Cover Image

Preventing Conflicting Gradients in Neural Marked Temporal Point Processes

Fuente: arXiv

Saved in:

Bibliographic Details
Main Authors:	Bosser, Tanguy, Taieb, Souhaib Ben
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

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

Probabilistic Calibration by Design for Neural Network Regression
by: Dheur, Victor, et al.
Published: (2024)

Multivariate Latent Recalibration for Conditional Normalizing Flows
by: Dheur, Victor, et al.
Published: (2025)

An Evidence-Based Post-Hoc Adjustment Framework for Anomaly Detection Under Data Contamination
by: Patra, Sukanya, et al.
Published: (2025)

Revisiting Deep Feature Reconstruction for Logical and Structural Industrial Anomaly Detection
by: Patra, Sukanya, et al.
Published: (2024)

Symmetric Aggregation of Conformity Scores for Efficient Uncertainty Sets
by: Alami, Nabil, et al.
Published: (2025)

Enforcing Calibration in Multi-Output Probabilistic Regression with Pre-rank Regularization
by: Desobry, Naomi, et al.
Published: (2025)

Detecting Abnormal Operations in Concentrated Solar Power Plants from Irregular Sequences of Thermal Images
by: Patra, Sukanya, et al.
Published: (2024)

Risk-Based Thresholding for Reliable Anomaly Detection in Concentrated Solar Power Plants
by: Estievenart, Yorick, et al.
Published: (2025)

Calibrated Multivariate Distributional Regression with Pre-Rank Regularization
by: Laajil, Aya, et al.
Published: (2026)

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

A Unified Comparative Study with Generalized Conformity Scores for Multi-Output Conformal Regression
by: Dheur, Victor, et al.
Published: (2025)

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

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

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

Hyper Hawkes Processes: Interpretable Models of Marked Temporal Point Processes
by: Boyd, Alex, et al.
Published: (2025)

Differentiable Adversarial Attacks for Marked Temporal Point Processes
by: Chakraborty, Pritish, et al.
Published: (2025)

Conditional Generative Modeling for High-dimensional Marked Temporal Point Processes
by: Dong, Zheng, et al.
Published: (2023)

Modeling Inter-Dependence Between Time and Mark in Multivariate Temporal Point Processes
by: Waghmare, Govind, et al.
Published: (2022)

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

Rectifying Conformity Scores for Better Conditional Coverage
by: Plassier, Vincent, et al.
Published: (2025)

ITPP: Learning Disentangled Event Dynamics in Marked Temporal Point Processes
by: Zhou, Wang-Tao, et al.
Published: (2025)

Bridging Discrete Marks and Continuous Dynamics: Dual-Path Cross-Interaction for Marked Temporal Point Processes
by: Liu, Yuxiang, et al.
Published: (2026)

Meta-Learning for Neural Network-based Temporal Point Processes
by: Takimoto, Yoshiaki, et al.
Published: (2024)

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

HierarchicalForecast: A Reference Framework for Hierarchical Forecasting in Python
by: Olivares, Kin G., et al.
Published: (2022)

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

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

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

Convergence of SGD for Training Neural Networks with Sliced Wasserstein Losses
by: Tanguy, Eloi
Published: (2023)

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

Per-Loss Adapters for Gradient Conflict in Physics-Informed Neural Networks
by: Kim, Bum Jun, et al.
Published: (2026)

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

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

Speculative Sampling for Parametric Temporal Point Processes
by: Biloš, Marin, et al.
Published: (2025)

Edit-Based Flow Matching for Temporal Point Processes
by: Lüdke, David, et al.
Published: (2025)

A Gentle Introduction to Conformal Time Series Forecasting
by: Stocker, M., et al.
Published: (2025)

A Bayesian Mixture Model of Temporal Point Processes with Determinantal Point Process Prior
by: Dong, Yiwei, et al.
Published: (2024)

Neural Temporal Point Processes for Forecasting Directional Relations in Evolving Hypergraphs
by: Gracious, Tony, et al.
Published: (2023)

EventFlow: Forecasting Temporal Point Processes with Flow Matching
by: Kerrigan, Gavin, et al.
Published: (2024)