{"total":57,"rows":[{"id":"2303.13440","title":"CLIP for All Things Zero-Shot Sketch-Based Image Retrieval, Fine-Grained or Not","created":"2023-03-23","cited_by_count":79,"area":null},{"id":"2212.00886","title":"Diffusion Generative Models in Infinite Dimensions","created":"2022-12-01","cited_by_count":49,"area":"diffusion"},{"id":"2312.17199","title":"Tractable Function-Space Variational Inference in Bayesian Neural Networks","created":"2023-12-28","cited_by_count":41,"area":null},{"id":"2101.03606","title":"The Gaussian Neural Process","created":"2021-01-10","cited_by_count":34,"area":null},{"id":"2301.10123","title":"Inducing Point Allocation for Sparse Gaussian Processes in High-Throughput Bayesian Optimisation","created":"2023-01-24","cited_by_count":32,"area":null},{"id":"2312.17210","title":"Continual Learning via Sequential Function-Space Variational Inference","created":"2023-12-28","cited_by_count":29,"area":null},{"id":"2306.11589","title":"Sampling from Gaussian Process Posteriors using Stochastic Gradient Descent","created":"2023-06-20","cited_by_count":27,"area":null},{"id":"2105.04504","title":"Deep Neural Networks as Point Estimates for Deep Gaussian Processes","created":"2021-05-10","cited_by_count":26,"area":null},{"id":"2106.03195","title":"Meta-Learning Reliable Priors in the Function Space","created":"2021-06-06","cited_by_count":23,"area":null},{"id":"2209.00517","title":"The Neural Process Family: Survey, Applications and Perspectives","created":"2022-09-01","cited_by_count":23,"area":null},{"id":"2106.07512","title":"Last Layer Marginal Likelihood for Invariance Learning","created":"2021-06-14","cited_by_count":21,"area":null},{"id":"2205.06342","title":"Generalized Variational Inference in Function Spaces: Gaussian Measures meet Bayesian Deep Learning","created":"2022-05-12","cited_by_count":21,"area":null},{"id":"2202.04005","title":"Improved Convergence Rates for Sparse Approximation Methods in Kernel-Based Learning","created":"2022-02-08","cited_by_count":18,"area":null},{"id":"2106.01121","title":"Connections and Equivalences between the Nystr\\\"om Method and Sparse Variational Gaussian Processes","created":"2021-06-02","cited_by_count":17,"area":null},{"id":"2303.13779","title":"Exploiting Unlabelled Photos for Stronger Fine-Grained SBIR","created":"2023-03-24","cited_by_count":14,"area":null},{"id":"2109.14492","title":"Variational Inference for Continuous-Time Switching Dynamical Systems","created":"2021-09-29","cited_by_count":10,"area":null},{"id":"2206.07673","title":"Wide Bayesian neural networks have a simple weight posterior: theory and accelerated sampling","created":"2022-06-15","cited_by_count":9,"area":null},{"id":"2406.04308","title":"Approximation-Aware Bayesian Optimization","created":"2024-06-06","cited_by_count":8,"area":null},{"id":"2106.10210","title":"Combining Pseudo-Point and State Space Approximations for Sum-Separable Gaussian Processes","created":"2021-06-18","cited_by_count":7,"area":null},{"id":"2210.07893","title":"Numerically Stable Sparse Gaussian Processes via Minimum Separation using Cover Trees","created":"2022-10-14","cited_by_count":5,"area":null},{"id":"2307.10052","title":"FaIRGP: A Bayesian Energy Balance Model for Surface Temperatures Emulation","created":"2023-07-14","cited_by_count":5,"area":null},{"id":"2505.11054","title":"NeuralSurv: Deep Survival Analysis with Bayesian Uncertainty Quantification","created":"2025-05-16","cited_by_count":5,"area":null},{"id":"2211.02476","title":"Sparse Gaussian Process Hyperparameters: Optimize or Integrate?","created":"2022-11-04","cited_by_count":4,"area":null},{"id":"2310.10171","title":"On permutation symmetries in Bayesian neural network posteriors: a variational perspective","created":"2023-10-16","cited_by_count":4,"area":null},{"id":"2108.11683","title":"Estimation of Riemannian distances between covariance operators and Gaussian processes","created":"2021-08-26","cited_by_count":3,"area":null},{"id":"2110.12798","title":"Variational Gaussian Processes: A Functional Analysis View","created":"2021-10-25","cited_by_count":3,"area":null},{"id":"2308.14142","title":"Integrated Variational Fourier Features for Fast Spatial Modelling with Gaussian Processes","created":"2023-08-27","cited_by_count":3,"area":null},{"id":"2502.04750","title":"Tighter sparse variational Gaussian processes","created":"2025-02-07","cited_by_count":3,"area":null},{"id":"2108.00819","title":"Active Learning in