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4 days ago · In this paper, we adopt a sparse graph formulation based on the inclusion of extra nodes in a simple grid graph. While the grid encodes the spatial disposition ...
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6 days ago · A . In this paper, we give an almost linear time and space algorithms to sample from an ex- ponential mechanism with an ℓ1-score function defined over an ...
5 days ago · This paper studies clustering algorithms for dynamically evolving graphs $\{G_t\}$, in which new edges (and potential new vertices) are added into a graph, ...
4 days ago · We first introduce a unified message-passing framework for designing spectral invariant GNNs, called Eigenspace Projection GNN (EPNN). A comprehensive analysis ...
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7 days ago · A core task in single-cell data analysis is recovering the latent dimensions encoding the genetic and epigenetic landscapes inhabited by cell types and ...
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4 days ago · PDF | We build interpretable and lightweight transformer-like neural networks by unrolling iterative optimization algorithms that minimize graph.
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6 days ago · This paper studies sequence modeling for prediction tasks with long range dependencies. We propose a new formulation for state space models (SSMs) based on ...
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2 days ago · Empirical experiments on multiple real-world datasets demonstrate that DrGNN outperforms state-of-the-art deep graph representation baseline algorithms.
4 days ago · This paper addresses the optimization of scheduling for workers at a logistics depot using a combination of genetic algorithm and simulated annealing algorithm.
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