5 citations · 8 across the 8 of their papers we have counts for
9 papers
Designing Transformers for Continuous Diffusion Language Models
Weijian Luo, Yujian Chen, Hao Jiang +2
Continuous diffusion language models (CDLMs) offer an alternative approach to parallel language generation; however, most existing studies directly use transformer architectures fr…
Enhancing Table Reasoning with Deterministic Table-State Rewards
Tung Sum Thomas Kwok, Xinyu Wang, Hengzhi He +9
Large Language Models (LLMs) struggle with multi-step reasoning over structured tables. The primary reason is the lack of explicit supervision for intermediate reasoning states. Ex…
Exploring Reasoning-Infused Text Embedding with Large Language Models for Zero-Shot Dense Retrieval
Yuxiang Liu, Tian Wang, Gourab Kundu +6
Transformer-based models such as BERT and E5 have significantly advanced text embedding by capturing rich contextual representations. However, many complex real-world queries requi…
TimeAutoDiff: A Unified Framework for Generation, Imputation, Forecasting, and Time-Varying Metadata Conditioning of Heterogeneous Time Series Tabular Data
Namjoon Suh, Yuning Yang, Din-Yin Hsieh +4
We present TimeAutoDiff, a unified latent-diffusion framework for four fundamental time-series tasks: unconditional generation, missing-data imputation, forecasting, and time-varyi…
CTSyn: A Foundation Model for Cross Tabular Data Generation
Xiaofeng Lin, Chenheng Xu, Matthew Yang +1
Generative Foundation Models (GFMs) have achieved remarkable success in producing high-quality synthetic data for images and text. However, their application to tabular data presen…
Neural Operators for Nonlinear Functionals on RKHS
Tian-Yi Zhou, Namjoon Suh, Guang Cheng +1
Motivated by the abundance of functional data, such as time series and images, we study the approximation and statistical learning of nonlinear functionals defined on reproducing k…
Theoretical Understanding of In-Context Learning in Shallow Transformers with Unstructured Data
Yue Xing, Xiaofeng Lin, Chenheng Xu +3
Large language models (LLMs) are powerful models that can learn concepts at the inference stage via in-context learning (ICL). While theoretical studies, e.g., \cite{zhang2023train…
A Survey on Statistical Theory of Deep Learning: Approximation, Training Dynamics, and Generative Models
Namjoon Suh, Guang Cheng
In this article, we review the literature on statistical theories of neural networks from three perspectives: approximation, training dynamics and generative models. In the first p…
AutoDiff: combining Auto-encoder and Diffusion model for tabular data synthesizing
Namjoon Suh, Xiaofeng Lin, Din-Yin Hsieh +2
Diffusion model has become a main paradigm for synthetic data generation in many subfields of modern machine learning, including computer vision, language model, or speech synthesi…