Showing cs.LGShow all
3 papers · 1 filter
cs.LG2025
Efficient Learning of Balanced Signed Graphs via Sparse Linear Programming
Haruki Yokota, Hiroshi Higashi, Yuichi Tanaka +1
Signed graphs are equipped with both positive and negative edge weights, encoding pairwise correlations as well as anti-correlations in data. A balanced signed graph is a signed gr…
cs.LG2024
Efficient Learning of Balanced Signed Graphs via Iterative Linear Programming
Haruki Yokota, Hiroshi Higashi, Yuichi Tanaka +1
Signed graphs are equipped with both positive and negative edge weights, encoding pairwise correlations as well as anti-correlations in data. A balanced signed graph has no cycles…
cs.LG2024
Signal Processing in the Retina: Interpretable Graph Classifier to Predict Ganglion Cell Responses
Yasaman Parhizkar, Gene Cheung, Andrew W. Eckford
It is a popular hypothesis in neuroscience that ganglion cells in the retina are activated by selectively detecting visual features in an observed scene. While ganglion cell firing…