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Found 10 papers, 4 papers with code
Large scale paired antibody language models
Antibodies are proteins produced by the immune system that can identify and neutralise a wide variety of antigens with high specificity and affinity, and constitute the most successful class of biotherapeutics.
Inverse folding for antibody sequence design using deep learning
We consider the problem of antibody sequence design given 3D structural information.
Structure-Aware Robustness Certificates for Graph Classification
Certifying the robustness of a graph-based machine learning model poses a critical challenge for safety.
Graph similarity learning for change-point detection in dynamic networks
The main novelty of our method is to use a siamese graph neural network architecture for learning a data-driven graph similarity function, which allows to effectively compare the current graph and its recent history.
Adversarial Attacks on Graph Classifiers via Bayesian Optimisation
While the majority of the literature focuses on such vulnerability in node-level classification tasks, little effort has been dedicated to analysing adversarial attacks on graph-level classification, an important problem with numerous real-life applications such as biochemistry and social network analysis.
On the Unreasonable Effectiveness of Feature propagation in Learning on Graphs with Missing Node Features
While Graph Neural Networks (GNNs) have recently become the de facto standard for modeling relational data, they impose a strong assumption on the availability of the node or edge features of the graph.
Adversarial Attacks on Graph Classification via Bayesian Optimisation
While the majority of the literature focuses on such vulnerability in node-level classification tasks, little effort has been dedicated to analysing adversarial attacks on graph-level classification, an important problem with numerous real-life applications such as biochemistry and social network analysis.
Attacking Graph Classification via Bayesian Optimisation
Graph neural networks have been shown to be vulnerable to adversarial attacks.
Interpretable Stability Bounds for Spectral Graph Filters
In this paper, we study filter stability and provide a novel and interpretable upper bound on the change of filter output, where the bound is expressed in terms of the endpoint degrees of the deleted and newly added edges, as well as the spatial proximity of those edges.
On the Stability of Graph Convolutional Neural Networks under Edge Rewiring
Graph neural networks are experiencing a surge of popularity within the machine learning community due to their ability to adapt to non-Euclidean domains and instil inductive biases.
