Search Results for "graph" - Page 2
Sort By:
Showing 70 open source projects for "graph"
View related business solutions-
Our Free Plans just got better! | Auth0You asked, we delivered! Auth0 is excited to expand our Free and Paid plans to include more options so you can focus on building, deploying, and scaling applications without having to worry about your security. Auth0 now, thank yourself later.
-
Cloud tools for web scraping and data extractionAutomate web data collection with cloud tools that handle anti-bot measures, browser rendering, and data transformation out of the box. Extract content from any website, push to vector databases for RAG workflows, or pipe directly into your apps via API. Schedule runs, set up webhooks, and connect to your existing stack. Free tier available, then scale as you need to.
-
1
Gorgonia
Gorgonia is a library that helps facilitate machine learning in Go
...Specifically, the library is pretty low-level, like Theano, but has higher goals like Tensorflow. The primary goal for Gorgonia is to be a highly performant machine learning/graph computation-based library that can scale across multiple machines. It should bring the appeal of Go (simple compilation and deployment process) to the ML world. It's a long way from there currently, however, the baby steps are already there. The main reason to use Gorgonia is developer comfort. If you're using a Go stack extensively, now you have access to the ability to create production-ready machine learning systems in an environment that you are already familiar and comfortable with. -
2
DeepKE
An Open Toolkit for Knowledge Graph Extraction and Construction
Supporting cnSchema, standard supervised setting, low-resource setting, document-level setting and multi-modal setting for knowledge base population. DeepKE is a knowledge extraction toolkit supporting cnSchema, standard supervised, low-resource, and document-level scenarios for entity, relation, and attribution extraction. It allows developers and researchers to customize datasets and models to extract information from unstructured texts. DeepKE supports low-resource settings with only a... -
3
Lightning Bolts
Toolbox of models, callbacks, and datasets for AI/ML researchers
Bolts package provides a variety of components to extend PyTorch Lightning, such as callbacks & datasets, for applied research and production. Torch ORT converts your model into an optimized ONNX graph, speeding up training & inference when using NVIDIA or AMD GPUs. We can introduce sparsity during fine-tuning with SparseML, which ultimately allows us to leverage the DeepSparse engine to see performance improvements at inference time. -
4
Spektral
Graph Neural Networks with Keras and Tensorflow 2
Spektral is a Python library for graph deep learning, based on the Keras API and TensorFlow 2. The main goal of this project is to provide a simple but flexible framework for creating graph neural networks (GNNs). You can use Spektral for classifying the users of a social network, predicting molecular properties, generating new graphs with GANs, clustering nodes, predicting links, and any other task where data is described by graphs. -
Atera all-in-one platform IT management software with AI agentsAtera’s AI agents don’t just assist, they act. From detection to resolution, they handle incidents and requests instantly, taking your IT management from automated to autonomous.
-
5
hloc
Visual localization made easy with hloc
...It implements Hierarchical Localization, leveraging image retrieval and feature matching, and is fast, accurate, and scalable. This codebase won the indoor/outdoor localization challenges at CVPR 2020 and ECCV 2020, in combination with SuperGlue, our graph neural network for feature matching. We provide step-by-step guides to localize with Aachen, InLoc, and to generate reference poses for your own data using SfM. Just download the datasets and you're reading to go! The notebook pipeline_InLoc.ipynb shows the steps for localizing with InLoc. It's much simpler since a 3D SfM model is not needed. ... -
6
AminePlatform
Amine is a Multi-Layer Platform for the dev. of Intelligent Systems
Amine is an Artificial Intelligence Multi-Layer Java Open Source Platform dedicated to the development of various kinds of Intelligent Systems and Agents (Knowledge-Based, Ontology-Based, Conceptual Graph -CG- Based, NLP, Reasoning and Learning, Natural Language Processing, etc.). Ontology, KB can be created and manipulated with various processes. CG theory is used as the main knowledge representation language. Amine provides two languages: PROLOG+CG which extends PROLOG with CG and Amine modules, and SYNERGY which is a visual activation/propagation based language. ... -
7
DIG
A library for graph deep learning research
The key difference with current graph deep learning libraries, such as PyTorch Geometric (PyG) and Deep Graph Library (DGL), is that, while PyG and DGL support basic graph deep learning operations, DIG provides a unified testbed for higher level, research-oriented graph deep learning tasks, such as graph generation, self-supervised learning, explainability, 3D graphs, and graph out-of-distribution. -
8
OGB
Benchmark datasets, data loaders, and evaluators for graph machine
...We expect the benchmark datasets to evolve. OGB provides a diverse set of challenging and realistic benchmark datasets that are of varying sizes and cover a variety graph machine learning tasks, including prediction of node, link, and graph properties. OGB fully automates dataset processing. The OGB data loaders automatically download and process graphs, provide graph objects that are fully compatible with Pytorch Geometric and DGL. OGB provides standardized dataset splits and evaluators that allow for easy and reliable comparison of different models in a unified manner. -
