Distribution-free conformal joint prediction regions for neural marked temporal point processes
Sequences of labeled events observed at irregular intervals in continuous time are
ubiquitous across various fields. Temporal Point Processes (TPPs) provide a mathematical …
ubiquitous across various fields. Temporal Point Processes (TPPs) provide a mathematical …
Interpretable Neural Temporal Point Processes for Modelling Electronic Health Records
B Liu - arXiv preprint arXiv:2404.08007, 2024 - arxiv.org
Electronic Health Records (EHR) can be represented as temporal sequences that record the
events (medical visits) from patients. Neural temporal point process (NTPP) has achieved …
events (medical visits) from patients. Neural temporal point process (NTPP) has achieved …
Preventing Conflicting Gradients in Neural Marked Temporal Point Processes
Neural Marked Temporal Point Processes (MTPP) are flexible models to capture complex
temporal inter-dependencies between labeled events. These models inherently learn two …
temporal inter-dependencies between labeled events. These models inherently learn two …
Modelling event sequence data by type-wise neural point process
B Liu - Data Mining and Knowledge Discovery, 2024 - Springer
Event sequence data widely exists in real life, where each event is typically represented as a
tuple, event type and occurrence time. Recently, neural point process (NPP), a probabilistic …
tuple, event type and occurrence time. Recently, neural point process (NPP), a probabilistic …
Cumulative Hazard Function Based Efficient Multivariate Temporal Point Process Learning
B Liu - arXiv preprint arXiv:2404.13663, 2024 - arxiv.org
Most existing temporal point process models are characterized by conditional intensity
function. These models often require numerical approximation methods for likelihood …
function. These models often require numerical approximation methods for likelihood …
A Case-Based Reasoning and Explaining Model for Temporal Point Process
B Liu - International Conference on Case-Based Reasoning, 2024 - Springer
Event sequence data widely exists in real life, where each event can be typically
represented as a tuple, event type and occurrence time. Combined with deep learning …
represented as a tuple, event type and occurrence time. Combined with deep learning …
University of Chinese Academy of Science, Beijing, China liubingqing20@ mails. ucas. ac. cn Academy of Mathematics and Systems Science, CAS, Beijing, China
B Liu - Case-Based Reasoning Research and Development …, 2024 - books.google.com
Event sequence data widely exists in real life, where each event can be typically
represented as a tuple, event type and occurrence time. Combined with deep learning …
represented as a tuple, event type and occurrence time. Combined with deep learning …