Cover Image

Continuum Dropout for Neural Differential Equations

Fuente: arXiv

Saved in:

Bibliographic Details
Main Authors:	Lee, Jonghun, Oh, YongKyung, Kim, Sungil, Lim, Dong-Young
Format:	Preprint
Published:	2025
Subjects:	Machine Learning
Online Access:	Acceder al recurso
Tags:	Add Tag No Tags, Be the first to tag this record!

Similar Items

Stable Neural Stochastic Differential Equations in Analyzing Irregular Time Series Data
by: Oh, YongKyung, et al.
Published: (2024)

Comprehensive Review of Neural Differential Equations for Time Series Analysis
by: Oh, YongKyung, et al.
Published: (2025)

Modeling Irregular Astronomical Time Series with Neural Stochastic Delay Differential Equations
by: Oh, YongKyung, et al.
Published: (2025)

TANDEM: Temporal Attention-guided Neural Differential Equations for Missingness in Time Series Classification
by: Oh, YongKyung, et al.
Published: (2025)

FlowPath: Learning Data-Driven Manifolds with Invertible Flows for Robust Irregularly-sampled Time Series Classification
by: Oh, YongKyung, et al.
Published: (2025)

DualDynamics: Synergizing Implicit and Explicit Methods for Robust Irregular Time Series Analysis
by: Oh, YongKyung, et al.
Published: (2024)

Position: State-of-the-Art Claims Require State-of-the-Art Evidence
by: Oh, YongKyung
Published: (2026)

Multi-View Contrastive Learning for Robust Domain Adaptation in Medical Time Series Analysis
by: Oh, YongKyung, et al.
Published: (2025)

Survey-aware Machine Learning: A Guideline for Valid Population Health Inference based on Scoping Review
by: Oh, YongKyung, et al.
Published: (2026)

Activation by Interval-wise Dropout: A Simple Way to Prevent Neural Networks from Plasticity Loss
by: Park, Sangyeon, et al.
Published: (2025)

DGSAM: Domain Generalization via Individual Sharpness-Aware Minimization
by: Song, Youngjun, et al.
Published: (2025)

Bridging Dynamic Factor Models and Neural Controlled Differential Equations for Nowcasting GDP
by: Lim, Seonkyu, et al.
Published: (2024)

Pseudo-Differential Neural Operator: Generalized Fourier Neural Operator for Learning Solution Operators of Partial Differential Equations
by: Shin, Jin Young, et al.
Published: (2022)

Differentially private synthesis of Spatial Point Processes
by: Kim, Dangchan, et al.
Published: (2025)

Speech Tokenizer is Key to Consistent Representation
by: Jung, Wonjin, et al.
Published: (2025)

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

Physics-Informed Laplace Neural Operator for Solving Partial Differential Equations
by: Kim, Heechang, et al.
Published: (2026)

Recovering Plasticity of Neural Networks via Soft Weight Rescaling
by: Oh, Seungwon, et al.
Published: (2025)

Grouped Differential Attention
by: Lim, Junghwan, et al.
Published: (2025)

Semantic-Aware Gaussian Process Calibration with Structured Layerwise Kernels for Deep Neural Networks
by: Lee, Kyung-hwan, et al.
Published: (2025)

Modeling Neural Networks with Privacy Using Neural Stochastic Differential Equations
by: Hong, Sanghyun, et al.
Published: (2025)

Attention-Based Neural-Augmented Kalman Filter for Legged Robot State Estimation
by: Lee, Seokju, et al.
Published: (2026)

Adaptive Tabu Dropout for Regularization of Deep Neural Network
by: Hasan, Md. Tarek, et al.
Published: (2024)

Neural Variational Dropout Processes
by: Jeon, Insu, et al.
Published: (2025)

Differentiable Cluster Graph Neural Network
by: Dong, Yanfei, et al.
Published: (2024)

P-DROP: Poisson-Based Dropout for Graph Neural Networks
by: Yun, Hyunsik
Published: (2025)

Symbolic Neural Ordinary Differential Equations
by: Li, Xin, et al.
Published: (2025)

Neural Variable-Order Fractional Differential Equation Networks
by: Cui, Wenjun, et al.
Published: (2025)

Neural Controlled Differential Equations with Quantum Hidden Evolutions
by: Yang, Lingyi, et al.
Published: (2024)

On the Generalization and Approximation Capacities of Neural Controlled Differential Equations
by: Bleistein, Linus, et al.
Published: (2023)

Variational Neural Stochastic Differential Equations with Change Points
by: El-Laham, Yousef, et al.
Published: (2024)

DInf-Grid: A Neural Differential Equation Solver with Differentiable Feature Grids
by: Kairanda, Navami, et al.
Published: (2026)

Randomized Neural Networks for Integro-Differential Equations with Application to Neutron Transport
by: Dang, Haoning, et al.
Published: (2026)

Neural Quantum Spectral Operator Learning for Solving Partial Differential Equations
by: Kim, Chanyoung, et al.
Published: (2026)

Dual Cone Gradient Descent for Training Physics-Informed Neural Networks
by: Hwang, Youngsik, et al.
Published: (2024)

Dirichlet-Based Monte Carlo Dropout for Uncertainty Estimation in Neural Networks
by: Hoblos, Rouaa, et al.
Published: (2026)

Enhancing Dropout-based Bayesian Neural Networks with Multi-Exit on FPGA
by: Chen, Hao Mark, et al.
Published: (2024)

Neural Structure Learning with Stochastic Differential Equations
by: Wang, Benjie, et al.
Published: (2023)

Learning Stable and Passive Neural Differential Equations
by: Cheng, Jing, et al.
Published: (2024)

On The Temporal Domain of Differential Equation Inspired Graph Neural Networks
by: Eliasof, Moshe, et al.
Published: (2024)