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Found 7 papers, 4 papers with code
Explaining neural network predictions of material strength
It is common in computer vision to employ an explainable AI saliency map to tell one what parts of an image are important to the network's decision.
Symbolic Regression via Neural-Guided Genetic Programming Population Seeding
Symbolic regression is the process of identifying mathematical expressions that fit observed output from a black-box process.
Improving exploration in policy gradient search: Application to symbolic optimization
Many machine learning strategies designed to automate mathematical tasks leverage neural networks to search large combinatorial spaces of mathematical symbols.
Deep symbolic regression: Recovering mathematical expressions from data via risk-seeking policy gradients
Discovering the underlying mathematical expressions describing a dataset is a core challenge for artificial intelligence.
Efficient Saliency Maps for Explainable AI
This provides an interesting comparison of scale information contributions within the network not provided by other saliency map methods.
Improvements to context based self-supervised learning
We develop a set of methods to improve on the results of self-supervised learning using context.
A Large Contextual Dataset for Classification, Detection and Counting of Cars with Deep Learning
It would be easy to train this method to count other kinds of objects and counting over new scenes requires no extra set up or assumptions about object locations.
Ranked #9 on
Object Counting
on CARPK
