A locally-built, LLM-digested index of recent arXiv papers in quant finance, geometry/topology, and statistical ML — keyword search served straight from SQLite on this machine.
DDN dynamically combines weights for domain-specific models, improving performance.
Combining logic and probability has been a long stand- ing goal of AI research. Markov Logic Networks (MLNs) achieve this by attaching weights to formulas in first-order logic, and can be seen as templates for constructing features for ground Markov networks. Most techniques for learning weights of MLNs are domain-size…
Algorithm improves model performance on shifted concepts without retraining.
The recent proliferation of publicly available graph-structured data has sparked an interest in machine learning algorithms for graph data. Since most traditional machine learning algorithms assume data to be tabular, embedding algorithms for mapping graph data to real-valued vector spaces has become an active area of …
Paper proposes a novel approach to improve temporal clustering of time series data.
Develops a tensor network framework to reduce RNN complexity for high-dimensional sequence modeling.
Advanced kernels improve Gaussian process accuracy by incorporating domain knowledge.
Stock selection improved with a novel neural model capturing continuous stock dynamics.
GRTR framework uses graph regularization to improve financial forecasting.
This paper evaluates LLMs for technical market analysis, finding GPT-4 Turbo and FinGPT outperform passive benchmarks.
SX-GeoTree improves spatially coherent explanations in geospatial regression trees.
Predict missing and future data points in light curves using scalable Gaussian Processes.
Novel parallel GNN predicts protein-ligand interactions with high accuracy.