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Found 5 papers, 3 papers with code
Scalable Deep Generative Modeling for Sparse Graphs
Based on this, we develop a novel autoregressive model, named BiGG, that utilizes this sparsity to avoid generating the full adjacency matrix, and importantly reduces the graph generation time complexity to $O((n + m)\log n)$.
Chip Placement with Deep Reinforcement Learning
To achieve these results, we pose placement as a Reinforcement Learning (RL) problem and train an agent to place the nodes of a chip netlist onto a chip canvas.
Generalized Clustering by Learning to Optimize Expected Normalized Cuts
We introduce a novel end-to-end approach for learning to cluster in the absence of labeled examples.
Selectivity Estimation for Range Predicates using Lightweight Models
Query optimizers depend on selectivity estimates of query predicates to produce a good execution plan.
GAP: Generalizable Approximate Graph Partitioning Framework
Graph partitioning is the problem of dividing the nodes of a graph into balanced partitions while minimizing the edge cut across the partitions.
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