Search Results for author: Huan Song

Found 12 papers, 2 papers with code

Graph Neural Prompting with Large Language Models

1 code implementation27 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.

Knowledge Graphs Language Modelling +2

Interactive Visual Pattern Search on Graph Data via Graph Representation Learning

no code implementations18 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.

Graph Representation Learning

Improve Unsupervised Domain Adaptation with Mixup Training

1 code implementation3 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.

Domain Generalization Human Activity Recognition +2

Audio Source Separation via Multi-Scale Learning with Dilated Dense U-Nets

no code implementations8 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.

Audio Source Separation

Designing an Effective Metric Learning Pipeline for Speaker Diarization

no code implementations1 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.

Metric Learning speaker-diarization +1

Attention Models with Random Features for Multi-layered Graph Embeddings

no code implementations2 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.

Network Embedding Node Classification

Improved Deep Embeddings for Inferencing with Multi-Layered Networks

no code implementations20 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.

Community Detection Link Prediction +2

Triplet Network with Attention for Speaker Diarization

no code implementations4 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.

Metric Learning speaker-diarization +1

Optimizing Kernel Machines using Deep Learning

no code implementations15 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.

Computational Efficiency

Attend and Diagnose: Clinical Time Series Analysis using Attention Models

no code implementations10 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.

Time Series Time Series Analysis

A Deep Learning Approach To Multiple Kernel Fusion

no code implementations28 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.

Activity Recognition

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