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Graphs in ML
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Rethinking Graph Transformers with Spectral Attention
Positional encodings based on the spectrum of the graph laplacian are proposed as a way to generalize global attention transformers to …
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Graph Augmented Normalizing Flows for Anomaly Detection of Multiple Time Series
A new method for simultaneously detecting anomalies across multiple time series. The structure of a Bayesian network is learned as the …
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GreaseLM: Graph REASoning Enhanced Language Models
Capabilities of large language models and massive knowledge graphs are combined through a novel layer-wise interaction between the language …
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Evaluation Metrics for Graph Generative Models: Problems, Pitfalls, and Practical Solutions
An investigation of common problems and pitfalls of the popular MMD technique for comparing distributions on graphs. Additionally, the …
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Understanding over-squashing and bottlenecks on graphs via curvature
Oversquashing refers to the struggling of graph neural networks with tasks requiring long-distance interactions between nodes. A new notion …
Other series in
Advances and fundamentals in ML
Simulation and AI
AI techniques are fundamentally transforming the field of simulation by combining physics-based modeling with data-driven machine learning.
Reinforcement Learning
Recent and popular advances in Reinforcement Learning are known to be data-hungry. Attempts to handle this deficit include developing …
Diffusion Models
Diffusion models (DM) have become the state of the art for sample quality in generative modelling. They work by sequentially corrupting …
Geometric deep learning
Specialized deep learning architectures exploit the intrinsic regularities arising from the underlying structure of the physical world. …
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