9
Karate Club
An API Oriented Open-source Python Framework for Unsupervised Learning
Karate Club is an unsupervised machine learning extension library for NetworkX. Karate Club consists of state-of-the-art methods to do unsupervised learning on graph-structured data. To put it simply it is a Swiss Army knife for small-scale graph mining research. First, it provides network embedding techniques at the node and graph level. Second, it includes a variety of overlapping and non-overlapping community detection methods. Implemented methods cover a wide range of network science (NetSci, Complenet), data mining (ICDM, CIKM, KDD), artificial intelligence (AAAI, IJCAI) and machine learning (NeurIPS, ICML, ICLR) conferences, workshops, and pieces from prominent journals. -
Grafana: The open and composable observability platformGrafana is the open source analytics & monitoring solution for every database.
-
10
handson-ml
Teaching you the fundamentals of Machine Learning in python
handson-ml hosts the notebooks for the first edition of the same hands-on ML book, reflecting the tooling and idioms of its time while teaching durable concepts. It walks through supervised and unsupervised learning with scikit-learn, then introduces deep learning using the earlier TensorFlow 1 graph-execution style. The examples underscore fundamentals like bias-variance trade-offs, regularization, and proper validation, grounding learners before they move to deep nets. Even though the deep learning stack evolved, the classical ML sections remain highly relevant for production data problems. The code is crafted to be clear rather than clever, prioritizing readability for newcomers. ... -
11
pyntcloud
pyntcloud is a Python library for working with 3D point clouds
...Point clouds are one of the most relevant entities for representing three dimensional data these days, along with polygonal meshes (which are just a special case of point clouds with connectivity graph attached). In its simplest form, a point cloud is a set of points in a cartesian coordinate system. Accurate 3D point clouds can nowadays be (easily and cheaply) acquired from different sources. pyntcloud enables simple and interactive exploration of point cloud data, regardless of which sensor was used to generate it or what the use case is. ... -
12
Apache MXNet (incubating)
A flexible and efficient library for deep learning
Apache MXNet is an open source deep learning framework designed for efficient and flexible research prototyping and production. It contains a dynamic dependency scheduler that automatically parallelizes both symbolic and imperative operations. On top of this is a graph optimization layer, overall making MXNet highly efficient yet still portable, lightweight and scalable. -
13
Graph4NLP
Graph4nlp is the library for the easy use of Graph Neural Networks
Graph4NLP is an easy-to-use library for R&D at the intersection of Deep Learning on Graphs and Natural Language Processing (i.e., DLG4NLP). It provides both full implementations of state-of-the-art models for data scientists and also flexible interfaces to build customized models for researchers and developers with whole-pipeline support. Built upon highly-optimized runtime libraries including DGL , Graph4NLP has both high running efficiency and great extensibility. The architecture of... -
14
Machine Learning PyTorch Scikit-Learn
Code Repository for Machine Learning with PyTorch and Scikit-Learn
...However, after putting so much passion and hard work into the changes and new topics, we thought it deserved a new title. So, what’s new? There are many contents and additions, including the switch from TensorFlow to PyTorch, new chapters on graph neural networks and transformers, a new section on gradient boosting, and many more that I will detail in a separate blog post. For those who are interested in knowing what this book covers in general, I’d describe it as a comprehensive resource on the fundamental concepts of machine learning and deep learning. The first half of the book introduces readers to machine learning using scikit-learn, the defacto approach for working with tabular datasets. ... -
15
Awesome Explainable Graph Reasoning
A collection of research papers and software related to explainability
...Recently, explainability of deep models on images and texts has achieved significant progress. In the area of graph data, graph neural networks (GNNs) and their explainability are experiencing rapid developments. However, there is neither a unified treatment of GNN explainability methods, nor a standard benchmark and testbed for evaluations. In this survey, we provide a unified and taxonomic view of current GNN explainability methods. -
16
MACE
Deep learning inference framework optimized for mobile platforms
...UI responsiveness guarantee is sometimes obligatory when running a model. Mechanism like automatically breaking OpenCL kernel into small units is introduced to allow better preemption for the UI rendering task. Graph level memory allocation optimization and buffer reuse are supported. The core library tries to keep minimum external dependencies to keep the library footprint small. -
17
Awesome Decision Tree Papers
A collection of research papers on decision, classification, etc.
