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Found 4 papers, 3 papers with code
FiGURe: Simple and Efficient Unsupervised Node Representations with Filter Augmentations
This paper presents a simple filter-based augmentation method to capture different parts of the eigen-spectrum.
Consistent Training via Energy-Based GFlowNets for Modeling Discrete Joint Distributions
We hypothesize that this can lead to incompatibility between the inductive optimization biases in training $R$ and in training the GFlowNet, potentially leading to worse samples and slow adaptation to changes in the distribution.
Biological Sequence Design with GFlowNets
In this work, we propose an active learning algorithm leveraging epistemic uncertainty estimation and the recently proposed GFlowNets as a generator of diverse candidate solutions, with the objective to obtain a diverse batch of useful (as defined by some utility function, for example, the predicted anti-microbial activity of a peptide) and informative candidates after each round.
A Piece-wise Polynomial Filtering Approach for Graph Neural Networks
We study various aspects of our proposed model including, dependency on the number of eigencomponents utilized, latent polynomial filters learned, and performance of the individual polynomials on the node classification task.
