{"total":27,"rows":[{"id":"2104.03528","title":"Neural Temporal Point Processes: A Review","created":"2021-04-08","cited_by_count":114,"area":null},{"id":"2110.13040","title":"Neural Flows: Efficient Alternative to Neural ODEs","created":"2021-10-25","cited_by_count":101,"area":null},{"id":"2110.14012","title":"Graph Posterior Network: Bayesian Predictive Uncertainty for Node Classification","created":"2021-10-26","cited_by_count":80,"area":null},{"id":"2110.03051","title":"Prior and Posterior Networks: A Survey on Evidential Deep Learning Methods For Uncertainty Estimation","created":"2021-10-06","cited_by_count":71,"area":null},{"id":"2206.01558","title":"Disentangling Epistemic and Aleatoric Uncertainty in Reinforcement Learning","created":"2022-06-03","cited_by_count":27,"area":null},{"id":"2409.04720","title":"A Comprehensive Survey on Evidential Deep Learning and Its Applications","created":"2024-09-07","cited_by_count":25,"area":null},{"id":"2105.04471","title":"Natural Posterior Network: Deep Bayesian Uncertainty for Exponential Family Distributions","created":"2021-05-10","cited_by_count":13,"area":null},{"id":"2401.01484","title":"Uncertainty Regularized Evidential Regression","created":"2024-01-03","cited_by_count":13,"area":null},{"id":"2306.14916","title":"Uncertainty Estimation for Molecules: Desiderata and Methods","created":"2023-06-20","cited_by_count":11,"area":null},{"id":"2404.10980","title":"Hyper Evidential Deep Learning to Quantify Composite Classification Uncertainty","created":"2024-04-17","cited_by_count":11,"area":null},{"id":"2312.11230","title":"Dirichlet-based Uncertainty Quantification for Personalized Federated Learning with Improved Posterior Networks","created":"2023-12-18","cited_by_count":8,"area":null},{"id":"2210.15294","title":"Modeling Inter-Dependence Between Time and Mark in Multivariate Temporal Point Processes","created":"2022-10-27","cited_by_count":7,"area":null},{"id":"2303.05796","title":"Training, Architecture, and Prior for Deterministic Uncertainty Methods","created":"2023-03-10","cited_by_count":7,"area":null},{"id":"2303.06067","title":"Modeling Events and Interactions through Temporal Processes -- A Survey","created":"2023-03-10","cited_by_count":6,"area":null},{"id":"2211.08499","title":"Probabilistic Querying of Continuous-Time Event Sequences","created":"2022-11-15","cited_by_count":5,"area":null},{"id":"2311.05795","title":"Improvements on Uncertainty Quantification for Node Classification via Distance-Based Regularization","created":"2023-11-10","cited_by_count":5,"area":null},{"id":"2306.01452","title":"dugMatting: Decomposed-Uncertainty-Guided Matting","created":"2023-06-02","cited_by_count":4,"area":null},{"id":"2406.04041","title":"Linear Opinion Pooling for Uncertainty Quantification on Graphs","created":"2024-06-06","cited_by_count":3,"area":null},{"id":"2302.04018","title":"A Survey on Event Prediction Methods from a Systems Perspective: Bringing Together Disparate Research Areas","created":"2023-02-08","cited_by_count":2,"area":null},{"id":"2302.12002","title":"Master's Thesis: Out-of-distribution Detection with Energy-based Models","created":"2023-01-28","cited_by_count":1,"area":null},{"id":"2409.04159","title":"CUQ-GNN: Committee-based Graph Uncertainty Quantification using Posterior Networks","created":"2024-09-06","cited_by_count":1,"area":null},{"id":"2301.04977","title":"Modeling the evolution of temporal knowledge graphs with uncertainty","created":"2023-01-12","cited_by_count":0,"area":null},{"id":"2308.02360","title":"Intensity-free Integral-based Learning of Marked Temporal Point Processes","created":"2023-08-04","cited_by_count":0,"area":null},{"id":"2403.04005","title":"On the Efficient Marginalization of Probabilistic Sequence Models","created":"2024-03-06","cited_by_count":0,"area":null},{"id":"2410.19512","title":"Marked Temporal Bayesian Flow Point Processes","created":"2024-10-25","cited_by_count":0,"area":null},{"id":"2412.08590","title":"Preventing Conflicting Gradients in Neural Marked Temporal Point Processes","created":"2024-12-11","cited_by_count":0,"area":null},{"id":"2607.07330","title":"Hypergraph Neural Stochastic Diffusion: An SDE Framework for Uncertainty Estimation","created":"2026-07-08","cited_by_count":0,"area":null}],"next_cursor":null}