A collection of research papers on decision, classification and regression trees with implementations. -
18
CapsGNN
A PyTorch implementation of "Capsule Graph Neural Network"
A PyTorch implementation of "Capsule Graph Neural Network" (ICLR 2019). The high-quality node embeddings learned from the Graph Neural Networks (GNNs) have been applied to a wide range of node-based applications and some of them have achieved state-of-the-art (SOTA) performance. However, when applying node embeddings learned from GNNs to generate graph embeddings, the scalar node representation may not suffice to preserve the node/graph properties efficiently, resulting in sub-optimal graph embeddings. ... -
19
Minkowski Engine
Auto-diff neural network library for high-dimensional sparse tensors
The Minkowski Engine is an auto-differentiation library for sparse tensors. It supports all standard neural network layers such as convolution, pooling, unspooling, and broadcasting operations for sparse tensors. The Minkowski Engine supports various functions that can be built on a sparse tensor. We list a few popular network architectures and applications here. To run the examples, please install the package and run the command in the package root directory. Compressing a neural network to... -
20
Couler
Unified Interface for Constructing and Managing Workflows
Couler is a system designed for unified machine learning workflow optimization in the cloud. Couler endeavors to provide a unified interface for constructing and optimizing workflows across various workflow engines, such as Argo Workflows, Tekton Pipelines, and Apache Airflow. Couler enhances workflow efficiency through features like Autonomous Workflow Construction, Automatic Artifact Caching Mechanisms, Big Workflow Auto Parallelism Optimization, and Automatic Hyperparameters Tuning. -
21
Awesome Community Detection Research
A curated list of community detection research papers
A collection of community detection papers. A curated list of community detection research papers with implementations. Similar collections about graph classification, classification/regression tree, fraud detection, and gradient boosting papers with implementations. -
22
TFLearn
Deep learning library featuring a higher-level API for TensorFlow
...Full transparency over Tensorflow. All functions are built over tensors and can be used independently of TFLearn. Powerful helper functions to train any TensorFlow graph, with support of multiple inputs, outputs, and optimizers. Easy and beautiful graph visualization, with details about weights, gradients, activations, and more. Effortless device placement for using multiple CPU/GPU. The high-level API currently supports the most of the recent deep learning models, such as Convolutions, LSTM, BiRNN, BatchNorm, etc. -
23
gradslam
gradslam is an open source differentiable dense SLAM library
...However, learning representations for SLAM has been an open question, because traditional SLAM systems are not end-to-end differentiable. In this work, we present gradSLAM, a differentiable computational graph take on SLAM. Leveraging the automatic differentiation capabilities of computational graphs, gradSLAM enables the design of SLAM systems that allow for gradient-based learning across each of their components, or the system as a whole. -
24
Euler
A distributed graph deep learning framework.
...Data in the fields of text, speech, and images is easier to process into a grid-like type of Euclidean space, which is suitable for processing by existing deep learning models. Graph is a data type in non-Euclidean space and cannot be directly applied to existing methods, requiring a specially designed graph neural network system. Graph-based learning methods such as graph neural networks combine end-to-end learning with inductive reasoning, and are expected to solve a series of problems such as relational reasoning and interpretability that deep learning cannot handle. -
25
StellarGraph
Machine Learning on Graphs
StellarGraph is a Python library for machine learning on graphs and networks. The StellarGraph library offers state-of-the-art algorithms for graph machine learning, making it easy to discover patterns and answer questions about graph-structured data. It can solve many machine learning tasks. Graph-structured data represent entities as nodes (or vertices) and relationships between them as edges (or links), and can include data associated with either as attributes. For example, a graph can contain people as nodes and friendships between them as links, with data like a person’s age and the date a friendship was established. ...
