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Found 14 papers, 5 papers with code
Self-Supervised Learning for User Localization
Following this, we utilize the encoder portion of the AE models to extract relevant features from labeled data, and finetune an MLP-based Position Estimation Model to accurately deduce user locations.
Memory-Efficient and Secure DNN Inference on TrustZone-enabled Consumer IoT Devices
We design a memory-efficient management method to support memory-demanding inference in TEEs.
From Factor Models to Deep Learning: Machine Learning in Reshaping Empirical Asset Pricing
This paper comprehensively reviews the application of machine learning (ML) and AI in finance, specifically in the context of asset pricing.
RAGIC: Risk-Aware Generative Adversarial Model for Stock Interval Construction
This promising outcome suggests that our approach effectively addresses the challenges of stock market prediction while incorporating vital risk considerations.
HI-GAN: Hierarchical Inpainting GAN with Auxiliary Inputs for Combined RGB and Depth Inpainting
To address the above challenges, we propose Hierarchical Inpainting GAN (HI-GAN), a novel approach comprising three GANs in a hierarchical fashion for RGBD inpainting.
DataFrame QA: A Universal LLM Framework on DataFrame Question Answering Without Data Exposure
This paper introduces DataFrame question answering (QA), a novel task that utilizes large language models (LLMs) to generate Pandas queries for information retrieval and data analysis on dataframes, emphasizing safe and non-revealing data handling.
Margin Trader: A Reinforcement Learning Framework for Portfolio Management with Margin and Constraints
In the field of portfolio management using reinforcement learn- ing, existing approaches have mainly focused on cash-only trading, overlooking the potential benefits and risks of margin trading.
Safety in Traffic Management Systems: A Comprehensive Survey
Traffic management systems play a vital role in ensuring safe and efficient transportation on roads.
Stock Broad-Index Trend Patterns Learning via Domain Knowledge Informed Generative Network
Predicting the Stock movement attracts much attention from both industry and academia.
SafeLight: A Reinforcement Learning Method toward Collision-free Traffic Signal Control
Traffic signal control is safety-critical for our daily life.
A review of Generative Adversarial Networks (GANs) and its applications in a wide variety of disciplines -- From Medical to Remote Sensing
Therefore in this article we provide a comprehensive overview of the applications of GANs in a wide variety of disciplines.
High Resolution Solar Image Generation using Generative Adversarial Networks
For generating high resolution solar images we use the Pix2PixHD and Pix2Pix algorithms.
A Deep Spatio-Temporal Fuzzy Neural Network for Passenger Demand Prediction
In spite of its importance, passenger demand prediction is a highly challenging problem, because the demand is simultaneously influenced by the complex interactions among many spatial and temporal factors and other external factors such as weather.
Deep Reinforcement Learning for Traffic Light Control in Vehicular Networks
In terms of how to dynamically adjust traffic signals' duration, existing works either split the traffic signal into equal duration or extract limited traffic information from the real data.
