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Found 6 papers, 2 papers with code
Collaborative Evolving Strategy for Automatic Data-Centric Development
By leveraging the strong complex problem-solving capabilities of large language models (LLMs), we propose an LLM-based autonomous agent, equipped with a strategy named Collaborative Knowledge-STudying-Enhanced Evolution by Retrieval (Co-STEER), to simultaneously address all the challenges.
Towards Data-Centric Automatic R&D
We appeal to future work to take developing techniques for tackling automatic R&D into consideration, thus bringing the opportunities of the potential revolutionary upgrade to human productivity.
Towards Efficient Information Fusion: Concentric Dual Fusion Attention Based Multiple Instance Learning for Whole Slide Images
In the realm of digital pathology, multi-magnification Multiple Instance Learning (multi-mag MIL) has proven effective in leveraging the hierarchical structure of Whole Slide Images (WSIs) to reduce information loss and redundant data.
Simple Multigraph Convolution Networks
Existing multigraph convolution methods either ignore the cross-view interaction among multiple graphs, or induce extremely high computational cost due to standard cross-view polynomial operators.
FinReport: Explainable Stock Earnings Forecasting via News Factor Analyzing Model
However, compared with financial institutions, it is not easy for ordinary investors to mine factors and analyze news.
Ranked #1 on
Stock Market Prediction
on Astock
NP$^2$L: Negative Pseudo Partial Labels Extraction for Graph Neural Networks
Moreover, applications of pseudo labels in graph neural networks (GNNs) oversee the difference between graph learning and other machine learning tasks such as message passing mechanism.
