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Alexander Lin
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2020 – today
- 2024
- [c13]Alexander Lin, Demba E. Ba:
An Efficient Algorithm For Clustered Multi-Task Compressive Sensing. ICASSP 2024: 2550-2554 - [c12]Sangyoon Lee, Francesca Branzoli, Thanh Nguyen, Ovidiu C. Andronesi, Alexander Lin, Roberto Liserre, Gerd Melkus, Clark Chen, Malgorzata Marjanska, Patrick J. Bolan:
A Deep Learning Approach for Placing Magnetic Resonance Spectroscopy Voxels in Brain Tumors. MICCAI (3) 2024: 543-552 - 2023
- [c11]Autumn Toney-Wails, Kornraphop Kawintiranon, Lisa Singh, Paul-Emmanuel Courtines, Alexander Lin, Haofei Zhang:
Investigating Scientific Misinformation Using Different Modes of Learning. SDU@AAAI 2023 - [c10]Alexander Lin, Bahareh Tolooshams, Yves F. Atchadé, Demba E. Ba:
Probabilistic Unrolling: Scalable, Inverse-Free Maximum Likelihood Estimation for Latent Gaussian Models. ICML 2023: 21153-21181 - [c9]Mohammad Al-Fares, Virginia Beauregard, Kevin Grant, Angus Griffith, Jahangir Hasan, Chen Huang, Quan Leng, Jiayao Li, Alexander Lin, Zhuotao Liu, Ahmed Mansy, Bill Martinusen, Nikil Mehta, Jeffrey C. Mogul, Andrew Narver, Anshul Nigham, Melanie Obenberger, Sean Smith, Kurt Steinkraus, Sheng Sun, Edward Thiele, Amin Vahdat:
Change Management in Physical Network Lifecycle Automation. USENIX ATC 2023: 635-653 - [i8]Alexander Lin, Bahareh Tolooshams, Yves F. Atchadé, Demba E. Ba:
Probabilistic Unrolling: Scalable, Inverse-Free Maximum Likelihood Estimation for Latent Gaussian Models. CoRR abs/2306.03249 (2023) - [i7]Alexander Lin, Lucas Monteiro Paes, Sree Harsha Tanneru, Suraj Srinivas, Himabindu Lakkaraju:
Word-Level Explanations for Analyzing Bias in Text-to-Image Models. CoRR abs/2306.05500 (2023) - [i6]Alexander Lin, Demba E. Ba:
An Efficient Algorithm for Clustered Multi-Task Compressive Sensing. CoRR abs/2310.00420 (2023) - 2022
- [j3]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]Alexander Lin, Alex Lu:
Incorporating knowledge of plates in batch normalization improves generalization of deep learning for microscopy images. MLCB 2022: 74-93 - [i5]Alexander Lin, Andrew H. Song, Berkin Bilgic, Demba E. Ba:
High-Dimensional Sparse Bayesian Learning without Covariance Matrices. CoRR abs/2202.12808 (2022) - 2021
- [j2]Xinping Hu, Yang Miang Goh, Alexander Lin:
Educational impact of an Augmented Reality (AR) application for teaching structural systems to non-engineering students. Adv. Eng. Informatics 50: 101436 (2021) - [c5]Derek Chen, Howard Chen, Yi Yang, Alexander Lin, Zhou Yu:
Action-Based Conversations Dataset: A Corpus for Building More In-Depth Task-Oriented Dialogue Systems. NAACL-HLT 2021: 3002-3017 - [i4]Alexander Lin, Andrew H. Song, Berkin Bilgic, Demba E. Ba:
Covariance-Free Sparse Bayesian Learning. CoRR abs/2105.10439 (2021) - [i3]Alexander Lin, Andrew H. Song, Demba E. Ba:
Mixture Model Auto-Encoders: Deep Clustering through Dictionary Learning. CoRR abs/2110.04683 (2021) - 2020
- [c4]Alexander Lin, Jeremy Wohlwend, Howard Chen, Tao Lei:
Autoregressive Knowledge Distillation through Imitation Learning. EMNLP (1) 2020: 6121-6133 - [i2]Alexander Lin, Jeremy Wohlwend, Howard Chen, Tao Lei:
Autoregressive Knowledge Distillation through Imitation Learning. CoRR abs/2009.07253 (2020)
2010 – 2019
- 2019
- [c3]Alexander Lin, Yingzhuo Zhang, Jeremy Heng, Stephen A. Allsop, Kay M. Tye, Pierre E. Jacob, Demba E. Ba:
Clustering Time Series with Nonlinear Dynamics: A Bayesian Non-Parametric and Particle-Based Approach. AISTATS 2019: 2476-2484 - [c2]Alexander Lin, Amil Merchant, Suproteem K. Sarkar, Alexander D'Amour:
Universal Causal Evaluation Engine: An API for empirically evaluating causal inference models. CD@KDD 2019: 50-58 - 2018
- [i1]Alexander Lin, Yingzhuo Zhang, Jeremy Heng, Stephen A. Allsop, Kay M. Tye, Pierre E. Jacob, Demba E. Ba:
Clustering Time Series with Nonlinear Dynamics: A Bayesian Non-Parametric and Particle-Based Approach. CoRR abs/1810.09920 (2018) - 2011
- [c1]Daniel Cocuzzo, Alexander Lin, Saadallah Ramadan, Carolyn Mountford, Nirmal Keshava:
Algorithms for characterizing brain metabolites in two-dimensional in vivo MR correlation spectroscopy. EMBC 2011: 4929-4934 - 2010
- [j1]Peter Stanwell, Philip Siddall, Nirmal Keshava, Daniel Cocuzzo, Saadallah Ramadan, Alexander Lin, David Herbert, Ashley Craig, Yvonne Tran, James W. Middleton, Shiva Gautam, Michael Cousins, Carolyn Mountford:
Neuro magnetic resonance spectroscopy using wavelet decomposition and statistical testing identifies biochemical changes in people with spinal cord injury and pain. NeuroImage 53(2): 544-552 (2010)
Coauthor Index
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