Large language models for mobility in transportation systems: A survey on forecasting tasks
Mobility analysis is a crucial element in the research area of transportation systems.
Forecasting traffic information offers a viable solution to address the conflict between …
Forecasting traffic information offers a viable solution to address the conflict between …
A Survey of Large Language Models for Financial Applications: Progress, Prospects and Challenges
Recent advances in large language models (LLMs) have unlocked novel opportunities for
machine learning applications in the financial domain. These models have demonstrated …
machine learning applications in the financial domain. These models have demonstrated …
Regime-aware factor allocation with optimal feature selection
In modern portfolio management, adapting to dynamic market conditions poses a significant
challenge for investors seeking optimal risk-adjusted returns. Traditional static allocation …
challenge for investors seeking optimal risk-adjusted returns. Traditional static allocation …
Only-train-electrical-to-optical-conversion (OTEOC): simple diffractive neural networks with optical readout
L Wu, Z Zhang - Optics Express, 2022 - opg.optica.org
Machine learning hardware based on optical diffraction is emerging as a new computing
platform with high throughput and low latency. The current all-optical diffractive deep neural …
platform with high throughput and low latency. The current all-optical diffractive deep neural …
Phase smoothing for diffractive deep neural networks
L Wu - Optics Communications, 2024 - Elsevier
Optical neural networks have shown promising advantages over its electronic counterpart
due to fast computational speed, high throughput, and low energy consumption. For the …
due to fast computational speed, high throughput, and low energy consumption. For the …
ESR-DDLN: Enhanced Single Image Super-Resolution Via Dual-Domain Learning Network
Most existing CNN-based super-resolution (SR) methods focus solely on the spatial domain.
We argue that frequency domain details are essential for reconstructing fine textures and …
We argue that frequency domain details are essential for reconstructing fine textures and …
Fully complex-valued deep learning model for visual perception
Deep learning models operating in the complex domain are used due to their rich
representation capacity. However, most of these models are either restricted to the first …
representation capacity. However, most of these models are either restricted to the first …