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Found 9 papers, 5 papers with code
Mix-n-Match : Ensemble and Compositional Methods for Uncertainty Calibration in Deep Learning
This paper studies the problem of post-hoc calibration of machine learning classifiers.
Leveraging Uncertainty from Deep Learning for Trustworthy Materials Discovery Workflows
In this paper, we leverage predictive uncertainty of deep neural networks to answer challenging questions material scientists usually encounter in machine learning based materials applications workflows.
Probabilistic Neighbourhood Component Analysis: Sample Efficient Uncertainty Estimation in Deep Learning
In this work, we show that the uncertainty estimation capability of state-of-the-art BNNs and Deep Ensemble models degrades significantly when the amount of training data is small.
Explainable Deep Learning for Uncovering Actionable Scientific Insights for Materials Discovery and Design
The scientific community has been increasingly interested in harnessing the power of deep learning to solve various domain challenges.
Actionable Attribution Maps for Scientific Machine Learning
The scientific community has been increasingly interested in harnessing the power of deep learning to solve various domain challenges.
Mix-n-Match: Ensemble and Compositional Methods for Uncertainty Calibration in Deep Learning
We show that none of the existing methods satisfy all three requirements, and demonstrate how Mix-n-Match calibration strategies (i. e., ensemble and composition) can help achieve remarkably better data-efficiency and expressive power while provably maintaining the classification accuracy of the original classifier.
Pipelines for Procedural Information Extraction from Scientific Literature: Towards Recipes using Machine Learning and Data Science
This system meets computational information and knowledge management (CIKM) requirements of metadata-driven payload extraction, named entity extraction, and relationship extraction from text.
Deep Kernels with Probabilistic Embeddings for Small-Data Learning
Experiments on a variety of datasets show that our approach outperforms the state-of-the-art in GP kernel learning in both supervised and semi-supervised settings.
Reliable and Explainable Machine Learning Methods for Accelerated Material Discovery
We also propose a transfer learning technique and show that the performance loss due to models' simplicity can be overcome by exploiting correlations among different material properties.
