11 citations · 22 across the 8 of their papers we have counts for
4 papers · 1 filter
Neural Network Classifier as Mutual Information Evaluator
Zhenyue Qin, Dongwoo Kim, Tom Gedeon
Cross-entropy loss with softmax output is a standard choice to train neural network classifiers. We give a new view of neural network classifiers with softmax and cross-entropy as…
Position-Sensing Graph Neural Networks: Proactively Learning Nodes Relative Positions
Zhenyue Qin, Yiqun Zhang Saeed Anwar, Dongwoo Kim +3
Most existing graph neural networks (GNNs) learn node embeddings using the framework of message passing and aggregation. Such GNNs are incapable of learning relative positions betw…
Rethinking Softmax with Cross-Entropy: Neural Network Classifier as Mutual Information Estimator
Zhenyue Qin, Dongwoo Kim, Tom Gedeon
Mutual information is widely applied to learn latent representations of observations, whilst its implication in classification neural networks remain to be better explained. We sho…
Softmax Is Not an Artificial Trick: An Information-Theoretic View of Softmax in Neural Networks
Zhenyue Qin, Dongwoo Kim
Despite great popularity of applying softmax to map the non-normalised outputs of a neural network to a probability distribution over predicting classes, this normalised exponentia…