Han Hu
6 papers in the PaperMetrix corpus
Papers by this author
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GCNet: Non-Local Networks Meet Squeeze-Excitation Networks and Beyond
2019
The Non-Local Network (NLNet) presents a pioneering approach for capturing long-range dependencies, via aggregating query-specific global context to each query position. However, through a rigorous empirical analysis, we have found that the global contexts modeled …
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Aligning Pretraining for Detection via Object-Level Contrastive Learning
2021 · arXiv (Cornell University)
Image-level contrastive representation learning has proven to be highly effective as a generic model for transfer learning. Such generality for transfer learning, however, sacrifices specificity if we are interested in a certain downstream task. We …
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Optimizing Data Center Energy Efficiency via Event-Driven Deep Reinforcement Learning
2022 · IEEE Transactions on Services Computing
To reduce the skyrocketing energy consumption of data centers, the prevailing approaches adopt the time-driven manner to control IT and cooling subsystems. These methods suffer from highly dynamic system states, complex action spaces and the …
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Carrier Frequency Offset in Internet of Things Radio Frequency Fingerprint Identification: An Experimental Review
2023 · IEEE Internet of Things Journal
Radio frequency fingerprint (RFF) identification has become a promising security solution for resource-constrained Internet-of-Things (IoT) devices, which relies on hardware impairments-induced radio frequency features for identification; among which, a hotspot feature is the carrier frequency …
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Joint Input and Output Coordination for Class-Incremental Learning
2024 · arXiv (Cornell University)
Incremental learning is nontrivial due to severe catastrophic forgetting. Although storing a small amount of data on old tasks during incremental learning is a feasible solution, current strategies still do not 1) adequately address the …
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Hypergraph Foundation Model
2025 · IEEE Transactions on Pattern Analysis and Machine Intelligence
Hypergraph neural networks (HGNNs) effectively model complex high-order relationships in domains like protein interactions and social networks by connecting multiple vertices through hyperedges, enhancing modeling capabilities, and reducing information loss. Developing foundation models for hypergraphs …