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Orchestration Graphs
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Adversarial attacks in Natural Language Processing apply perturbations in the character or token levels. Token-level attacks, gaining prominence for their use of gradient-based methods, are susceptible to altering sentence semantics, leading to invalid adv ...
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Graph machine learning offers a powerful framework with natural applications in scientific fields such as chemistry, biology and material sciences. By representing data as a graph, we encode the prior knowledge that the data is composed of a set of entitie ...
EPFL2023A full characterization of invariant embeddability of unimodular planar graphs
When can a unimodular random planar graph be drawn in the Euclidean or the hyperbolic plane in a way that the distribution of the random drawing is isometry-invariant? This question was answered for one-ended unimodular graphs in Benjamini and Timar, using ...
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In several machine learning settings, the data of interest are well described by graphs. Examples include data pertaining to transportation networks or social networks. Further, biological data, such as proteins or molecules, lend themselves well to graph- ...
EPFL2022Representation Learning for Multi-relational Data
Recent years have witnessed a rise in real-world data captured with rich structural information that can be better depicted by multi-relational or heterogeneous graphs.However, research on relational representation learning has so far mostly focused on the ...
EPFL2021Direct Model-checking of SysML Models
Model-checking intends to verify whether a property is satisfied by a model, or not. Model-checking of high-level models, e.g. SysML models, usually first requires a model transformation to a low level formal specification. The present papers proposes a ne ...
SCITEPRESS2021