Cover Image

pyrtklib: An open-source package for tightly coupled deep learning and GNSS integration for positioning in urban canyons

Fuente: arXiv

Saved in:

Bibliographic Details
Main Authors:	Hu, Runzhi, Xu, Penghui, Zhong, Yihan, Wen, Weisong
Format:	Preprint
Published:	2024
Subjects:	Machine Learning Artificial Intelligence
Online Access:	Acceder al recurso
Tags:	Add Tag No Tags, Be the first to tag this record!

Similar Items

bacpipe: a Python package to make bioacoustic deep learning models accessible
by: Kather, Vincent S., et al.
Published: (2026)

survex: an R package for explaining machine learning survival models
by: Spytek, Mikołaj, et al.
Published: (2023)

Reinforcement learning with combinatorial actions for coupled restless bandits
by: Xu, Lily, et al.
Published: (2025)

Memorization in deep learning: A survey
by: Wei, Jiaheng, et al.
Published: (2024)

An approach of deep reinforcement learning for maximizing the net present value of stochastic projects
by: Xu, Wei, et al.
Published: (2025)

When LLMs meet open-world graph learning: a new perspective for unlabeled data uncertainty
by: Wen, Yanzhe, et al.
Published: (2025)

FlexMS is a flexible framework for benchmarking deep learning-based mass spectrum prediction tools in metabolomics
by: Zhong, Yunhua, et al.
Published: (2026)

Learning to fuse: dynamic integration of multi-source data for accurate battery lifespan prediction
by: Shanxuan, He, et al.
Published: (2025)

Stable Attention Response for Reliable Precipitation Nowcasting
by: Wen, Penghui, et al.
Published: (2026)

An AI-driven framework for rapid and localized optimizations of urban open spaces
by: Eshraghi, Pegah, et al.
Published: (2025)

Uncovering hidden geometry in Transformers via disentangling position and context
by: Song, Jiajun, et al.
Published: (2023)

Assessment of deep learning models integrated with weather and environmental variables for wildfire spread prediction and a case study of the 2023 Maui fires
by: Kim, Jiyeon, et al.
Published: (2025)

Preventing overfitting in deep learning using differential privacy
by: Khatri, Alizishaan Anwar Hussein
Published: (2026)

A comparative study of deep learning and ensemble learning to extend the horizon of traffic forecasting
by: Zheng, Xiao, et al.
Published: (2025)

OpenFPL: An open-source forecasting method rivaling state-of-the-art Fantasy Premier League services
by: Groos, Daniel
Published: (2025)

Spatiotemporal deep learning models for detection of rapid intensification in cyclones
by: Sutar, Vamshika, et al.
Published: (2025)

Economic span selection of bridge based on deep reinforcement learning
by: Zhang, Leye, et al.
Published: (2024)

Static and multivariate-temporal attentive fusion transformer for readmission risk prediction
by: Sun, Zhe, et al.
Published: (2024)

A deep learning and machine learning approach to predict neonatal death in the context of São Paulo
by: Raihan, Mohon, et al.
Published: (2025)

FLAIN: Mitigating Backdoor Attacks in Federated Learning via Flipping Weight Updates of Low-Activation Input Neurons
by: Ding, Binbin, et al.
Published: (2024)

An efficient deep reinforcement learning environment for flexible job-shop scheduling
by: Wu, Xinquan, et al.
Published: (2025)

On the consistency of hyper-parameter selection in value-based deep reinforcement learning
by: Obando-Ceron, Johan, et al.
Published: (2024)

Recursive deep learning framework for forecasting the decadal world economic outlook
by: Wang, Tianyi, et al.
Published: (2023)

Training instability in deep learning follows low-dimensional dynamical principles
by: Zhang, Zhipeng, et al.
Published: (2026)

Differential privacy for medical deep learning: methods, tradeoffs, and deployment implications
by: Mohammadi, Marziyeh, et al.
Published: (2025)

Exploring the design space of deep-learning-based weather forecasting systems
by: Siddiqui, Shoaib Ahmed, et al.
Published: (2024)

Multiplicative update rules for accelerating deep learning training and increasing robustness
by: Kirtas, Manos, et al.
Published: (2023)

Explainable deep learning improves human mental models of self-driving cars
by: Kenny, Eoin M., et al.
Published: (2024)

Don't throw the baby out with the bathwater: How and why deep learning for ARC
by: Cole, Jack, et al.
Published: (2025)

Ultra-short-term solar power forecasting by deep learning and data reconstruction
by: Wang, Jinbao, et al.
Published: (2025)

Synthetic Information towards Maximum Posterior Ratio for deep learning on Imbalanced Data
by: Nguyen, Hung, et al.
Published: (2024)

Softmax is $1/2$-Lipschitz: A tight bound across all $\ell_p$ norms
by: Nair, Pravin
Published: (2025)

McCast: Memory-Guided Latent Drift Correction for Long-Horizon Precipitation Nowcasting
by: Wen, Penghui, et al.
Published: (2026)

Ratio-Variance Regularized Policy Optimization for Efficient LLM Fine-tuning
by: Luo, Yu, et al.
Published: (2026)

pUniFind: a unified large pre-trained deep learning model pushing the limit of mass spectra interpretation
by: Zhao, Jiale, et al.
Published: (2025)

Designing a double deep reinforcement learning selection tool for resilient demand prediction
by: Benziane, Bilel Abderrahmane, et al.
Published: (2026)

A redescription mining framework for post-hoc explaining and relating deep learning models
by: Mihelčić, Matej, et al.
Published: (2025)

In value-based deep reinforcement learning, a pruned network is a good network
by: Obando-Ceron, Johan, et al.
Published: (2024)

Extreme value forecasting using relevance-based data augmentation with deep learning models
by: Hua, Junru, et al.
Published: (2025)

Implicit meta-learning may lead language models to trust more reliable sources
by: Krasheninnikov, Dmitrii, et al.
Published: (2023)