{"total":18,"rows":[{"id":"2210.15435","title":"Grokking phase transitions in learning local rules with gradient descent","created":"2022-10-26","cited_by_count":28,"area":null},{"id":"2202.08177","title":"Generative modeling with projected entangled-pair states","created":"2022-02-16","cited_by_count":16,"area":null},{"id":"2305.19440","title":"Machine learning with tree tensor networks, CP rank constraints, and tensor dropout","created":"2023-05-30","cited_by_count":15,"area":null},{"id":"2107.12549","title":"DISP6D: Disentangled Implicit Shape and Pose Learning for Scalable 6D Pose Estimation","created":"2021-07-27","cited_by_count":12,"area":null},{"id":"2106.08334","title":"Quantum-inspired event reconstruction with Tensor Networks: Matrix Product States","created":"2021-06-15","cited_by_count":9,"area":null},{"id":"2310.20498","title":"Generative Learning of Continuous Data by Tensor Networks","created":"2023-10-31","cited_by_count":9,"area":null},{"id":"2202.10471","title":"Classical versus Quantum: comparing Tensor Network-based Quantum Circuits on LHC data","created":"2022-02-21","cited_by_count":7,"area":null},{"id":"2306.08595","title":"TensorKrowch: Smooth integration of tensor networks in machine learning","created":"2023-06-14","cited_by_count":5,"area":null},{"id":"2502.13090","title":"tn4ml: Tensor Network Training and Customization for Machine Learning","created":"2025-02-18","cited_by_count":5,"area":null},{"id":"2503.21815","title":"ATP: Adaptive Threshold Pruning for Efficient Data Encoding in Quantum Neural Networks","created":"2025-03-26","cited_by_count":5,"area":null},{"id":"2111.08014","title":"Tensor network to learn the wavefunction of data","created":"2021-11-15","cited_by_count":4,"area":null},{"id":"2303.11249","title":"What Makes Data Suitable for a Locally Connected Neural Network? A Necessary and Sufficient Condition Based on Quantum Entanglement","created":"2023-03-20","cited_by_count":4,"area":null},{"id":"2208.04372","title":"Generalization and Overfitting in Matrix Product State Machine Learning Architectures","created":"2022-08-08","cited_by_count":3,"area":null},{"id":"2110.03898","title":"Differentiable Programming of Isometric Tensor Networks","created":"2021-10-08","cited_by_count":2,"area":null},{"id":"2209.09098","title":"Deep tensor networks with matrix product operators","created":"2022-09-16","cited_by_count":2,"area":null},{"id":"2306.14787","title":"Distributive Pre-Training of Generative Modeling Using Matrix-Product States","created":"2023-06-26","cited_by_count":2,"area":null},{"id":"2412.15826","title":"Using matrix-product states for time-series machine learning","created":"2024-12-20","cited_by_count":2,"area":null},{"id":"2403.12969","title":"Entangling Machine Learning with Quantum Tensor Networks","created":"2024-01-09","cited_by_count":0,"area":null}],"next_cursor":null}