Dynamic LoRA, a layer-wise adaptive variant of LoRA, reportedly improves GLUE accuracy from 87.4% to 88.1% at only 0.1% more trainable parameters, but the write-up lacks reproducibility.
Stock Type Prediction Model Based on Hierarchical Graph Neural Network
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abstract
This paper introduces a novel approach to stock data analysis by employing a Hierarchical Graph Neural Network (HGNN) model that captures multi-level information and relational structures in the stock market. The HGNN model integrates stock relationship data and hierarchical attributes to predict stock types effectively. The paper discusses the construction of a stock industry relationship graph and the extraction of temporal information from historical price sequences. It also highlights the design of a graph convolution operation and a temporal attention aggregator to model the macro market state. The integration of these features results in a comprehensive stock prediction model that addresses the challenges of utilizing stock relationship data and modeling hierarchical attributes in the stock market.
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Dynamic Adaptation of LoRA Fine-Tuning for Efficient and Task-Specific Optimization of Large Language Models
Dynamic LoRA, a layer-wise adaptive variant of LoRA, reportedly improves GLUE accuracy from 87.4% to 88.1% at only 0.1% more trainable parameters, but the write-up lacks reproducibility.