Imagen de Portada

Taylor-Sensus Network: Embracing Noise to Enlighten Uncertainty for Scientific Data

Fuente: arXiv

Guardado en:

Detalles Bibliográficos
Autores principales:	Song, Guangxuan, Fu, Dongmei, Qiu, Zhongwei, Meng, Jintao, Zhang, Dawei
Formato:	Preprint
Publicado:	2024
Materias:	Machine Learning
Acceso en línea:	Acceder al recurso
Etiquetas:	Agregar Etiqueta Sin Etiquetas, Sea el primero en etiquetar este registro!

Ejemplares similares

Bridging the Semantic-Numerical Gap: A Numerical Reasoning Method of Cross-modal Knowledge Graph for Material Property Prediction
por: Song, Guangxuan, et al.
Publicado: (2023)

Enlightenment Period Improving DNN Performance
por: Liu, Tiantian, et al.
Publicado: (2025)

Embracing Aleatoric Uncertainty in Medical Multimodal Learning with Missing Modalities
por: Gong, Linxiao, et al.
Publicado: (2026)

Echoes of Socratic Doubt: Embracing Uncertainty in Calibrated Evidential Reinforcement Learning
por: Stutts, Alex Christopher, et al.
Publicado: (2024)

Optimizing Mixture of Block Attention
por: Xiao, Guangxuan, et al.
Publicado: (2025)

Uncertainty Quantification With Noise Injection in Neural Networks: A Bayesian Perspective
por: Yuan, Xueqiong, et al.
Publicado: (2025)

Geometric Generative Modeling with Noise-Conditioned Graph Networks
por: Pao-Huang, Peter, et al.
Publicado: (2025)

Uncertainty-Aware Distribution-to-Distribution Flow Matching for Scientific Imaging
por: Wu, Dongxia, et al.
Publicado: (2026)

Natural Geometry of Robust Data Attribution: From Convex Models to Deep Networks
por: Li, Shihao, et al.
Publicado: (2025)

FreshGNN: Reducing Memory Access via Stable Historical Embeddings for Graph Neural Network Training
por: Huang, Kezhao, et al.
Publicado: (2023)

Probabilistic Neural Networks (PNNs) for Modeling Aleatoric Uncertainty in Scientific Machine Learning
por: Pourkamali-Anaraki, Farhad, et al.
Publicado: (2024)

Conformalized-KANs: Uncertainty Quantification with Coverage Guarantees for Kolmogorov-Arnold Networks (KANs) in Scientific Machine Learning
por: Mollaali, Amirhossein, et al.
Publicado: (2025)

Epistemic Uncertainty and Observation Noise with the Neural Tangent Kernel
por: Calvo-Ordoñez, Sergio, et al.
Publicado: (2024)

Reinforcement Learning-based Feature Generation Algorithm for Scientific Data
por: Xiao, Meng, et al.
Publicado: (2025)

Capsule Neural Networks as Noise Stabilizer for Time Series Data
por: Kim, Soyeon, et al.
Publicado: (2024)

Position: Embracing Negative Results in Machine Learning
por: Karl, Florian, et al.
Publicado: (2024)

DMIS: Dynamic Mesh-based Importance Sampling for Training Physics-Informed Neural Networks
por: Yang, Zijiang, et al.
Publicado: (2022)

Quest: Query-Aware Sparsity for Efficient Long-Context LLM Inference
por: Tang, Jiaming, et al.
Publicado: (2024)

Uncertainty-Aware Gradient Signal-to-Noise Data Selection for Instruction Tuning
por: Yuan, Zhihang, et al.
Publicado: (2026)

Graph Neural Networks with Coarse- and Fine-Grained Division for Mitigating Label Sparsity and Noise
por: Li, Shuangjie, et al.
Publicado: (2024)

Posterior Uncertainty Quantification in Neural Networks using Data Augmentation
por: Wu, Luhuan, et al.
Publicado: (2024)

Embracing Large Language Models in Traffic Flow Forecasting
por: Zhao, Yusheng, et al.
Publicado: (2024)

Principle-Evolvable Scientific Discovery via Uncertainty Minimization
por: Pu, Yingming, et al.
Publicado: (2026)

Semi-Supervised Noise Adaptation: Transferring Knowledge from Noise Domain
por: Yao, Yuan, et al.
Publicado: (2026)

Tabular Data Augmentation for Machine Learning: Progress and Prospects of Embracing Generative AI
por: Cui, Lingxi, et al.
Publicado: (2024)

Is AI Robust Enough for Scientific Research?
por: Zhang, Jun-Jie, et al.
Publicado: (2024)

Incorporating Taylor Series and Recursive Structure in Neural Networks for Time Series Prediction
por: Mau, Jarrod, et al.
Publicado: (2024)

Test-Time Learning of Causal Structure from Interventional Data
por: Chen, Wei, et al.
Publicado: (2026)

A Hybrid Multi-Factor Network with Dynamic Sequence Modeling for Early Warning of Intraoperative Hypotension
por: Cheng, Mingyue, et al.
Publicado: (2024)

NoiseHGNN: Synthesized Similarity Graph-Based Neural Network For Noised Heterogeneous Graph Representation Learning
por: Zhang, Xiong, et al.
Publicado: (2024)

Uncertainty-Aware Foundation Models for Clinical Data
por: Zhou, Qian, et al.
Publicado: (2026)

SmoothQuant: Accurate and Efficient Post-Training Quantization for Large Language Models
por: Xiao, Guangxuan, et al.
Publicado: (2022)

Quantile-Free Uncertainty Quantification in Graph Neural Networks
por: park, Soyoung, et al.
Publicado: (2026)

Fast Track to Winning Tickets: Repowering One-Shot Pruning for Graph Neural Networks
por: Yue, Yanwei, et al.
Publicado: (2024)

Bridging Neural Networks and Dynamic Time Warping for Adaptive Time Series Classification
por: Qu, Jintao, et al.
Publicado: (2025)

Rollout Roulette: A Probabilistic Inference Approach to Inference-Time Scaling of LLMs using Particle-Based Monte Carlo Methods
por: Puri, Isha, et al.
Publicado: (2025)

Daunce: Data Attribution through Uncertainty Estimation
por: Pan, Xingyuan, et al.
Publicado: (2025)

Efficient and Principled Scientific Discovery through Bayesian Optimization: A Tutorial
por: Yu, Zhongwei, et al.
Publicado: (2026)

Using Random Noise Equivariantly to Boost Graph Neural Networks Universally
por: Wang, Xiyuan, et al.
Publicado: (2025)

SHIELD: A Segmented Hierarchical Memory Architecture for Energy-Efficient LLM Inference on Edge NPUs
por: Zhang, Jintao, et al.
Publicado: (2026)