Pinned Repositories
Adversarials-in-NLP
Beat_noisy_labels
Applied semi-supervision on the noisy-detected data via self-supervised technique: generate randomly rotation in training set and trained a ResNet model to predict both classification labels and rotation labels, and designed the loss function to focus more on self-supervision task for examples detected as noisy, while more on the original classification task for examples detected as clean.
BookRecommender
A recommender system of books using PySpark.
DSGA-1008_Deep_Learning
Courseworks for Deep Learning
DSGA-1011_NLP
Courseworks for Natural Language Processing with Representation Learning.
homepage-academic
shuang-gao.github.io
DS3001-ToolsforML
sharon-gao's Repositories
sharon-gao/Adversarials-in-NLP
sharon-gao/Beat_noisy_labels
Applied semi-supervision on the noisy-detected data via self-supervised technique: generate randomly rotation in training set and trained a ResNet model to predict both classification labels and rotation labels, and designed the loss function to focus more on self-supervision task for examples detected as noisy, while more on the original classification task for examples detected as clean.
sharon-gao/BookRecommender
A recommender system of books using PySpark.
sharon-gao/DSGA-1008_Deep_Learning
Courseworks for Deep Learning
sharon-gao/DSGA-1011_NLP
Courseworks for Natural Language Processing with Representation Learning.
sharon-gao/homepage-academic
sharon-gao/shuang-gao.github.io