Community detection in node-attributed social networks: a survey
P Chunaev - Computer Science Review, 2020 - Elsevier
Community detection is a fundamental problem in social network analysis consisting,
roughly speaking, in unsupervised dividing social actors (modeled as nodes in a social …
roughly speaking, in unsupervised dividing social actors (modeled as nodes in a social …
Hierarchical materials from high information content macromolecular building blocks: construction, dynamic interventions, and prediction
Hierarchical materials that exhibit order over multiple length scales are ubiquitous in nature.
Because hierarchy gives rise to unique properties and functions, many have sought …
Because hierarchy gives rise to unique properties and functions, many have sought …
Stable bias: Evaluating societal representations in diffusion models
As machine learning-enabled Text-to-Image (TTI) systems are becoming increasingly
prevalent and seeing growing adoption as commercial services, characterizing the social …
prevalent and seeing growing adoption as commercial services, characterizing the social …
Stable bias: Analyzing societal representations in diffusion models
As machine learning-enabled Text-to-Image (TTI) systems are becoming increasingly
prevalent and seeing growing adoption as commercial services, characterizing the social …
prevalent and seeing growing adoption as commercial services, characterizing the social …
Survey of vector database management systems
There are now over 20 commercial vector database management systems (VDBMSs), all
produced within the past five years. But embedding-based retrieval has been studied for …
produced within the past five years. But embedding-based retrieval has been studied for …
Learning to simulate complex physics with graph networks
A Sanchez-Gonzalez, J Godwin… - International …, 2020 - proceedings.mlr.press
Here we present a machine learning framework and model implementation that can learn to
simulate a wide variety of challenging physical domains, involving fluids, rigid solids, and …
simulate a wide variety of challenging physical domains, involving fluids, rigid solids, and …
Node similarity preserving graph convolutional networks
Graph Neural Networks (GNNs) have achieved tremendous success in various real-world
applications due to their strong ability in graph representation learning. GNNs explore the …
applications due to their strong ability in graph representation learning. GNNs explore the …
Umap: Uniform manifold approximation and projection for dimension reduction
L McInnes, J Healy, J Melville - arxiv preprint arxiv:1802.03426, 2018 - arxiv.org
UMAP (Uniform Manifold Approximation and Projection) is a novel manifold learning
technique for dimension reduction. UMAP is constructed from a theoretical framework based …
technique for dimension reduction. UMAP is constructed from a theoretical framework based …
[HTML][HTML] How much can k-means be improved by using better initialization and repeats?
In this paper, we study what are the most important factors that deteriorate the performance
of the k-means algorithm, and how much this deterioration can be overcome either by using …
of the k-means algorithm, and how much this deterioration can be overcome either by using …
Billion-scale similarity search with GPUs
Similarity search finds application in database systems handling complex data such as
images or videos, which are typically represented by high-dimensional features and require …
images or videos, which are typically represented by high-dimensional features and require …