{"total":13,"rows":[{"id":"2209.12951","title":"Liquid Structural State-Space Models","created":"2022-09-26","cited_by_count":136,"area":null},{"id":"2106.13898","title":"Closed-form Continuous-time Neural Models","created":"2021-06-25","cited_by_count":65,"area":null},{"id":"2107.11442","title":"Compressing Neural Networks: Towards Determining the Optimal Layer-wise Decomposition","created":"2021-07-23","cited_by_count":33,"area":null},{"id":"2210.04763","title":"On the Forward Invariance of Neural ODEs","created":"2022-10-10","cited_by_count":11,"area":null},{"id":"2210.14672","title":"Sparsity in Continuous-Depth Neural Networks","created":"2022-10-26","cited_by_count":10,"area":null},{"id":"2206.01261","title":"Entangled Residual Mappings","created":"2022-06-02","cited_by_count":4,"area":null},{"id":"2404.16890","title":"Layer Collapse Can be Induced by Unstructured Pruning","created":"2024-04-24","cited_by_count":4,"area":null},{"id":"2204.07412","title":"End-to-End Sensitivity-Based Filter Pruning","created":"2022-04-15","cited_by_count":1,"area":null},{"id":"2210.06650","title":"Interpreting Neural Policies with Disentangled Tree Representations","created":"2022-10-13","cited_by_count":1,"area":null},{"id":"2403.04805","title":"Pruning neural network models for gene regulatory dynamics using data and domain knowledge","created":"2024-03-05","cited_by_count":1,"area":null},{"id":"2210.11114","title":"Pruning by Active Attention Manipulation","created":"2022-10-20","cited_by_count":0,"area":null},{"id":"2508.02039","title":"Model Recycling Framework for Multi-Source Data-Free Supervised Transfer Learning","created":"2025-08-04","cited_by_count":0,"area":null},{"id":"2510.13134","title":"Convergence, design and training of continuous-time dropout as a random batch method","created":"2025-10-15","cited_by_count":0,"area":null}],"next_cursor":null}