Search Results for author:
Found 2 papers, 2 papers with code
AI Feynman 2.0: Pareto-optimal symbolic regression exploiting graph modularity
We present an improved method for symbolic regression that seeks to fit data to formulas that are Pareto-optimal, in the sense of having the best accuracy for a given complexity.
Sentiment Predictability for Stocks
In this work, we present our findings and experiments for stock-market prediction using various textual sentiment analysis tools, such as mood analysis and event extraction, as well as prediction models, such as LSTMs and specific convolutional architectures.
Cannot find the paper you are looking for? You can
Submit a new open access paper.
