Zobrazeno 1 - 8
of 8
pro vyhledávání: '"Finkelshtein, Ben"'
Message Passing Neural Networks (MPNNs) are a staple of graph machine learning. MPNNs iteratively update each node's representation in an input graph by aggregating messages from the node's neighbors, which necessitates a memory complexity of the ord
Externí odkaz:
http://arxiv.org/abs/2405.20724
We present a new angle on the expressive power of graph neural networks (GNNs) by studying how the predictions of real-valued GNN classifiers, such as those classifying graphs probabilistically, evolve as we apply them on larger graphs drawn from som
Externí odkaz:
http://arxiv.org/abs/2403.03880
Graph neural networks are popular architectures for graph machine learning, based on iterative computation of node representations of an input graph through a series of invariant transformations. A large class of graph neural networks follow a standa
Externí odkaz:
http://arxiv.org/abs/2310.01267
Strategic classification studies learning in settings where users can modify their features to obtain favorable predictions. Most current works focus on simple classifiers that trigger independent user responses. Here we examine the implications of l
Externí odkaz:
http://arxiv.org/abs/2205.15765
Equivariance to permutations and rigid motions is an important inductive bias for various 3D learning problems. Recently it has been shown that the equivariant Tensor Field Network architecture is universal -- it can approximate any equivariant funct
Externí odkaz:
http://arxiv.org/abs/2203.01216
Graph neural networks (GNNs) have shown broad applicability in a variety of domains. These domains, e.g., social networks and product recommendations, are fertile ground for malicious users and behavior. In this paper, we show that GNNs are vulnerabl
Externí odkaz:
http://arxiv.org/abs/2011.03574
Publikováno v:
In Neurocomputing 7 November 2022 513:1-12
Publikováno v:
Functional Ecology; Dec2019, Vol. 33 Issue 12, p2417-2429, 13p