Andrew H. Song

According to our database, Andrew H. Song authored at least 26 papers between 2018 and 2024.

Collaborative distances:

• Dijkstra number of four.
• Erdős number of four.

2024

HEST-1k: A Dataset for Spatial Transcriptomics and Histology Image Analysis.

Guillaume Jaume, Paul Doucet, Andrew H. Song, Ming Y. Lu, Cristina Almagro-Pérez, Sophia J. Wagner, Anurag J. Vaidya, Richard J. Chen, Drew F. K. Williamson, Ahrong Kim, Faisal Mahmood

CoRR, 2024

Artificial Intelligence for Digital and Computational Pathology.

Andrew H. Song, Guillaume Jaume, Drew F. K. Williamson, Ming Y. Lu, Anurag Vaidya, Tiffany R. Miller, Faisal Mahmood

CoRR, 2024

Two-Phase Multitask Autoencoder-Based Deep Learning Framework for Subject-Independent EEG Motor Imagery Classification.

ChangGyun Jin, Andrew H. Song, Seong-Eun Kim

IEEE Access, 2024

Multimodal Prototyping for cancer survival prediction.

Andrew H. Song, Richard J. Chen, Guillaume Jaume, Anurag J. Vaidya, Alexander S. Baras, Faisal Mahmood

Proceedings of the Forty-first International Conference on Machine Learning, 2024

Multistain Pretraining for Slide Representation Learning in Pathology.

Guillaume Jaume, Anurag Vaidya, Andrew Zhang, Andrew H. Song, Richard J. Chen, Sharifa Sahai, Dandan Mo, Emilio Madrigal, Long Phi Le, Faisal Mahmood

Proceedings of the Computer Vision - ECCV 2024, 2024

Morphological Prototyping for Unsupervised Slide Representation Learning in Computational Pathology.

Andrew H. Song, Richard J. Chen, Tong Ding, Drew F. K. Williamson, Guillaume Jaume, Faisal Mahmood

Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, 2024

Transcriptomics-Guided Slide Representation Learning in Computational Pathology.

Guillaume Jaume, Lukas Oldenburg, Anurag Vaidya, Richard J. Chen, Drew F. K. Williamson, Thomas Peeters, Andrew H. Song, Faisal Mahmood

Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, 2024

Triage of 3D pathology data via 2.5D multiple-instance learning to guide pathologist assessments.

Gan Gao, Andrew H. Song, Fiona Wang, David Brenes, Rui Wang, Sarah S. L. Chow, Kevin W. Bishop, Lawrence D. True, Faisal Mahmood, Jonathan T. C. Liu

Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, 2024

2023

A General-Purpose Self-Supervised Model for Computational Pathology.

Richard J. Chen, Tong Ding, Ming Y. Lu, Drew F. K. Williamson, Guillaume Jaume, Bowen Chen, Andrew Zhang, Daniel Shao, Andrew H. Song, Muhammad Shaban, Mane Williams, Anurag Vaidya, Sharifa Sahai, Lukas Oldenburg, Luca L. Weishaupt, Judy J. Wang, Walt Williams, Long Phi Le, Georg Gerber, Faisal Mahmood

CoRR, 2023

Weakly Supervised AI for Efficient Analysis of 3D Pathology Samples.

Andrew H. Song, Mane Williams, Drew F. K. Williamson, Guillaume Jaume, Andrew Zhang, Bowen Chen, Robert Serafin, Jonathan T. C. Liu, Alexander S. Baras, Anil V. Parwani, Faisal Mahmood

CoRR, 2023

2022

Generative models for neural time series with structured domain priors.

Andrew H. Song

PhD thesis, 2022

Covariance-Free Sparse Bayesian Learning.

Alexander Lin, Andrew H. Song, Berkin Bilgic, Demba E. Ba

IEEE Trans. Signal Process., 2022

Gaussian Process Convolutional Dictionary Learning.

Andrew H. Song, Bahareh Tolooshams, Demba E. Ba

IEEE Signal Process. Lett., 2022

Adaptive State-Space Multitaper Spectral Estimation.

Andrew H. Song, Seong-Eun Kim, Emery N. Brown

IEEE Signal Process. Lett., 2022

Incorporating Intratumoral Heterogeneity into Weakly-Supervised Deep Learning Models via Variance Pooling.

Iain Carmichael, Andrew H. Song, Richard J. Chen, Drew F. K. Williamson, Tiffany Y. Chen, Faisal Mahmood

Proceedings of the Medical Image Computing and Computer Assisted Intervention - MICCAI 2022, 2022

High-Dimensional Sparse Bayesian Learning without Covariance Matrices.

Alexander Lin, Andrew H. Song, Berkin Bilgic, Demba E. Ba

Proceedings of the IEEE International Conference on Acoustics, 2022

Mixture Model Auto-Encoders: Deep Clustering Through Dictionary Learning.

Alexander Lin, Andrew H. Song, Demba E. Ba

Proceedings of the IEEE International Conference on Acoustics, 2022

2021

PLSO: A generative framework for decomposing nonstationary time-series into piecewise stationary oscillatory components.

Andrew H. Song, Demba E. Ba, Emery N. Brown

Proceedings of the Thirty-Seventh Conference on Uncertainty in Artificial Intelligence, 2021

2020

Convolutional Dictionary Learning With Grid Refinement.

Andrew H. Song, Francisco J. Flores, Demba E. Ba

IEEE Trans. Signal Process., 2020

Convolutional dictionary learning based auto-encoders for natural exponential-family distributions.

Bahareh Tolooshams, Andrew H. Song, Simona Temereanca, Demba E. Ba

Proceedings of the 37th International Conference on Machine Learning, 2020

Channel-Attention Dense U-Net for Multichannel Speech Enhancement.

Bahareh Tolooshams, Ritwik Giri, Andrew H. Song, Umut Isik, Arvindh Krishnaswamy

Proceedings of the 2020 IEEE International Conference on Acoustics, 2020

2019

Fast Convolutional Dictionary Learning off the Grid.

Andrew H. Song, Francisco J. Flores, Demba E. Ba

CoRR, 2019

Deep Exponential-Family Auto-Encoders.

Bahareh Tolooshams, Andrew H. Song, Simona Temereanca, Demba E. Ba

CoRR, 2019

Multitaper Infinite Hidden Markov Model for EEG.

Andrew H. Song, Leon Chlon, Hugo Soulat, John Tauber, Sandya Subramanian, Demba E. Ba, Michael J. Prerau

Proceedings of the 41st Annual International Conference of the IEEE Engineering in Medicine and Biology Society, 2019

2018

Multitaper Spectral Estimation HDP-HMMs for EEG Sleep Inference.

Leon Chlon, Andrew H. Song, Sandya Subramanian, Hugo Soulat, John Tauber, Demba E. Ba, Michael J. Prerau

CoRR, 2018

A Smoother State Space Multitaper Spectrogram.

Andrew H. Song, Sourish Chakravarty, Emery N. Brown

Proceedings of the 40th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, 2018