Three new research papers explore advanced techniques for uncertainty quantification in deep learning models. The first paper introduces intuitionistic fuzzy deep randomized neural networks (IF-dRVFL and IF-edRVFL) to improve robustness against noisy data and outliers in classification tasks. The second paper empirically analyzes different uncertainty quantification methods, including Deep Ensembles, Bayesian Neural Networks, and Monte Carlo-dropout, for genomics applications, finding Bayesian Neural Networks to be more reliable for imbalanced and out-of-distribution data. The third paper proposes a two-step MV-DeepONet for probabilistic operator learning, enhancing the representation of cross-location conditional dependence in uncertainty propagation for complex physical systems governed by PDEs. AI
IMPACT These papers advance the state-of-the-art in uncertainty quantification, crucial for reliable AI deployment in sensitive domains like genomics and physical systems.
RANK_REASON Cluster consists of three arXiv pre-print papers detailing novel research in deep learning.
- arXiv
- DeepONet
- Hugging Face
- MV-DeepONet
- Probabilistic DeepONet
- Bayesian Neural Networks
- Deep Ensembles
- deep Random Vector Functional Link
- ensemble deep RVFL
- IF-dRVFL
- IF-edRVFL
- Keel
- Monte Carlo Dropout
- Random vector functional link network with L21 norm regularization for robot visual servo control with feature constraint
- University of California, Irvine
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