11 citations · 43 across the 14 of their papers we have counts for
14 papers
Improving Representation Learning for Histopathologic Images with Cluster Constraints
Weiyi Wu, Chongyang Gao, Joseph DiPalma +2
Recent advances in whole-slide image (WSI) scanners and computational capabilities have significantly propelled the application of artificial intelligence in histopathology slide a…
Bootstrapping Vision-Language Learning with Decoupled Language Pre-training
Yiren Jian, Chongyang Gao, Soroush Vosoughi
We present a novel methodology aimed at optimizing the application of frozen large language models (LLMs) for resource-intensive vision-language (VL) pre-training. The current para…
Joint Latent Topic Discovery and Expectation Modeling for Financial Markets
Lili Wang, Chenghan Huang, Chongyang Gao +2
In the pursuit of accurate and scalable quantitative methods for financial market analysis, the focus has shifted from individual stock models to those capturing interrelations bet…
Knowledge from Large-Scale Protein Contact Prediction Models Can Be Transferred to the Data-Scarce RNA Contact Prediction Task
Yiren Jian, Chongyang Gao, Chen Zeng +2
RNA, whose functionality is largely determined by its structure, plays an important role in many biological activities. The prediction of pairwise structural proximity between each…
Learning to Collocate Visual-Linguistic Neural Modules for Image Captioning
Xu Yang, Hanwang Zhang, Chongyang Gao +1
Humans tend to decompose a sentence into different parts like \textsc{sth do sth at someplace} and then fill each part with certain content. Inspired by this, we follow the \textit…
Non-Linguistic Supervision for Contrastive Learning of Sentence Embeddings
Yiren Jian, Chongyang Gao, Soroush Vosoughi
Semantic representation learning for sentences is an important and well-studied problem in NLP. The current trend for this task involves training a Transformer-based sentence encod…
Contrastive Learning for Prompt-Based Few-Shot Language Learners
Yiren Jian, Chongyang Gao, Soroush Vosoughi
The impressive performance of GPT-3 using natural language prompts and in-context learning has inspired work on better fine-tuning of moderately-sized models under this paradigm. F…
Embedding Hallucination for Few-Shot Language Fine-tuning
Yiren Jian, Chongyang Gao, Soroush Vosoughi
Few-shot language learners adapt knowledge from a pre-trained model to recognize novel classes from a few-labeled sentences. In such settings, fine-tuning a pre-trained language mo…
Non-Parallel Text Style Transfer with Self-Parallel Supervision
Ruibo Liu, Chongyang Gao, Chenyan Jia +2
The performance of existing text style transfer models is severely limited by the non-parallel datasets on which the models are trained. In non-parallel datasets, no direct mapping…
Knowledge Infused Decoding
Ruibo Liu, Guoqing Zheng, Shashank Gupta +5
Pre-trained language models (LMs) have been shown to memorize a substantial amount of knowledge from the pre-training corpora; however, they are still limited in recalling factuall…
MetaPix: Domain Transfer for Semantic Segmentation by Meta Pixel Weighting
Yiren Jian, Chongyang Gao
Training a deep neural model for semantic segmentation requires collecting a large amount of pixel-level labeled data. To alleviate the data scarcity problem presented in the real…
Auto-Parsing Network for Image Captioning and Visual Question Answering
Xu Yang, Chongyang Gao, Hanwang Zhang +1
We propose an Auto-Parsing Network (APN) to discover and exploit the input data's hidden tree structures for improving the effectiveness of the Transformer-based vision-language sy…
Embedding Heterogeneous Networks into Hyperbolic Space Without Meta-path
Lili Wang, Chongyang Gao, Chenghan Huang +3
Networks found in the real-world are numerous and varied. A common type of network is the heterogeneous network, where the nodes (and edges) can be of different types. Accordingly,…
An Empirical Survey of Unsupervised Text Representation Methods on Twitter Data
Lili Wang, Chongyang Gao, Jason Wei +3
The field of NLP has seen unprecedented achievements in recent years. Most notably, with the advent of large-scale pre-trained Transformer-based language models, such as BERT, ther…