activity
20172022
most citedImprove Unsupervised Domain Adaptation with Mixup Training

123 citations · 146 across the 4 of their papers we have counts for

collaborators

8 papers

cs.LG202215 cited

Interactive Visual Pattern Search on Graph Data via Graph Representation Learning

Huan Song, Zeng Dai, Panpan Xu +1

Graphs are a ubiquitous data structure to model processes and relations in a wide range of domains. Examples include control-flow graphs in programs and semantic scene graphs in im…

stat.ML2020123 cited

Improve Unsupervised Domain Adaptation with Mixup Training

Shen Yan, Huan Song, Nanxiang Li +2

Unsupervised domain adaptation studies the problem of utilizing a relevant source domain with abundant labels to build predictive modeling for an unannotated target domain. Recent…

cs.LG20195 cited

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

Vivek Sivaraman Narayanaswamy, Sameeksha Katoch, Jayaraman J. Thiagarajan +2

Modern audio source separation techniques rely on optimizing sequence model architectures such as, 1D-CNNs, on mixture recordings to generalize well to unseen mixtures. Specificall…

cs.SI2018

Improved Deep Embeddings for Inferencing with Multi-Layered Networks

Huan Song, Jayaraman J. Thiagarajan

Inferencing with network data necessitates the mapping of its nodes into a vector space, where the relationships are preserved. However, with multi-layered networks, where multiple…

stat.ML2018

Designing an Effective Metric Learning Pipeline for Speaker Diarization

Vivek Sivaraman Narayanaswamy, Jayaraman J. Thiagarajan, Huan Song +1

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 identitie…

stat.ML2018

GrAMME: Semi-Supervised Learning using Multi-layered Graph Attention Models

Uday Shankar Shanthamallu, Jayaraman J. Thiagarajan, Huan Song +1

Modern data analysis pipelines are becoming increasingly complex due to the presence of multi-view information sources. While graphs are effective in modeling complex relationships…