Stars
Reading list for research topics in multimodal machine learning
Python code for "Probabilistic Machine learning" book by Kevin Murphy
XCOPA: A Multilingual Dataset for Causal Commonsense Reasoning
Beautiful visualizations of how language differs among document types.
A list of recent papers about Meta / few-shot learning methods applied in NLP areas.
Pretrain, finetune and deploy AI models on multiple GPUs, TPUs with zero code changes.
higher is a pytorch library allowing users to obtain higher order gradients over losses spanning training loops rather than individual training steps.
Optimus: the first large-scale pre-trained VAE language model
A PyTorch Library for Meta-learning Research
😎 An up-to-date & curated list of awesome semi-supervised learning papers, methods & resources.
A collection of extensions and data-loaders for few-shot learning & meta-learning in PyTorch
A curated list of awesome work on VAEs, disentanglement, representation learning, and generative models.
Solutions to "Machine Learning: A Probabilistic Perspective"
Tensorflow Implementation of Stochastic Wasserstein Autoencoder for Probabilistic Sentence Generation (NAACL 2019).
Deep generative models for semi-supervised learning.
Facebook AI Research Sequence-to-Sequence Toolkit written in Python.
[WNGT(2019)] On the Importance of the Kullback-Leibler Divergence Term in Variational Autoencoders for Text Generation
A large annotated semantic parsing corpus for developing natural language interfaces.
Unsupervised clustering with (Gaussian mixture) VAEs
A Corpus for Multilingual Document Classification in Eight Languages.
Introductory Python course for computational lingustics
[NAACL(2019)] Generating Knowledge Graph Paths from Textual Definitions using Sequence-to-Sequence Models
Acceptance rates for the major AI conferences
A library for Multilingual Unsupervised or Supervised word Embeddings
Stanford NLP Python library for tokenization, sentence segmentation, NER, and parsing of many human languages