graph construction
217 papers with code • 0 benchmarks • 4 datasets
Benchmarks
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Libraries
Use these libraries to find graph construction models and implementationsMost implemented papers
Visualizing Large-scale and High-dimensional Data
We propose the LargeVis, a technique that first constructs an accurately approximated K-nearest neighbor graph from the data and then layouts the graph in the low-dimensional space.
EFANNA : An Extremely Fast Approximate Nearest Neighbor Search Algorithm Based on kNN Graph
In this paper, we propose EFANNA, an extremely fast approximate nearest neighbor search algorithm based on $k$NN Graph.
sCAKE: Semantic Connectivity Aware Keyword Extraction
Combination of the proposed graph construction and scoring methods leads to a novel, parameterless keyword extraction method (sCAKE) based on semantic connectivity of words in the document.
DyNet: The Dynamic Neural Network Toolkit
In the static declaration strategy that is used in toolkits like Theano, CNTK, and TensorFlow, the user first defines a computation graph (a symbolic representation of the computation), and then examples are fed into an engine that executes this computation and computes its derivatives.
Two-Stream Adaptive Graph Convolutional Networks for Skeleton-Based Action Recognition
In addition, the second-order information (the lengths and directions of bones) of the skeleton data, which is naturally more informative and discriminative for action recognition, is rarely investigated in existing methods.
Structured Sparse R-CNN for Direct Scene Graph Generation
The key to our method is a set of learnable triplet queries and a structured triplet detector which could be jointly optimized from the training set in an end-to-end manner.
X-LoRA: Mixture of Low-Rank Adapter Experts, a Flexible Framework for Large Language Models with Applications in Protein Mechanics and Molecular Design
Starting with a set of pre-trained LoRA adapters, our gating strategy uses the hidden states to dynamically mix adapted layers, allowing the resulting X-LoRA model to draw upon different capabilities and create never-before-used deep layer-wise combinations to solve tasks.
Probabilistic Weather Forecasting with Hierarchical Graph Neural Networks
While most current machine learning models focus on deterministic forecasts, accurately capturing the uncertainty in the chaotic weather system calls for probabilistic modeling.
Neighborhood and Graph Constructions using Non-Negative Kernel Regression
Data-driven neighborhood definitions and graph constructions are often used in machine learning and signal processing applications.
Learning to Propagate Labels: Transductive Propagation Network for Few-shot Learning
The goal of few-shot learning is to learn a classifier that generalizes well even when trained with a limited number of training instances per class.