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Found 8 papers, 4 papers with code
Multimodal Learning for Materials
Artificial intelligence is transforming computational materials science, improving the prediction of material properties, and accelerating the discovery of novel materials.
Constructive Assimilation: Boosting Contrastive Learning Performance through View Generation Strategies
Recently, several attempts have been made to replace such domain-specific, human-designed transformations with generated views that are learned.
Multi-Symmetry Ensembles: Improving Diversity and Generalization via Opposing Symmetries
In this work, we present Multi-Symmetry Ensembles (MSE), a framework for constructing diverse ensembles by capturing the multiplicity of hypotheses along symmetry axes, which explore the hypothesis space beyond stochastic perturbations of model weights and hyperparameters.
On the Importance of Calibration in Semi-supervised Learning
State-of-the-art (SOTA) semi-supervised learning (SSL) methods have been highly successful in leveraging a mix of labeled and unlabeled data by combining techniques of consistency regularization and pseudo-labeling.
Equivariant Contrastive Learning
In state-of-the-art self-supervised learning (SSL) pre-training produces semantically good representations by encouraging them to be invariant under meaningful transformations prescribed from human knowledge.
Surrogate- and invariance-boosted contrastive learning for data-scarce applications in science
Deep learning techniques have been increasingly applied to the natural sciences, e. g., for property prediction and optimization or material discovery.
Equivariant Self-Supervised Learning: Encouraging Equivariance in Representations
In state-of-the-art self-supervised learning (SSL) pre-training produces semantically good representations by encouraging them to be invariant under meaningful transformations prescribed from human knowledge.
Deep Learning for Bayesian Optimization of Scientific Problems with High-Dimensional Structure
Bayesian optimization (BO) is a popular paradigm for global optimization of expensive black-box functions, but there are many domains where the function is not completely a black-box.
