Finally, my notes are online now: NeurIPS_notes_Irene
Author Archives: Irene
Expanded AAN.how: Learning NLP Made Much Easier!
More resources! We have almost doubled the number of manually collected resources since our previous release, now totaling over 13,000.
Slideshare (6): Cross-lingual Paper reading notes
During this summer, I did a project on cross-lingual NLP tasks. Recently I was working my notes and I organized them into a better format. I would like to share some of the notes with the readers who might be interested in this topic. Cross_lingual_NLP(PDF) Papers covered: A Robust Abstractive System for Cross-Lingual Summarization MASS:Continue reading “Slideshare (6): Cross-lingual Paper reading notes”
Slideshare (5): Graph Neural Networks
A quick introduction to Graph Neural Networks: Graph Neural Networks List of papers covered: Semi-Supervised Classification with Graph Convolutional Networks Graph Attention Networks Variational Graph Auto-Encoders Keep It Simple: Graph Autoencoders Without Graph Convolutional Networks
Prepare for the Interviews!
For the past few years after my Master’s, I did many jobs, long term, short term, internship, or full-time. I also had too many interviews, some of them I failed. Together with my friends, we had collected many materials, including basic algorithms, popular questions, basic machine learning knowledge, and deep learning knowledge. Then I organizedContinue reading “Prepare for the Interviews!”
Deep Learning 18: GANs with PyTorch
In this short post, I will share a very brief GAN (Generative Adversarial Network) model and in practice, how do we train it using PyTorch. Also, I will include some tips about training as I myself found it is hard to train, especially when working with my own data and model. Training GAN models IContinue reading “Deep Learning 18: GANs with PyTorch”
Deep Learning 17: text classification with BERT using PyTorch
Why BERT If you are a big fun of PyTorch and NLP, you must try to use the PyTorch based BERT implementation! If you have your own dataset and want to try the state-of-the-art model, BERT is a good choice. Please check the code from https://github.com/huggingface/pytorch-pretrained-BERT to get a close look. However, in this post,Continue reading “Deep Learning 17: text classification with BERT using PyTorch”
Understanding Variational Graph Auto-Encoders
Variational Auto-Encoders My post about Auto-encoder. For Variational Auto-Encoders (VAE) (from paper Auto-Encoding Variational Bayes), we actually add latent variables to the existing Autoencoders. The main idea is, we want to restrict the parameters from a known distribution. Why we want this? We wish the generative model to provide more “creative” things. If the modelContinue reading “Understanding Variational Graph Auto-Encoders”
Resources for BioNLP: datasets and tools
Corpora for general medical texts Open Research Corpus Over 39 million published research papers in Computer Science, Neuroscience, and Biomedical. Full dataset 36G, not restricted.
Understanding Graph Convolutional Networks
Why Graphs? Graph Convolution Networks (GCNs) [0] deal with graphs where the data form with a graph structure. A typical graph is represented as G(V, E), where V is the collection of all the nodes and Eis the collection of all the edges.
IreneLi 