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ORCIDAndrew H. Song
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2020 – today
- 2024
[j5]ChangGyun Jin
, Andrew H. Song, Seong-Eun Kim:
Two-Phase Multitask Autoencoder-Based Deep Learning Framework for Subject-Independent EEG Motor Imagery Classification. IEEE Access 12: 77356-77367 (2024)- [c13]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:
Triage of 3D pathology data via 2.5D multiple-instance learning to guide pathologist assessments. CVPR Workshops 2024: 6955-6965 - [c12]Guillaume Jaume, Lukas Oldenburg, Anurag Vaidya, Richard J. Chen, Drew F. K. Williamson, Thomas Peeters, Andrew H. Song, Faisal Mahmood:
Transcriptomics-Guided Slide Representation Learning in Computational Pathology. CVPR 2024: 9632-9644 - [c11]Andrew H. Song, Richard J. Chen, Tong Ding, Drew F. K. Williamson, Guillaume Jaume, Faisal Mahmood:
Morphological Prototyping for Unsupervised Slide Representation Learning in Computational Pathology. CVPR 2024: 11566-11578 - [c10]Guillaume Jaume, Anurag Vaidya, Andrew Zhang, Andrew H. Song, Richard J. Chen, Sharifa Sahai, Dandan Mo, Emilio Madrigal, Long Phi Le, Faisal Mahmood:
Multistain Pretraining for Slide Representation Learning in Pathology. ECCV (33) 2024: 19-37 - [c9]Andrew H. Song, Richard J. Chen, Guillaume Jaume, Anurag J. Vaidya, Alexander S. Baras, Faisal Mahmood:
Multimodal Prototyping for cancer survival prediction. ICML 2024 - [i18]Andrew H. Song, Guillaume Jaume, Drew F. K. Williamson, Ming Y. Lu, Anurag Vaidya, Tiffany R. Miller, Faisal Mahmood:
Artificial Intelligence for Digital and Computational Pathology. CoRR abs/2401.06148 (2024) - [i17]Guillaume Jaume, Lukas Oldenburg, Anurag Vaidya, Richard J. Chen, Drew F. K. Williamson, Thomas Peeters, Andrew H. Song, Faisal Mahmood:
Transcriptomics-guided Slide Representation Learning in Computational Pathology. CoRR abs/2405.11618 (2024) - [i16]Andrew H. Song, Richard J. Chen, Tong Ding, Drew F. K. Williamson, Guillaume Jaume, Faisal Mahmood:
Morphological Prototyping for Unsupervised Slide Representation Learning in Computational Pathology. CoRR abs/2405.11643 (2024) - [i15]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:
Triage of 3D pathology data via 2.5D multiple-instance learning to guide pathologist assessments. CoRR abs/2406.07061 (2024) - [i14]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:
HEST-1k: A Dataset for Spatial Transcriptomics and Histology Image Analysis. CoRR abs/2406.16192 (2024) - [i13]Andrew H. Song, Richard J. Chen, Guillaume Jaume, Anurag J. Vaidya, Alexander S. Baras, Faisal Mahmood:
Multimodal Prototyping for cancer survival prediction. CoRR abs/2407.00224 (2024) - [i12]Guillaume Jaume, Anurag Vaidya, Andrew Zhang, Andrew H. Song, Richard J. Chen, Sharifa Sahai, Dandan Mo, Emilio Madrigal, Long Phi Le, Faisal Mahmood:
Multistain Pretraining for Slide Representation Learning in Pathology. CoRR abs/2408.02859 (2024) - 2023
- [i11]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:
Weakly Supervised AI for Efficient Analysis of 3D Pathology Samples. CoRR abs/2307.14907 (2023) - [i10]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:
A General-Purpose Self-Supervised Model for Computational Pathology. CoRR abs/2308.15474 (2023) - 2022
- [b1]Andrew H. Song:
Generative models for neural time series with structured domain priors. Massachusetts Institute of Technology, USA, 2022 - [j4]Andrew H. Song, Bahareh Tolooshams, Demba E. Ba:
