Search Results for author:
Found 6 papers, 5 papers with code
POUF: Prompt-oriented unsupervised fine-tuning for large pre-trained models
Through prompting, large-scale pre-trained models have become more expressive and powerful, gaining significant attention in recent years.
A Prototype-Oriented Clustering for Domain Shift with Source Privacy
Finally, it refines the target model on the target domain data without guidance from the source model.
Representing Mixtures of Word Embeddings with Mixtures of Topic Embeddings
This paper introduces a new topic-modeling framework where each document is viewed as a set of word embedding vectors and each topic is modeled as an embedding vector in the same embedding space.
Alignment Attention by Matching Key and Query Distributions
The neural attention mechanism has been incorporated into deep neural networks to achieve state-of-the-art performance in various domains.
A Prototype-Oriented Framework for Unsupervised Domain Adaptation
Existing methods for unsupervised domain adaptation often rely on minimizing some statistical distance between the source and target samples in the latent space.
Contextual Dropout: An Efficient Sample-Dependent Dropout Module
However, the quality of uncertainty estimation is highly dependent on the dropout probabilities.
