---
title: Multi-Task DL for Pavement Prediction
url: https://www.ml-quant.com/papers/ssrn/5082558/
site: ML-Quant (https://www.ml-quant.com)
updated: 2026-09-26
license: Summaries CC BY 4.0; links go to the original sources
index: https://www.ml-quant.com/llms.txt
identifier: SSRN 5082558
source_url: https://papers.ssrn.com/sol3/papers.cfm?abstract_id=5082558
featured: 2025-01-08
citations: unknown
topic: ML & AI Methods
---


# Multi-Task DL for Pavement Prediction

The study creates a multitask deep learning approach to predict lane-level pavement performance using historical data, tested with a real case in China.

- Source: https://papers.ssrn.com/sol3/papers.cfm?abstract_id=5082558
- Identifier: SSRN 5082558
- Released: 2025-01-04
- First featured: Quant Letter No. 81 (2025-01-08): https://www.ml-quant.com/issues/2025-01-08/
- Citations (Semantic Scholar): not tracked
- Published in: not yet
- Topic: ML & AI Methods

## Related

- [Transformers are SSMs: Generalized Models and Efficient Algorithms Through Structured State Space Duality](https://www.ml-quant.com/papers/arxiv/2405.21060/): The research identifies a link between state-space models and Transformers in deep learning, leading to the creation of a faster language modeling architecture, Mamba-2.
- [SOAP: Improving and Stabilizing Shampoo using Adam](https://www.ml-quant.com/papers/arxiv/2409.11321/): A new algorithm, SOAP, enhances the computational efficiency of the Shampoo preconditioning method in deep learning tasks, reducing iterations and time, with an online implementation available.
- [Edge Directionality Improves Learning on Heterophilic Graphs](https://www.ml-quant.com/papers/arxiv/2305.10498/): The study presents Directed Graph Neural Network (Dir-GNN), a new deep learning framework for directed graphs that surpasses traditional models in heterophilic benchmarks.
- [TabR: Tabular Deep Learning Meets Nearest Neighbors](https://www.ml-quant.com/papers/arxiv/2307.14338/): Tabular DL Meets Nearest Neighbors: TabR, a new deep learning model for tabular data, outperforms existing models by using a k-Nearest-Neighbors-like component for better predictions.
- [Year-over-Year Developments in Financial Fraud Detection via Deep Learning: A Systematic Literature Review](https://www.ml-quant.com/papers/arxiv/2502.00201/): The study examines progress in deep learning methods for detecting financial fraud, reviewing 57 studies from 2019 to 2024, and discusses challenges and opportunities such as data privacy, feature engineering, and model interpretability.
- [Modular Duality in Deep Learning](https://www.ml-quant.com/papers/arxiv/2410.21265/): The article presents a new theory of modular dualization for general neural networks, providing a theoretical basis for fast and scalable training algorithms, potentially leading to a new generation of optimizers for neural architectures.