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20192023
most citedProbabilistic End-to-end Noise Correction for Learning with Noisy Labels

67 citations · 113 across the 10 of their papers we have counts for

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Showing 2022Show all

5 papers · 1 filter

cs.LG2022

Edge-Varying Fourier Graph Networks for Multivariate Time Series Forecasting

Kun Yi, Qi Zhang, Liang Hu +4

The key problem in multivariate time series (MTS) analysis and forecasting aims to disclose the underlying couplings between variables that drive the co-movements. Considerable rec…

cs.LG2022★ 1 cited

Distributional Drift Adaptation with Temporal Conditional Variational Autoencoder for Multivariate Time Series Forecasting

Hui He, Qi Zhang, Kun Yi +3

Due to the non-stationary nature, the distribution of real-world multivariate time series (MTS) changes over time, which is known as distribution drift. Most existing MTS forecasti…

cs.CV2022★ 8 cited

Masked Image Modeling with Denoising Contrast

Kun Yi, Yixiao Ge, Xiaotong Li +5

Since the development of self-supervised visual representation learning from contrastive learning to masked image modeling (MIM), there is no significant difference in essence, tha…

cs.CV2022

mc-BEiT: Multi-choice Discretization for Image BERT Pre-training

Xiaotong Li, Yixiao Ge, Kun Yi +3

Image BERT pre-training with masked image modeling (MIM) becomes a popular practice to cope with self-supervised representation learning. A seminal work, BEiT, casts MIM as a class…

cs.CV2022★ 1 cited

PENCIL: Deep Learning with Noisy Labels

Kun Yi, Guo-Hua Wang, Jianxin Wu

Deep learning has achieved excellent performance in various computer vision tasks, but requires a lot of training examples with clean labels. It is easy to collect a dataset with n…