- School of Mathematics and Statistics - Research Publications
School of Mathematics and Statistics - Research Publications
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ItemNo Preview AvailableCombating Noisy Labels with Sample Selection by Mining High-Discrepancy ExamplesXia, X ; Han, B ; Zhan, Y ; Yu, J ; Gong, M ; Gong, C ; Liu, T (IEEE, 2023-01-01)
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ItemNo Preview AvailableGenerating Dynamic Kernels via Transformers for Lane DetectionChen, Z ; Liu, Y ; Gong, M ; Du, B ; Qian, G ; Smith-Miles, K (IEEE, 2023-01-01)
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ItemNo Preview AvailableKnowledge Distillation for Feature Extraction in Underwater VSLAMYang, J ; Gong, M ; Nair, G ; Lee, JH ; Monty, J ; Pu, Y (IEEE, 2023-01-01)
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ItemNo Preview AvailableUnpaired Image-to-Image Translation with Shortest Path RegularizationXie, S ; Xu, Y ; Gong, M ; Zhang, K (IEEE, 2023-06)
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ItemNo Preview AvailableStyle Interleaved Learning for Generalizable Person Re-IdentificationTan, W ; Ding, C ; Wang, P ; Gong, M ; Jia, K (IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC, 2024)
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ItemNo Preview AvailableAdaptive Local-Component-aware Graph Convolutional Network for One-shot Skeleton-based Action RecognitionZhu, A ; Ke, Q ; Gong, M ; Bailey, J (IEEE COMPUTER SOC, 2023)
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ItemNo Preview AvailableDeep Learning Is Singular, and That's GoodWei, S ; Murfet, D ; Gong, M ; Li, H ; Gell-Redman, J ; Quella, T (IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC, 2023-12)In singular models, the optimal set of parameters forms an analytic set with singularities, and a classical statistical inference cannot be applied to such models. This is significant for deep learning as neural networks are singular, and thus, "dividing" by the determinant of the Hessian or employing the Laplace approximation is not appropriate. Despite its potential for addressing fundamental issues in deep learning, a singular learning theory appears to have made little inroads into the developing canon of a deep learning theory. Via a mix of theory and experiment, we present an invitation to the singular learning theory as a vehicle for understanding deep learning and suggest an important future work to make the singular learning theory directly applicable to how deep learning is performed in practice.
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ItemNo Preview AvailableProgressive Video Summarization via Multimodal Self-supervised LearningLi, H ; Ke, Q ; Gong, M ; Drummond, T (IEEE COMPUTER SOC, 2023)
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ItemNo Preview AvailableMaximum Spatial Perturbation Consistency for Unpaired Image-to-Image TranslationXu, Y ; Xie, S ; Wu, W ; Zhang, K ; Gong, M ; Batmanghelich, K (IEEE COMPUTER SOC, 2022)
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ItemNo Preview AvailableAlleviating Semantics Distortion in Unsupervised Low-Level Image-to-Image Translation via Structure Consistency ConstraintGuo, J ; Li, J ; Fu, H ; Gong, M ; Zhang, K ; Tao, D (IEEE COMPUTER SOC, 2022)
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