PyTorch tutorials.

LDA for semisupervised topic modeling

《Pytorch实用教程》（第二版）无论是零基础入门，还是CV、NLP、LLM项目应用，或是进阶工程化部署落地，在这里都有。相信在本书的帮助下，读者将能够轻松掌握 PyTorch 的使用，成为一名优秀的深度学习工程师。

Materials for JKSU-CIS paper "Content-based quality evaluation of scientific papers using coarse feature and knowledge entity network"

TextAttack 🐙 is a Python framework for adversarial attacks, data augmentation, and model training in NLP https://textattack.readthedocs.io/en/master/

Data augmentation for NLP, presented at EMNLP 2019

the code for this paper : Automatic Recognition and Classification of Future Work Sentences from Academic Atricles in a Specific Domain

Contrastive Language-Image Pretraining

ICME 2022: Few-shot Multi-modal Sentiment Analysis with Prompt-based Vision-aware Language Modeling

List of useful data augmentation resources. You will find here some not common techniques, libraries, links to GitHub repos, papers, and others.

Pytorch Implementations of large number classical backbone CNNs, data enhancement, torch loss, attention, visualization and some common algorithms.

A comprehensive mapping database of English to Chinese technical vocabulary in the artificial intelligence domain

Measuring the Innovation of Method Knowledge Elements in Scientific Literature

PyTorch implementation of the NIPS-17 paper "Poincaré Embeddings for Learning Hierarchical Representations"

Pytorch Implementation of Tensor Fusion Networks for multimodal sentiment analysis.

Dataset and codes for our IJCAI 2019 paper "Adapting BERT for Target-Oriented Multimodal Sentiment Classification"

Notebooks for upcoming fastai book (draft / incomplete)