Search Results for author: T. Nathan Mundhenk

Found 7 papers, 4 papers with code

Explaining neural network predictions of material strength

no code implementations5 Nov 2021 Ian A. Palmer, T. Nathan Mundhenk, Brian Gallagher, Yong Han

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.

Improving exploration in policy gradient search: Application to symbolic optimization

1 code implementation19 Jul 2021 Mikel Landajuela Larma, Brenden K. Petersen, Soo K. Kim, Claudio P. Santiago, Ruben Glatt, T. Nathan Mundhenk, Jacob F. Pettit, Daniel M. Faissol

Many machine learning strategies designed to automate mathematical tasks leverage neural networks to search large combinatorial spaces of mathematical symbols.

Symbolic Regression

Efficient Saliency Maps for Explainable AI

1 code implementation26 Nov 2019 T. Nathan Mundhenk, Barry Y. Chen, Gerald Friedland

This provides an interesting comparison of scale information contributions within the network not provided by other saliency map methods.

Astronomy

A Large Contextual Dataset for Classification, Detection and Counting of Cars with Deep Learning

no code implementations14 Sep 2016 T. Nathan Mundhenk, Goran Konjevod, Wesam A. Sakla, Kofi Boakye

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.

Density Estimation General Classification

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