10 citations · 11 across the 8 of their papers we have counts for
8 papers
Converge to Surprise: Evolutionary Self-supervised Image Clustering
Canlin Zhang, Xiuwen Liu
A variety of self-supervised image clustering approaches are invented in the past years. However, all dominant approaches are exploitative: The direction of parameter updates is de…
A Theory of Machine Understanding via the Minimum Description Length Principle
Canlin Zhang, Xiuwen Liu
Deep neural networks trained through end-to-end learning have achieved remarkable success across various domains in the past decade. However, the end-to-end learning strategy, orig…
Learning Regularities from Data using Spiking Functions: A Theory
Canlin Zhang, Xiuwen Liu
Deep neural networks trained in an end-to-end manner are proven to be efficient in a wide range of machine learning tasks. However, there is one drawback of end-to-end learning: Th…
Inductive Link Prediction in Knowledge Graphs using Path-based Neural Networks
Canlin Zhang, Xiuwen Liu
Link prediction is a crucial research area in knowledge graphs, with many downstream applications. In many real-world scenarios, inductive link prediction is required, where predic…
Theoretical Rule-based Knowledge Graph Reasoning by Connectivity Dependency Discovery
Canlin Zhang, Chun-Nan Hsu, Yannis Katsis +2
Discovering precise and interpretable rules from knowledge graphs is regarded as an essential challenge, which can improve the performances of many downstream tasks and even provid…
Dense Embeddings Preserving the Semantic Relationships in WordNet
Canlin Zhang, Xiuwen Liu
In this paper, we provide a novel way to generate low dimensional vector embeddings for the noun and verb synsets in WordNet, where the hypernym-hyponym relationship is preserved i…
An Analysis on the Learning Rules of the Skip-Gram Model
Canlin Zhang, Xiuwen Liu, Daniel Bis
To improve the generalization of the representations for natural language processing tasks, words are commonly represented using vectors, where distances among the vectors are rela…
Towards Quantifying Intrinsic Generalization of Deep ReLU Networks
Shaeke Salman, Canlin Zhang, Xiuwen Liu +1
Understanding the underlying mechanisms that enable the empirical successes of deep neural networks is essential for further improving their performance and explaining such network…