Gaussian Process Convolutional Dictionary Learning. IEEE Signal Process. Lett. 29: 95-99 (2022) - [j3]Andrew H. Song, Seong-Eun Kim, Emery N. Brown:
Adaptive State-Space Multitaper Spectral Estimation. IEEE Signal Process. Lett. 29: 523-527 (2022) - [j2]Alexander Lin, Andrew H. Song, Berkin Bilgic, Demba E. Ba:
Covariance-Free Sparse Bayesian Learning. IEEE Trans. Signal Process. 70: 3818-3831 (2022) - [c8]Alexander Lin, Andrew H. Song, Berkin Bilgic, Demba E. Ba:
High-Dimensional Sparse Bayesian Learning without Covariance Matrices. ICASSP 2022: 1511-1515 - [c7]Alexander Lin, Andrew H. Song, Demba E. Ba:
Mixture Model Auto-Encoders: Deep Clustering Through Dictionary Learning. ICASSP 2022: 3368-3372 - [c6]Iain Carmichael, Andrew H. Song, Richard J. Chen, Drew F. K. Williamson, Tiffany Y. Chen, Faisal Mahmood:
Incorporating Intratumoral Heterogeneity into Weakly-Supervised Deep Learning Models via Variance Pooling. MICCAI (2) 2022: 387-397 - [i9]Alexander Lin, Andrew H. Song, Berkin Bilgic, Demba E. Ba:
High-Dimensional Sparse Bayesian Learning without Covariance Matrices. CoRR abs/2202.12808 (2022) - [i8]Iain Carmichael, Andrew H. Song, Richard J. Chen, Drew F. K. Williamson, Tiffany Y. Chen, Faisal Mahmood:
Incorporating intratumoral heterogeneity into weakly-supervised deep learning models via variance pooling. CoRR abs/2206.08885 (2022) - 2021
- [c5]Andrew H. Song, Demba E. Ba, Emery N. Brown:
PLSO: A generative framework for decomposing nonstationary time-series into piecewise stationary oscillatory components. UAI 2021: 1371-1381 - [i7]Andrew H. Song, Bahareh Tolooshams, Demba E. Ba:
Gaussian Process Convolutional Dictionary Learning. CoRR abs/2104.00530 (2021) - [i6]Alexander Lin, Andrew H. Song, Berkin Bilgic, Demba E. Ba:
Covariance-Free Sparse Bayesian Learning. CoRR abs/2105.10439 (2021) - [i5]Alexander Lin, Andrew H. Song, Demba E. Ba:
Mixture Model Auto-Encoders: Deep Clustering through Dictionary Learning. CoRR abs/2110.04683 (2021) - 2020
- [j1]Andrew H. Song, Francisco J. Flores, Demba E. Ba:
Convolutional Dictionary Learning With Grid Refinement. IEEE Trans. Signal Process. 68: 2558-2573 (2020) - [c4]Bahareh Tolooshams, Ritwik Giri, Andrew H. Song, Umut Isik, Arvindh Krishnaswamy:
Channel-Attention Dense U-Net for Multichannel Speech Enhancement. ICASSP 2020: 836-840 - [c3]Bahareh Tolooshams, Andrew H. Song, Simona Temereanca, Demba E. Ba:
Convolutional dictionary learning based auto-encoders for natural exponential-family distributions. ICML 2020: 9493-9503 - [i4]Bahareh Tolooshams, Ritwik Giri, Andrew H. Song, Umut Isik, Arvindh Krishnaswamy:
Channel-Attention Dense U-Net for Multichannel Speech Enhancement. CoRR abs/2001.11542 (2020)
2010 – 2019
- 2019
- [c2]Andrew H. Song, Leon Chlon, Hugo Soulat, John Tauber, Sandya Subramanian, Demba E. Ba, Michael J. Prerau:
Multitaper Infinite Hidden Markov Model for EEG. EMBC 2019: 5803-5807 - [i3]Bahareh Tolooshams, Andrew H. Song, Simona Temereanca, Demba E. Ba:
Deep Exponential-Family Auto-Encoders. CoRR abs/1907.03211 (2019) - [i2]Andrew H. Song, Francisco J. Flores, Demba E. Ba:
Fast Convolutional Dictionary Learning off the Grid. CoRR abs/1907.09063 (2019) - 2018
- [c1]Andrew H. Song, Sourish Chakravarty, Emery N. Brown:
A Smoother State Space Multitaper Spectrogram. EMBC 2018: 33-36 - [i1]Leon Chlon, Andrew H. Song, Sandya Subramanian, Hugo Soulat, John Tauber, Demba E. Ba, Michael J. Prerau:
Multitaper Spectral Estimation HDP-HMMs for EEG Sleep Inference. CoRR abs/1805.07300 (2018)
Coauthor Index
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