Open-source alternatives to ConvE

DGL is a Python library for building and training graph neural networks. It functions as a graph message passing framework and a geometric deep learning tool, enabling the development of models that analyze graph-structured data. The library is designed for large-scale graph processing, utilizing distributed training and neighbor sampling to handle datasets with billions of edges. It provides specialized support for heterogeneous graph modeling, allowing for the representation of complex real-world entities with multiple node and edge types. Its capabilities cover a wide range of graph tasks

FalkorDB is a high-performance graph database management system and vector graph database. It serves as a knowledge graph construction tool and a GraphRAG knowledge store, integrating structured property graphs with vector search to provide grounded context for large language models. The engine is designed as a multi-tenant graph engine, capable of hosting thousands of isolated datasets within a single instance. The system distinguishes itself by using linear algebra for query execution, treating relationship tensors as matrix multiplications to achieve low-latency multi-hop traversals. It ut

Search and filter videos based on objects that appear in them using convolutional neural networks

Flower is a federated learning framework and distributed machine learning orchestrator designed to train models across decentralized devices. It functions as a privacy-preserving toolkit that enables model training and data analysis on local hardware, ensuring raw data remains on the device while contributing to a synchronized global model. The system employs an agnostic wrapper and integrator to connect diverse machine learning libraries, allowing different frameworks to operate within the same training loop. It uses a remote procedure call orchestrator to manage the exchange of model weight

Base pretrained models and datasets in pytorch (MNIST, SVHN, CIFAR10, CIFAR100, STL10, AlexNet, VGG16, VGG19, ResNet, Inception, SqueezeNet)

Hierarchical Attentive Recurrent Tracking

This is a PyTorch object detection framework that implements the Single Shot MultiBox Detector for identifying and localizing multiple objects within images and video. The project provides a neural network architecture designed for single-shot object detection, which predicts bounding boxes and class labels in one pass. The implementation includes a real-time object detector capable of processing live video streams to track and label objects across sequential frames. It also features a complete computer vision training pipeline for preparing image datasets and training model weights. The fra

This project is a TensorFlow-based neural style transfer tool and deep learning image processor. It uses convolutional neural networks to apply the artistic style of one image to the content of another through neural image synthesis. The system supports multi-style blending to combine artistic characteristics from several different images into a single output. It also includes color-preserving stylization, which maintains the original color palette of the source image by merging source color data with the luminance of the stylized result. The tool provides capabilities for style abstraction

Code for PackNet: Adding Multiple Tasks to a Single Network by Iterative Pruning

:speaker: Deep Learning & 3D Convolutional Neural Networks for Speaker Verification

:unlock: Lip Reading - Cross Audio-Visual Recognition using 3D Architectures

A PyTorch implementation of DenseNet.

Classification with PyTorch.