791 citations · 1.5k across the 57 of their papers we have counts for
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Dropout's Dream Land: Generalization from Learned Simulators to Reality
Zac Wellmer, James T. Kwok
A World Model is a generative model used to simulate an environment. World Models have proven capable of learning spatial and temporal representations of Reinforcement Learning env…
Bilinear Scoring Function Search for Knowledge Graph Learning
Yongqi Zhang, Quanming Yao, James Tin-Yau Kwok
Learning embeddings for entities and relations in knowledge graph (KG) have benefited many downstream tasks. In recent years, scoring functions, the crux of KG learning, have been…
Pyramidal Dense Attention Networks for Lightweight Image Super-Resolution
Huapeng Wu, Jie Gui, Jun Zhang +2
Recently, deep convolutional neural network methods have achieved an excellent performance in image superresolution (SR), but they can not be easily applied to embedded devices due…
Feedback Pyramid Attention Networks for Single Image Super-Resolution
Huapeng Wu, Jie Gui, Jun Zhang +2
Recently, convolutional neural network (CNN) based image super-resolution (SR) methods have achieved significant performance improvement. However, most CNN-based methods mainly foc…
TOHAN: A One-step Approach towards Few-shot Hypothesis Adaptation
Haoang Chi, Feng Liu, Wenjing Yang +5
In few-shot domain adaptation (FDA), classifiers for the target domain are trained with accessible labeled data in the source domain (SD) and few labeled data in the target domain…
SparseBERT: Rethinking the Importance Analysis in Self-attention
Han Shi, Jiahui Gao, Xiaozhe Ren +4
Transformer-based models are popularly used in natural language processing (NLP). Its core component, self-attention, has aroused widespread interest. To understand the self-attent…
Topology-aware Tensor Decomposition for Meta-graph Learning
Hansi Yang, Peiyu Zhang, Quanming Yao
Heterogeneous graphs generally refers to graphs with different types of nodes and edges. A common approach for extracting useful information from heterogeneous graphs is to use met…