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Found 6 papers, 1 papers with code
LLMs for Relational Reasoning: How Far are We?
Large language models (LLMs) have revolutionized many areas (e. g. natural language processing, software engineering, etc.)
PST: Improving Quantitative Trading via Program Sketch-based Tuning
Particularly, PST first proposes using a novel symbolic program sketch to embed the abstract human expert knowledge of market trends.
Do We Really Need Graph Neural Networks for Traffic Forecasting?
Spatio-temporal graph neural networks (STGNN) have become the most popular solution to traffic forecasting.
GALOIS: Boosting Deep Reinforcement Learning via Generalizable Logic Synthesis
In this paper, we combine the above two paradigms together and propose a novel Generalizable Logic Synthesis (GALOIS) framework to synthesize hierarchical and strict cause-effect logic programs.
Unveiling Project-Specific Bias in Neural Code Models
We propose a Cond-Idf measurement to interpret this behavior, which quantifies the relatedness of a token with a label and its project-specificness.
Fair and accurate age prediction using distribution aware data curation and augmentation
One of the main causes of unfair behavior in age prediction methods lies in the distribution and diversity of the training data.
