Asteroid is a Pytorch-based audio source separation toolkit that enables fast experimentation on common datasets. It comes with a source code that supports a large …
Keywords: Asteroid, Pytorch, audio source separation, toolkit, audio, sound processing, ConvTasnet, Tasnet, Deep clustering, DualPathRNN, Chimera++, Wavesplit
Attention-based Deep Multiple Instance Learning
The official implementation of a method for multiple instance learning using deep neural networks and attention mechanism.
Keywords: multiple instance learning, deep learning, attention, PyTorch, weak classification
The reference implementation for the attri2vec algorithm.
Keywords: Attri2vec, representation learning, graph machine learning, node classification, link prediction
AugLy is a data augmentations library that currently supports four modalities (audio, image, text & video) and over 100 augmentations. Each modality’s augmentations are contained …
Keywords: data augmentation, machine learning, Python, PyTorch
AutoGL is an AutoML framework & toolkit for machine learning on graphs. AutoGL is developed for researchers and developers to conduct AutoML on graph datasets …
Keywords: AutoML, graph neural architecture search, machine learning, graph neural networks, NAS, GraphNAS, Python, PyTorch
AvaTaR is a novel and automatic framework that optimizes an LLM agent to effectively use the provided tools and improve its performance on a given …
Keywords: large language models, agents, LLM, LLM agents, contrastive reasoning, tool usage, machine learning, Python, PyTorch
Backprop is a Python library for fine-tuning and deploying machine learning models. It includes pre-trained models for natural language processing, e.g., question answering, text summarisation, …
Keywords: neural networks, pre-trained models, fine-tuning, library, natural language processing, computer vision, Python, PyTorch
Implementation of multiple instance learning (MIL) with the interactions between bags modelled using a Bayesian graph neural network (GNN).
Keywords: multiple instance learning, graph neural networks, Bayesian, MIL, GNN, machine learning, Python, Keras, TensorFlow
Implements scalable variational inference methods for Bayesian structure learning. Bayesian Causal Discovery Nets (BCD Nets) is a variational inference framework for estimating a distribution over …
Keywords: causal machine learning, causal structure learning, Bayesian methods, variational inference, Python, Jax
A framework for benchmarking the performance of graph neural network algorithms including datasets.
Keywords: graph neural network, machine learning, datasets, benchmark, graph convolutions, PyTorch