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Found 6 papers, 1 papers with code
Mitigating Contradictions in Dialogue Based on Contrastive Learning
Chatbot models have achieved remarkable progress in recent years but tend to yield contradictory responses.
MolHF: A Hierarchical Normalizing Flow for Molecular Graph Generation
However, limited attention is paid to hierarchical generative models, which can exploit the inherent hierarchical structure (with rich semantic information) of the molecular graphs and generate complex molecules of larger size that we shall demonstrate to be difficult for most existing models.
Learning Large-scale Network Embedding from Representative Subgraph
Then, NES computes the network embedding from this representative subgraph, efficiently.
Fast Extraction of Word Embedding from Q-contexts
In this work, we show that with merely a small fraction of contexts (Q-contexts)which are typical in the whole corpus (and their mutual information with words), one can construct high-quality word embedding with negligible errors.
Balancing Accuracy and Fairness for Interactive Recommendation with Reinforcement Learning
Fairness in recommendation has attracted increasing attention due to bias and discrimination possibly caused by traditional recommenders.
A Matrix Chernoff Bound for Markov Chains and Its Application to Co-occurrence Matrices
Our result gives the first bound on the convergence rate of the co-occurrence matrix and the first sample complexity analysis in graph representation learning.
