237 citations · 652 across the 21 of their papers we have counts for
11 papers · 1 filter
PreDiff: Precipitation Nowcasting with Latent Diffusion Models
Zhihan Gao, Xingjian Shi, Boran Han +6
Earth system forecasting has traditionally relied on complex physical models that are computationally expensive and require significant domain expertise. In the past decade, the un…
XTab: Cross-table Pretraining for Tabular Transformers
Bingzhao Zhu, Xingjian Shi, Nick Erickson +3
The success of self-supervised learning in computer vision and natural language processing has motivated pretraining methods on tabular data. However, most existing tabular self-su…
Tailoring Instructions to Student's Learning Levels Boosts Knowledge Distillation
Yuxin Ren, Zihan Zhong, Xingjian Shi +3
It has been commonly observed that a teacher model with superior performance does not necessarily result in a stronger student, highlighting a discrepancy between current teacher t…
A Cheaper and Better Diffusion Language Model with Soft-Masked Noise
Jiaao Chen, Aston Zhang, Mu Li +2
Diffusion models that are based on iterative denoising have been recently proposed and leveraged in various generation tasks like image generation. Whereas, as a way inherently bui…
Prompt Pre-Training with Twenty-Thousand Classes for Open-Vocabulary Visual Recognition
Shuhuai Ren, Aston Zhang, Yi Zhu +5
This work proposes POMP, a prompt pre-training method for vision-language models. Being memory and computation efficient, POMP enables the learned prompt to condense semantic infor…
RAF: Holistic Compilation for Deep Learning Model Training
Cody Hao Yu, Haozheng Fan, Guangtai Huang +9
As deep learning is pervasive in modern applications, many deep learning frameworks are presented for deep learning practitioners to develop and train DNN models rapidly. Meanwhile…
LayoutDiffuse: Adapting Foundational Diffusion Models for Layout-to-Image Generation
Jiaxin Cheng, Xiao Liang, Xingjian Shi +3
Layout-to-image generation refers to the task of synthesizing photo-realistic images based on semantic layouts. In this paper, we propose LayoutDiffuse that adapts a foundational d…
AIM: Adapting Image Models for Efficient Video Action Recognition
Taojiannan Yang, Yi Zhu, Yusheng Xie +3
Recent vision transformer based video models mostly follow the ``image pre-training then finetuning" paradigm and have achieved great success on multiple video benchmarks. However,…
Towards Geospatial Foundation Models via Continual Pretraining
Matias Mendieta, Boran Han, Xingjian Shi +2
Geospatial technologies are becoming increasingly essential in our world for a wide range of applications, including agriculture, urban planning, and disaster response. To help imp…
Multimodal Chain-of-Thought Reasoning in Language Models
Zhuosheng Zhang, Aston Zhang, Mu Li +3
Large language models (LLMs) have shown impressive performance on complex reasoning by leveraging chain-of-thought (CoT) prompting to generate intermediate reasoning chains as the…
Parameter-Efficient Fine-Tuning Design Spaces
Jiaao Chen, Aston Zhang, Xingjian Shi +3
Parameter-efficient fine-tuning aims to achieve performance comparable to fine-tuning, using fewer trainable parameters. Several strategies (e.g., Adapters, prefix tuning, BitFit,…