Yihao Xue

Orcid: 0000-0002-3310-4864

According to our database1, Yihao Xue authored at least 16 papers between 2020 and 2024.

Collaborative distances:
  • Dijkstra number2 of four.
  • Erdős number3 of four.

Timeline

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PhD thesis 
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Links

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Bibliography

2024
Few-shot Adaptation to Distribution Shifts By Mixing Source and Target Embeddings.
Proceedings of the Forty-first International Conference on Machine Learning, 2024

Understanding the Robustness of Multi-modal Contrastive Learning to Distribution Shift.
Proceedings of the Twelfth International Conference on Learning Representations, 2024

Investigating the Benefits of Projection Head for Representation Learning.
Proceedings of the Twelfth International Conference on Learning Representations, 2024

2023
A novel momentum prototypical neural network to cross-domain fault diagnosis for rotating machinery subject to cold-start.
Neurocomputing, October, 2023

A Novel Local Binary Temporal Convolutional Neural Network for Bearing Fault Diagnosis.
IEEE Trans. Instrum. Meas., 2023

Deep Transfer Learning for Bearing Fault Diagnosis: A Systematic Review Since 2016.
IEEE Trans. Instrum. Meas., 2023

Towards Mitigating Spurious Correlations in the Wild: A Benchmark & a more Realistic Dataset.
CoRR, 2023

Eliminating Spurious Correlations from Pre-trained Models via Data Mixing.
CoRR, 2023

Which Features are Learnt by Contrastive Learning? On the Role of Simplicity Bias in Class Collapse and Feature Suppression.
Proceedings of the International Conference on Machine Learning, 2023

2022
Polyphonic music generation generative adversarial network with Markov decision process.
Multim. Tools Appl., 2022

Superior generalization of smaller models in the presence of significant label noise.
CoRR, 2022

Investigating Why Contrastive Learning Benefits Robustness against Label Noise.
Proceedings of the International Conference on Machine Learning, 2022

2021
Successive Over Relaxation Recurrent Confidence Inference Network Based on Linear Extrapolation.
IEEE Access, 2021

Toward Understanding the Influence of Individual Clients in Federated Learning.
Proceedings of the Thirty-Fifth AAAI Conference on Artificial Intelligence, 2021

2020
Toward Understanding the Influence of Individual Clients in Federated Learning.
CoRR, 2020

S-EEGNet: Electroencephalogram Signal Classification Based on a Separable Convolution Neural Network With Bilinear Interpolation.
IEEE Access, 2020


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