271 citations · 1.3k across the 60 of their papers we have counts for
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Graph Sparsification for GCN Towards Optimal Crop Yield Predictions
Saghar Bagheri, Gene Cheung, Tim Eadie
In agronomics, predicting crop yield at a per field/county granularity is important for farmers to minimize uncertainty and plan seeding for the next crop cycle. While state-of-the…
Unsupervised Graph Spectral Feature Denoising for Crop Yield Prediction
Saghar Bagheri, Chinthaka Dinesh, Gene Cheung +1
Prediction of annual crop yields at a county granularity is important for national food production and price stability. In this paper, towards the goal of better crop yield predict…
Sparse Graph Learning with Spectrum Prior for Deep Graph Convolutional Networks
Jin Zeng, Yang Liu, Gene Cheung +1
A graph convolutional network (GCN) employs a graph filtering kernel tailored for data with irregular structures. However, simply stacking more GCN layers does not improve performa…
Unfolding Projection-free SDP Relaxation of Binary Graph Classifier via GDPA Linearization
Cheng Yang, Gene Cheung, Wai-tian Tan +1
Algorithm unfolding creates an interpretable and parsimonious neural network architecture by implementing each iteration of a model-based algorithm as a neural layer. However, unfo…
Projection-free Graph-based Classifier Learning using Gershgorin Disc Perfect Alignment
Cheng Yang, Gene Cheung, Guangtao Zhai
In semi-supervised graph-based binary classifier learning, a subset of known labels are used to infer unknown labels, assuming that the label signal is smo…
Signed Graph Metric Learning via Gershgorin Disc Perfect Alignment
Cheng Yang, Gene Cheung, Wei Hu
Given a convex and differentiable objective $Q(\M)$ for a real symmetric matrix $\M$ in the positive definite (PD) cone -- used to compute Mahalanobis distances -- we propose a fas…
Graph Metric Learning via Gershgorin Disc Alignment
Cheng Yang, Gene Cheung, Wei Hu
We propose a fast general projection-free metric learning framework, where the minimization objective is a convex differentiable f…
Robust Deep Graph Based Learning for Binary Classification
Minxiang Ye, Vladimir Stankovic, Lina Stankovic +1
Convolutional neural network (CNN)-based feature learning has become state of the art, since given sufficient training data, CNN can significantly outperform traditional methods fo…
Robust Semi-Supervised Graph Classifier Learning with Negative Edge Weights
Gene Cheung, Weng-Tai Su, Yu Mao +1
In a semi-supervised learning scenario, (possibly noisy) partially observed labels are used as input to train a classifier, in order to assign labels to unclassified samples. In th…