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Found 8 papers, 1 papers with code
Dynamic Silos: Increased Modularity in Intra-organizational Communication Networks during the Covid-19 Pandemic
Workplace communications around the world were drastically altered by Covid-19, related work-from-home orders, and the rise of remote work.
PACSET (Packed Serialized Trees): Reducing Inference Latency for Tree Ensemble Deployment
We present methods to serialize and deserialize tree ensembles that optimize inference latency when models are not already loaded into memory.
MANIFOLD FORESTS: CLOSING THE GAP ON NEURAL NETWORKS
In particular, DFs dominate other methods in tabular data, that is, when the feature space is unstructured, so that the signal is invariant to permuting feature indices.
Vertex Classification on Weighted Networks
This paper proposes a discrimination technique for vertices in a weighted network.
Semi-External Memory Sparse Matrix Multiplication for Billion-Node Graphs
In contrast, we scale sparse matrix multiplication beyond memory capacity by implementing sparse matrix dense matrix multiplication (SpMM) in a semi-external memory (SEM) fashion; i. e., we keep the sparse matrix on commodity SSDs and dense matrices in memory.
Distributed, Parallel, and Cluster Computing
Multiscale Dictionary Learning for Estimating Conditional Distributions
Nonparametric estimation of the conditional distribution of a response given high-dimensional features is a challenging problem.
Robust Multimodal Graph Matching: Sparse Coding Meets Graph Matching
We also present results on multimodal graphs and applications to collaborative inference of brain connectivity from alignment-free functional magnetic resonance imaging (fMRI) data.
Robust Vertex Classification
For random graphs distributed according to stochastic blockmodels, a special case of latent position graphs, adjacency spectral embedding followed by appropriate vertex classification is asymptotically Bayes optimal; but this approach requires knowledge of and critically depends on the model dimension.
