Cover Image

Meta-Learning for Neural Network-based Temporal Point Processes

Fuente: arXiv

Saved in:

Bibliographic Details
Main Authors:	Takimoto, Yoshiaki, Tanaka, Yusuke, Iwata, Tomoharu, Okawa, Maya, Kim, Hideaki, Toda, Hiroyuki, Kurashima, Takeshi
Format:	Preprint
Published:	2024
Subjects:	Machine Learning
Online Access:	Acceder al recurso
Tags:	Add Tag No Tags, Be the first to tag this record!

Similar Items

Aggregated Multi-output Gaussian Processes with Knowledge Transfer Across Domains
by: Tanaka, Yusuke, et al.
Published: (2022)

A Representer Theorem for Hawkes Processes via Penalized Least Squares Minimization
by: Kim, Hideaki, et al.
Published: (2025)

K$^2$IE: Kernel Method-based Kernel Intensity Estimators for Inhomogeneous Poisson Processes
by: Kim, Hideaki, et al.
Published: (2025)

Neural Operators Meet Energy-based Theory: Operator Learning for Hamiltonian and Dissipative PDEs
by: Tanaka, Yusuke, et al.
Published: (2024)

MetaCaDI: A Meta-Learning Framework for Scalable Causal Discovery with Unknown Interventions
by: Ong, Hans Jarett, et al.
Published: (2025)

Meta-learning for Positive-unlabeled Classification
by: Kumagai, Atsutoshi, et al.
Published: (2024)

Meta-learning Representations for Learning from Multiple Annotators
by: Kumagai, Atsutoshi, et al.
Published: (2025)

Explanation-based Training with Differentiable Insertion/Deletion Metric-aware Regularizers
by: Yoshikawa, Yuya, et al.
Published: (2023)

Deep Koopman-layered Model with Universal Property Based on Toeplitz Matrices
by: Hashimoto, Yuka, et al.
Published: (2024)

Unified generalization analysis for physics informed neural networks
by: Hashimoto, Yuka, et al.
Published: (2026)

Data-driven Projection Generation for Efficiently Solving Heterogeneous Quadratic Programming Problems
by: Iwata, Tomoharu, et al.
Published: (2025)

Deep Graph Neural Point Process For Learning Temporal Interactive Networks
by: Chen, Su, et al.
Published: (2025)

Reassessing Evaluation Functions in Algorithmic Recourse: An Empirical Study from a Human-Centered Perspective
by: Tominaga, Tomu, et al.
Published: (2024)

Compositional Abilities Emerge Multiplicatively: Exploring Diffusion Models on a Synthetic Task
by: Okawa, Maya, et al.
Published: (2023)

Understanding the Expressivity and Trainability of Fourier Neural Operator: A Mean-Field Perspective
by: Koshizuka, Takeshi, et al.
Published: (2023)

On Non-asymptotic Theory of Recurrent Neural Networks in Temporal Point Processes
by: Chen, Zhiheng, et al.
Published: (2024)

Emergence of Hidden Capabilities: Exploring Learning Dynamics in Concept Space
by: Park, Core Francisco, et al.
Published: (2024)

Swing-by Dynamics in Concept Learning and Compositional Generalization
by: Yang, Yongyi, et al.
Published: (2024)

Marked Neural Spatio-Temporal Point Process Involving a Dynamic Graph Neural Network
by: Moallemy-Oureh, Alice, et al.
Published: (2022)

Decoupled Marked Temporal Point Process using Neural Ordinary Differential Equations
by: Song, Yujee, et al.
Published: (2024)

Deep Positive-Unlabeled Anomaly Detection for Contaminated Unlabeled Data
by: Takahashi, Hiroshi, et al.
Published: (2024)

A Continuous-time Tractable Model for Present-biased Agents
by: Akagi, Yasunori, et al.
Published: (2024)

Preventing Conflicting Gradients in Neural Marked Temporal Point Processes
by: Bosser, Tanguy, et al.
Published: (2024)

Advances in Temporal Point Processes: Bayesian, Neural, and LLM Approaches
by: Zhou, Feng, et al.
Published: (2025)

Representation Shattering in Transformers: A Synthetic Study with Knowledge Editing
by: Nishi, Kento, et al.
Published: (2024)

Distributed Quantum Neural Networks via Partitioned Features Encoding
by: Kawase, Yoshiaki
Published: (2023)

Learning Marked Temporal Point Process Explanations based on Counterfactual and Factual Reasoning
by: Liu, Sishun, et al.
Published: (2025)

Automatic design optimization of preference-based subjective evaluation with online learning in crowdsourcing environment
by: Yasuda, Yusuke, et al.
Published: (2024)

An Adaptive Resonance Theory-based Topological Clustering Algorithm with a Self-Adjusting Vigilance Parameter
by: Masuyama, Naoki, et al.
Published: (2025)

Explicit and Implicit Graduated Optimization in Deep Neural Networks
by: Sato, Naoki, et al.
Published: (2024)

Interpretable Neural Temporal Point Processes for Modelling Electronic Health Records
by: Liu, Bingqing
Published: (2024)

Positive-Unlabeled Diffusion Models for Preventing Sensitive Data Generation
by: Takahashi, Hiroshi, et al.
Published: (2025)

A Variational Autoencoder for Neural Temporal Point Processes with Dynamic Latent Graphs
by: Yang, Sikun, et al.
Published: (2023)

Addressing Mark Imbalance in Integration-free Neural Marked Temporal Point Processes
by: Liu, Sishun, et al.
Published: (2025)

In-Context Learning of Temporal Point Processes with Foundation Inference Models
by: Berghaus, David, et al.
Published: (2025)

Neuro-Symbolic Temporal Point Processes
by: Yang, Yang, et al.
Published: (2024)

Distribution-Free Conformal Joint Prediction Regions for Neural Marked Temporal Point Processes
by: Dheur, Victor, et al.
Published: (2024)

Cumulative Distribution Function based General Temporal Point Processes
by: Wang, Maolin, et al.
Published: (2024)

Marked Temporal Bayesian Flow Point Processes
by: Chen, Hui, et al.
Published: (2024)

Add and Thin: Diffusion for Temporal Point Processes
by: Lüdke, David, et al.
Published: (2023)