{"total":28,"rows":[{"id":"2305.15930","title":"End-to-End Meta-Bayesian Optimisation with Transformer Neural Processes","created":"2023-05-25","cited_by_count":32,"area":null},{"id":"2102.07868","title":"GP-Tree: A Gaussian Process Classifier for Few-Shot Incremental Learning","created":"2021-02-15","cited_by_count":26,"area":null},{"id":"2205.15449","title":"Posterior and Computational Uncertainty in Gaussian Processes","created":"2022-05-30","cited_by_count":24,"area":null},{"id":"2109.01545","title":"Large-Scale Learning with Fourier Features and Tensor Decompositions","created":"2021-09-03","cited_by_count":14,"area":null},{"id":"2311.04465","title":"Solving High Frequency and Multi-Scale PDEs with Gaussian Processes","created":"2023-11-08","cited_by_count":14,"area":null},{"id":"2206.06597","title":"Permutation Search of Tensor Network Structures via Local Sampling","created":"2022-06-14","cited_by_count":13,"area":null},{"id":"2205.14283","title":"Rethinking Bayesian Learning for Data Analysis: The Art of Prior and Inference in Sparsity-Aware Modeling","created":"2022-05-28","cited_by_count":12,"area":null},{"id":"2304.12875","title":"Alternating Local Enumeration (TnALE): Solving Tensor Network Structure Search with Fewer Evaluations","created":"2023-04-25","cited_by_count":12,"area":null},{"id":"2203.06031","title":"Exploiting Low-Rank Tensor-Train Deep Neural Networks Based on Riemannian Gradient Descent With Illustrations of Speech Processing","created":"2022-03-11","cited_by_count":10,"area":null},{"id":"2106.06695","title":"SKIing on Simplices: Kernel Interpolation on the Permutohedral Lattice for Scalable Gaussian Processes","created":"2021-06-12","cited_by_count":9,"area":null},{"id":"2106.05992","title":"Scalable Variational Gaussian Processes via Harmonic Kernel Decomposition","created":"2021-06-10","cited_by_count":8,"area":null},{"id":"2206.11128","title":"tntorch: Tensor Network Learning with PyTorch","created":"2022-06-22","cited_by_count":7,"area":null},{"id":"2410.11165","title":"Toward Efficient Kernel-Based Solvers for Nonlinear PDEs","created":"2024-10-15","cited_by_count":7,"area":null},{"id":"2103.00393","title":"Hierarchical Inducing Point Gaussian Process for Inter-domain Observations","created":"2021-02-28","cited_by_count":6,"area":null},{"id":"2402.02456","title":"tnGPS: Discovering Unknown Tensor Network Structure Search Algorithms via Large Language Models (LLMs)","created":"2024-02-04","cited_by_count":5,"area":null},{"id":"2205.12961","title":"Position: Tensor Networks are a Valuable Asset for Green AI","created":"2022-05-25","cited_by_count":3,"area":null},{"id":"2403.19500","title":"Tensor Network-Constrained Kernel Machines as Gaussian Processes","created":"2024-03-28","cited_by_count":3,"area":null},{"id":"2507.11136","title":"Interpretable Bayesian Tensor Network Kernel Machines with Automatic Rank and Feature Selection","created":"2025-07-15","cited_by_count":3,"area":null},{"id":"2305.14451","title":"Kernel Interpolation with Sparse Grids","created":"2023-05-23","cited_by_count":2,"area":null},{"id":"2310.20630","title":"Projecting basis functions with tensor networks for Gaussian process regression","created":"2023-10-31","cited_by_count":2,"area":null},{"id":"2106.11827","title":"Lower and Upper Bounds on the VC-Dimension of Tensor Network Models","created":"2021-06-22","cited_by_count":1,"area":null},{"id":"2510.13772","title":"Tensor Gaussian Processes: Efficient Solvers for Nonlinear PDEs","created":"2025-10-15","cited_by_count":1,"area":null},{"id":"2307.06055","title":"Function-Space Regularization for Deep Bayesian Classification","created":"2023-07-12","cited_by_count":0,"area":null},{"id":"2408.02346","title":"Exploiting Hankel-Toeplitz Structures for Fast Computation of Kernel Precision Matrices","created":"2024-08-05","cited_by_count":0,"area":null},{"id":"2408.07480","title":"Adaptive Basis Function Selection for Computationally Efficient Predictions","created":"2024-08-14","cited_by_count":0,"area":null},{"id":"2409.03276","title":"Tensor network square root Kalman filter for online Gaussian process regression","created":"2024-09-05","cited_by_count":0,"area":null},{"id":"2603.23746","title":"Kronecker-Structured Nonparametric Spatiotemporal Point Processes","created":"2026-03-24","cited_by_count":0,"area":null},{"id":"2605.10285","title":"Scalable Gaussian process inference via neural feature maps","created":"2026-05-11","cited_by_count":0,"area":null}],"next_cursor":null}