04","cited_by_count":179,"area":null},{"id":"2106.11642","title":"Repulsive Deep Ensembles are Bayesian","created":"2021-06-22","cited_by_count":104,"area":null},{"id":"2102.10472","title":"Learning Neural Network Subspaces","created":"2021-02-20","cited_by_count":82,"area":null},{"id":"2103.04047","title":"Reinforcement Learning, Bit by Bit","created":"2021-03-06","cited_by_count":59,"area":null},{"id":"2103.01133","title":"Posterior Meta-Replay for Continual Learning","created":"2021-03-01","cited_by_count":45,"area":null},{"id":"2106.14568","title":"Deep Ensembling with No Overhead for either Training or Testing: The All-Round Blessings of Dynamic Sparsity","created":"2021-06-28","cited_by_count":44,"area":null},{"id":"2307.08114","title":"Tangent Model Composition for Ensembling and Continual Fine-tuning","created":"2023-07-16","cited_by_count":25,"area":null},{"id":"2306.10460","title":"Instant Soup: Cheap Pruning Ensembles in A Single Pass Can Draw Lottery Tickets from Large Models","created":"2023-06-18","cited_by_count":20,"area":null},{"id":"2306.16788","title":"Sparse Model Soups: A Recipe for Improved Pruning via Model Averaging","created":"2023-06-29","cited_by_count":20,"area":null},{"id":"2410.10801","title":"Mix Data or Merge Models? Optimizing for Diverse Multi-Task Learning","created":"2024-10-14","cited_by_count":17,"area":null},{"id":"2111.13280","title":"Efficient Self-Ensemble for Semantic Segmentation","created":"2021-11-26","cited_by_count":16,"area":null},{"id":"2407.13771","title":"Training-Free Model Merging for Multi-target Domain Adaptation","created":"2024-07-18","cited_by_count":15,"area":null},{"id":"2202.11782","title":"Prune and Tune Ensembles: Low-Cost Ensemble Learning With Sparse Independent Subnetworks","created":"2022-02-23","cited_by_count":13,"area":null},{"id":"2206.10915","title":"Understanding the effect of sparsity on neural networks robustness","created":"2022-06-22","cited_by_count":8,"area":null},{"id":"2210.09818","title":"Disentangling the Predictive Variance of Deep Ensembles through the Neural Tangent Kernel","created":"2022-10-18","cited_by_count":4,"area":null},{"id":"2303.12992","title":"A Survey of Historical Learning: Learning Models with Learning History","created":"2023-03-23","cited_by_count":3,"area":null},{"id":"2404.08079","title":"DIMAT: Decentralized Iterative Merging-And-Training for Deep Learning Models","created":"2024-04-11","cited_by_count":3,"area":null},{"id":"2406.08423","title":"State Soup: In-Context Skill Learning, Retrieval and Mixing","created":"2024-06-12","cited_by_count":3,"area":null},{"id":"2309.08610","title":"Do the Frankenstein, or how to achieve better out-of-distribution performance with manifold mixing model soup","created":"2023-08-28","cited_by_count":1,"area":null},{"id":"2505.21857","title":"Optimizable Model Averaging for Ensembling Heterogeneous Model Outputs","created":"2025-05-28","cited_by_count":1,"area":null},{"id":"2309.13752","title":"Improving Robustness of Deep Convolutional Neural Networks via Multiresolution Learning","created":"2023-09-24","cited_by_count":0,"area":null},{"id":"2606.25087","title":"Neural Network Quantization by Learning Low-Loss Subspaces","created":"2026-06-23","cited_by_count":0,"area":null},{"id":"2609.24328","title":"A Distributional Optimisation Perspective on Combining Models in Deep Learning","created":"2026-09-21","cited_by_count":0,"area":null}],"next_cursor":null}