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Found 3 papers, 1 papers with code
Diversified Ensemble of Independent Sub-Networks for Robust Self-Supervised Representation Learning
Ensembling a neural network is a widely recognized approach to enhance model performance, estimate uncertainty, and improve robustness in deep supervised learning.
FiLM-Ensemble: Probabilistic Deep Learning via Feature-wise Linear Modulation
We show that the idea can be extended to uncertainty quantification: by modulating the network activations of a single deep network with FiLM, one obtains a model ensemble with high diversity, and consequently well-calibrated estimates of epistemic uncertainty, with low computational overhead in comparison.
Self-GenomeNet: Self-supervised Learning with Reverse-Complement Context Prediction for Nucleotide-level Genomics Data
We introduce Self-GenomeNet, a novel contrastive self-supervised learning method for nucleotide-level genomic data, which substantially improves the quality of the learned representations and performance compared to the current state-of-the-art deep learning frameworks.
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