1 code implementation • 27 Sep 2023 • Yijun Tian, Huan Song, Zichen Wang, Haozhu Wang, Ziqing Hu, Fang Wang, Nitesh V. Chawla, Panpan Xu
While existing work has explored utilizing knowledge graphs (KGs) to enhance language modeling via joint training and customized model architectures, applying this to LLMs is problematic owing to their large number of parameters and high computational cost.
no code implementations • 12 May 2023 • Jie Xu, Lu Lu, Sen yang, Bilin Liang, Xinwei Peng, Jiali Pang, Jinru Ding, Xiaoming Shi, Lingrui Yang, Huan Song, Kang Li, Xin Sun, Shaoting Zhang
The responses generated by chatbots based on LLMs are recorded for blind evaluations by five licensed medical experts.
no code implementations • 18 Feb 2022 • Huan Song, Zeng Dai, Panpan Xu, Liu Ren
GraphQ provides a visual query interface with a query editor and a multi-scale visualization of the results, as well as a user feedback mechanism for refining the results with additional constraints.
1 code implementation • 3 Jan 2020 • Shen Yan, Huan Song, Nanxiang Li, Lincan Zou, Liu Ren
Unsupervised domain adaptation studies the problem of utilizing a relevant source domain with abundant labels to build predictive modeling for an unannotated target domain.
Ranked #49 on Domain Generalization on PACS
no code implementations • 8 Apr 2019 • Vivek Sivaraman Narayanaswamy, Sameeksha Katoch, Jayaraman J. Thiagarajan, Huan Song, Andreas Spanias
We also investigate the impact of dense connections on the extraction process that encourage feature reuse and better gradient flow.
no code implementations • 1 Nov 2018 • Vivek Sivaraman Narayanaswamy, Jayaraman J. Thiagarajan, Huan Song, Andreas Spanias
State-of-the-art speaker diarization systems utilize knowledge from external data, in the form of a pre-trained distance metric, to effectively determine relative speaker identities to unseen data.
no code implementations • 2 Oct 2018 • Uday Shankar Shanthamallu, Jayaraman J. Thiagarajan, Huan Song, Andreas Spanias
Though deep network embeddings, e. g. DeepWalk, are widely adopted for community discovery, we argue that feature learning with random node attributes, using graph neural networks, can be more effective.
no code implementations • 20 Sep 2018 • Huan Song, Jayaraman J. Thiagarajan
Inferencing with network data necessitates the mapping of its nodes into a vector space, where the relationships are preserved.
no code implementations • 4 Aug 2018 • Huan Song, Megan Willi, Jayaraman J. Thiagarajan, Visar Berisha, Andreas Spanias
In automatic speech processing systems, speaker diarization is a crucial front-end component to separate segments from different speakers.
no code implementations • 15 Nov 2017 • Huan Song, Jayaraman J. Thiagarajan, Prasanna Sattigeri, Andreas Spanias
To this end, we develop the DKMO (Deep Kernel Machine Optimization) framework, that creates an ensemble of dense embeddings using Nystrom kernel approximations and utilizes deep learning to generate task-specific representations through the fusion of the embeddings.
no code implementations • 10 Nov 2017 • Huan Song, Deepta Rajan, Jayaraman J. Thiagarajan, Andreas Spanias
With widespread adoption of electronic health records, there is an increased emphasis for predictive models that can effectively deal with clinical time-series data.
no code implementations • 28 Dec 2016 • Huan Song, Jayaraman J. Thiagarajan, Prasanna Sattigeri, Karthikeyan Natesan Ramamurthy, Andreas Spanias
Kernel fusion is a popular and effective approach for combining multiple features that characterize different aspects of data.