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Found 8 papers, 5 papers with code
A Latent Diffusion Model for Protein Structure Generation
Proteins are complex biomolecules that perform a variety of crucial functions within living organisms.
Learning Large Causal Structures from Inverse Covariance Matrix via Sparse Matrix Decomposition
In the context of linear structural equation models (SEMs), this paper focuses on learning causal structures from the inverse covariance matrix.
Generating 3D Molecules for Target Protein Binding
Second, to preserve the desirable equivariance property, we select a local reference atom according to the designed auxiliary classifiers and then construct a local spherical coordinate system.
Automated Data Augmentations for Graph Classification
In this work, we propose GraphAug, a novel automated data augmentation method aiming at computing label-invariant augmentations for graph classification.
Crowdsourcing Evaluation of Saliency-based XAI Methods
In order to compare various saliency-based XAI methods quantitatively, several approaches for automated evaluation schemes have been proposed; however, there is no guarantee that such automated evaluation metrics correctly evaluate explainability, and a high rating by an automated evaluation scheme does not necessarily mean a high explainability for humans.
Inter-domain Multi-relational Link Prediction
Multi-relational graph is a ubiquitous and important data structure, allowing flexible representation of multiple types of interactions and relations between entities.
Bermuda Triangles: GNNs Fail to Detect Simple Topological Structures
Most graph neural network architectures work by message-passing node vector embeddings over the adjacency matrix, and it is assumed that they capture graph topology by doing that.
Linear Tensor Projection Revealing Nonlinearity
We propose a learning method that searches for a subspace that maximizes the prediction accuracy while retaining as much of the original data information as possible, even if the prediction model in the subspace has strong nonlinearity.