Gaussian Process State Space Model","created":"2021-07-30","cited_by_count":2,"area":null},{"id":"2203.06997","title":"Modelling Non-Smooth Signals with Complex Spectral Structure","created":"2022-03-14","cited_by_count":2,"area":null},{"id":"2209.04142","title":"Causal Modeling of Policy Interventions From Sequences of Treatments and Outcomes","created":"2022-09-09","cited_by_count":2,"area":null},{"id":"2309.00854","title":"A Unifying Variational Framework for Gaussian Process Motion Planning","created":"2023-09-02","cited_by_count":2,"area":null},{"id":"2309.15604","title":"Entropic Matching for Expectation Propagation of Markov Jump Processes","created":"2023-09-27","cited_by_count":2,"area":null},{"id":"2310.17852","title":"Function Space Bayesian Pseudocoreset for Bayesian Neural Networks","created":"2023-10-27","cited_by_count":2,"area":null},{"id":"2311.00663","title":"Variational Gaussian Processes For Linear Inverse Problems","created":"2023-11-01","cited_by_count":2,"area":null},{"id":"2402.09849","title":"Recommendations for Baselines and Benchmarking Approximate Gaussian Processes","created":"2024-02-15","cited_by_count":2,"area":null},{"id":"2409.16632","title":"Functional Stochastic Gradient MCMC for Bayesian Neural Networks","created":"2024-09-25","cited_by_count":2,"area":null},{"id":"2502.08730","title":"New Bounds for Sparse Variational Gaussian Processes","created":"2025-02-12","cited_by_count":2,"area":null},{"id":"2103.03321","title":"On MCMC for variationally sparse Gaussian processes: A pseudo-marginal approach","created":"2021-03-04","cited_by_count":1,"area":null},{"id":"2109.04188","title":"Towards Fully Automated Segmentation of Rat Cardiac MRI by Leveraging Deep Learning Frameworks","created":"2021-09-09","cited_by_count":1,"area":null},{"id":"2110.00568","title":"Conditional Deep Gaussian Processes: empirical Bayes hyperdata learning","created":"2021-10-01","cited_by_count":1,"area":null},{"id":"2110.13515","title":"Modular Gaussian Processes for Transfer Learning","created":"2021-10-26","cited_by_count":1,"area":null},{"id":"2209.01693","title":"Variational Inference for Model-Free and Model-Based Reinforcement Learning","created":"2022-09-04","cited_by_count":1,"area":null},{"id":"2303.00028","title":"Efficient Sensor Placement from Regression with Sparse Gaussian Processes in Continuous and Discrete Spaces","created":"2023-02-28","cited_by_count":1,"area":null},{"id":"2310.00097","title":"Pointwise uncertainty quantification for sparse variational Gaussian process regression with a Brownian motion prior","created":"2023-09-29","cited_by_count":1,"area":null},{"id":"2406.02177","title":"One-Shot Federated Learning with Bayesian Pseudocoresets","created":"2024-06-04","cited_by_count":1,"area":null},{"id":"2408.07588","title":"Adjusting Model Size in Continual Gaussian Processes: How Big is Big Enough?","created":"2024-08-14","cited_by_count":1,"area":null},{"id":"2505.21133","title":"Robust and Computation-Aware Gaussian Processes","created":"2025-05-27","cited_by_count":1,"area":null},{"id":"2205.04296","title":"AODisaggregation: toward global aerosol vertical profiles","created":"2022-05-06","cited_by_count":0,"area":null},{"id":"2303.08606","title":"On the Calibration and Uncertainty with P\\'{o}lya-Gamma Augmentation for Dialog Retrieval Models","created":"2023-03-15","cited_by_count":0,"area":null},{"id":"2307.06055","title":"Function-Space Regularization for Deep Bayesian Classification","created":"2023-07-12","cited_by_count":0,"area":null},{"id":"2409.08112","title":"Review of Recent Advances in Gaussian Process Regression Methods","created":"2024-09-12","cited_by_count":0,"area":null},{"id":"2503.21473","title":"DeepRV: Accelerating Spatiotemporal Inference with Pre-trained Neural Priors","created":"2025-03-27","cited_by_count":0,"area":null},{"id":"2507.02377","title":"Sparse Gaussian Processes: Structured Approximations and Power-EP Revisited","created":"2025-07-03","cited_by_count":0,"area":null},{"id":"2509.22482","title":"Bayesian Transfer Operators in Reproducing Kernel Hilbert Spaces","created":"2025-09-26","cited_by_count":0,"area":null},{"id":"2608.25706","title":"Fast rates in Bayesian online learning with approximate posteriors","created":"2026-08-26","cited_by_count":0,"area":null},{"id":"2610.13382","title":"BLOKS: Scaling Kernel Solvers with Sketched Block Preconditioning","created":"2026-10-09","cited_by_count":0,"area":null}],"next_cursor":null}