Search Results for author: Xingzhi Sun

Found 5 papers, 2 papers with code

Bayesian Formulations for Graph Spectral Denoising

no code implementations27 Nov 2023 Sam Leone, Xingzhi Sun, Michael Perlmutter, Smita Krishnaswamy

In particular, we present algorithms for the cases where the signal is perturbed by Gaussian noise, dropout, and uniformly distributed noise.

Denoising

Graph topological property recovery with heat and wave dynamics-based features on graphs

no code implementations18 Sep 2023 Dhananjay Bhaskar, Yanlei Zhang, Charles Xu, Xingzhi Sun, Oluwadamilola Fasina, Guy Wolf, Maximilian Nickel, Michael Perlmutter, Smita Krishnaswamy

In this paper, we propose Graph Differential Equation Network (GDeNet), an approach that harnesses the expressive power of solutions to PDEs on a graph to obtain continuous node- and graph-level representations for various downstream tasks.

Interactive Molecular Discovery with Natural Language

1 code implementation21 Jun 2023 Zheni Zeng, Bangchen Yin, Shipeng Wang, Jiarui Liu, Cheng Yang, Haishen Yao, Xingzhi Sun, Maosong Sun, Guotong Xie, Zhiyuan Liu

Natural language is expected to be a key medium for various human-machine interactions in the era of large language models.

Property Prediction

Exploring the Impact of Model Scaling on Parameter-Efficient Tuning

1 code implementation4 Jun 2023 Yusheng Su, Chi-Min Chan, Jiali Cheng, Yujia Qin, Yankai Lin, Shengding Hu, Zonghan Yang, Ning Ding, Xingzhi Sun, Guotong Xie, Zhiyuan Liu, Maosong Sun

Our investigations reveal that model scaling (1) mitigates the effects of the positions of tunable parameters on performance, and (2) enables tuning methods to achieve performance comparable to full-parameter fine-tuning by optimizing fewer tunable parameters.

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