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Found 6 papers, 2 papers with code
Reducing the Cost of Quantum Chemical Data By Backpropagating Through Density Functional Theory
For comparison, Sch\"utt et al. (2019) spent 626 hours creating a dataset on which they trained their NN for 160h, for a total of 786h; our method achieves comparable performance within 31h.
Resource-constrained knowledge diffusion processes inspired by human peer learning
We consider a setting where a population of artificial learners is given, and the objective is to optimize aggregate measures of performance, under constraints on training resources.
Towards Foundational Models for Molecular Learning on Large-Scale Multi-Task Datasets
Recently, pre-trained foundation models have enabled significant advancements in multiple fields.
Generalized Laplacian Positional Encoding for Graph Representation Learning
Graph neural networks (GNNs) are the primary tool for processing graph-structured data.
Long Range Graph Benchmark
Graph Neural Networks (GNNs) that are based on the message passing (MP) paradigm generally exchange information between 1-hop neighbors to build node representations at each layer.
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Neurally boosted supervised spectral clustering
Spectral network embeddings are based on the computation of eigenvectors of a normalized graph Laplacian.
