links to conference publications in graph-based deep learning
- graph-neural-networks
- graph-convolutional-networks
- graph
- deep-learning
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links to conference publications in graph-based deep learning
MapDB provides concurrent Maps, Sets and Queues backed by disk storage or off-heap-memory. It is a fast and easy to use embedded Java database engine.
Lightning ⚡️ fast forecasting with statistical and econometric models.
Run Keras models in the browser, with GPU support using WebGL
Easily train your own text-generating neural network of any size and complexity on any text dataset with a few lines of code.
M3 monorepo - Distributed TSDB, Aggregator and Query Engine, Prometheus Sidecar, Graphite Compatible, Metrics Platform
Database based asynchronous priority queue system -- Extracted from Shopify
High-level library to help with training and evaluating neural networks in PyTorch flexibly and transparently.
A simple, battle-tested and generic set type for the Go language. Trusted by GoogleCloudPlatform, Docker, 1Password, Ethereum and Hashicorp.
A fast single-producer, single-consumer lock-free queue for C++
A unified, comprehensive and efficient recommendation library
:earth_americas: Simple and ready-to-use tutorials for TensorFlow
Accompanying source code for Machine Learning with TensorFlow. Refer to the book for step-by-step explanations.
Python package for the evaluation of odometry and SLAM
Scalable and user friendly neural :brain: forecasting algorithms.
Promise queue with concurrency control
Near-zero config .NET library that makes advanced application features like Task Scheduling, Caching, Queuing, Event Broadcasting, and more a breeze!
Dashboard and code-driven configuration for Laravel queues.
Typhoeus wraps libcurl in order to make fast and reliable requests.
Queueing jobs in Postgres from Node.js like a boss
₿ A guide (for humans!) to cryto-currencies and their algos.
A simple, fast, robust job/task queue for Node.js, backed by Redis.
The repository host gives no description.
Matlab/Octave toolbox for deep learning. Includes Deep Belief Nets, Stacked Autoencoders, Convolutional Neural Nets, Convolutional Autoencoders and vanilla Neural Nets. Each method has examples to get you started.