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ORCIDJohn V. Guttag
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- affiliation: MIT, Cambridge, USA
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2020 – today
- 2024
[c104]Jose Javier Gonzalez Ortiz, John V. Guttag, Adrian V. Dalca:
Magnitude Invariant Parametrizations Improve Hypernetwork Learning. ICLR 2024- [i48]Marianne Rakic, Hallee E. Wong, Jose Javier Gonzalez Ortiz, Beth Cimini, John V. Guttag, Adrian V. Dalca:
Tyche: Stochastic In-Context Learning for Medical Image Segmentation. CoRR abs/2401.13650 (2024) - 2023
- [c103]Harini Suresh
, Divya Shanmugam, Tiffany Chen, Annie G. Bryan, Alexander D'Amour, John V. Guttag, Arvind Satyanarayan:
Kaleidoscope: Semantically-grounded, context-specific ML model evaluation. CHI 2023: 775:1-775:13 - [c102]Katie Matton, Robert Lewis, John V. Guttag, Rosalind W. Picard:
Contrastive Learning of Electrodermal Activity Representations for Stress Detection. CHIL 2023: 410-426 - [c101]Angie W. Boggust, Harini Suresh, Hendrik Strobelt, John V. Guttag, Arvind Satyanarayan:
Saliency Cards: A Framework to Characterize and Compare Saliency Methods. FAccT 2023: 285-296 - [c100]Victor Ion Butoi, Jose Javier Gonzalez Ortiz, Tianyu Ma, Mert R. Sabuncu, John V. Guttag, Adrian V. Dalca:
UniverSeg: Universal Medical Image Segmentation. ICCV 2023: 21381-21394 - [c99]Aniruddh Raghu, Payal Chandak, Ridwan Alam, John V. Guttag, Collin M. Stultz:
Sequential Multi-Dimensional Self-Supervised Learning for Clinical Time Series. ICML 2023: 28531-28548 - [c98]Rajiv Movva, Divya Shanmugam, Kaihua Hou, Priya Pathak, John V. Guttag, Nikhil Garg, Emma Pierson:
Coarse race data conceals disparities in clinical risk score performance. MLHC 2023: 443-472 - [c97]Jose Javier Gonzalez Ortiz, John V. Guttag, Adrian V. Dalca:
Scale-Space Hypernetworks for Efficient Biomedical Image Analysis. NeurIPS 2023 - [i47]Jose Javier Gonzalez Ortiz, John V. Guttag, Adrian V. Dalca:
Amortized Learning of Dynamic Feature Scaling for Image Segmentation. CoRR abs/2304.05448 (2023) - [i46]Victor Ion Butoi, Jose Javier Gonzalez Ortiz, Tianyu Ma, Mert R. Sabuncu, John V. Guttag, Adrian V. Dalca:
UniverSeg: Universal Medical Image Segmentation. CoRR abs/2304.06131 (2023) - [i45]Jose Javier Gonzalez Ortiz, John V. Guttag, Adrian V. Dalca:
Non-Proportional Parametrizations for Stable Hypernetwork Learning. CoRR abs/2304.07645 (2023) - [i44]Rajiv Movva, Divya Shanmugam, Kaihua Hou, Priya Pathak, John V. Guttag, Nikhil Garg, Emma Pierson:
Coarse race data conceals disparities in clinical risk score performance. CoRR abs/2304.09270 (2023) - [i43]Emily Mu, John V. Guttag, Maggie Makar:
Multi-Similarity Contrastive Learning. CoRR abs/2307.02712 (2023) - [i42]Aniruddh Raghu, Payal Chandak, Ridwan Alam, John V. Guttag, Collin M. Stultz:
Sequential Multi-Dimensional Self-Supervised Learning for Clinical Time Series. CoRR abs/2307.10923 (2023) - [i41]Emily Mu, Kathleen M. Lewis, Adrian V. Dalca, John V. Guttag:
Generating Image-Specific Text Improves Fine-grained Image Classification. CoRR abs/2307.11315 (2023) - [i40]Hallee E. Wong, Marianne Rakic, John V. Guttag, Adrian V. Dalca:
ScribblePrompt: Fast and Flexible Interactive Segmentation for Any Medical Image. CoRR abs/2312.07381 (2023) - 2022
- [c96]Aniruddh Raghu, Divya Shanmugam, Eugene Pomerantsev, John V. Guttag, Collin M. Stultz:
Data Augmentation for Electrocardiograms. CHIL 2022: 282-310 - [c95]Harini Suresh, Kathleen M. Lewis, John V. Guttag, Arvind Satyanarayan:
Intuitively Assessing ML Model Reliability through Example-Based Explanations and Editing Model Inputs. IUI 2022: 767-781 - [i39]Andrew Hoopes, Malte Hoffmann, Douglas N. Greve, Bruce Fischl, John V. Guttag, Adrian V. Dalca:
Learning the Effect of Registration Hyperparameters with HyperMorph. CoRR abs/2203.16680 (2022) - [i38]Aniruddh Raghu, Divya Shanmugam, Eugene Pomerantsev, John V. Guttag, Collin M. Stultz:
Data Augmentation for Electrocardiograms. CoRR abs/2204.04360 (2022) - [i37]Angie W. Boggust, Harini Suresh, Hendrik Strobelt, John V. Guttag, Arvind Satyanarayan:
Beyond Faithfulness: A Framework to Characterize and Compare Saliency Methods. CoRR abs/2206.02958 (2022) - [i36]Helen Lu, Divya Shanmugam, Harini Suresh, John V. Guttag:
Improved Text Classification via Test-Time Augmentation. CoRR abs/2206.13607 (2022) - [i35]Divya Shanmugam, Katie Lewis, Jose Javier Gonzalez Ortiz, Agnieszka Kurant, John V. Guttag:
At the Intersection of Deep Learning and Conceptual Art: The End of Signature. CoRR abs/2207.04312 (2022) - [i34]Kathleen M. Lewis, John V. Guttag:
SizeGAN: Improving Size Representation in Clothing Catalogs. CoRR abs/2211.02892 (2022) - 2021
- [j40]Susanne Gaube, Harini Suresh, Martina Raue, Alexander Merritt, Seth J. Berkowitz, Eva Lermer, Joseph F. Coughlin, John V. Guttag, Errol Colak, Marzyeh Ghassemi:
Do as AI say: susceptibility in deployment of clinical decision-aids. npj Digit. Medicine 4 (2021) - [c94]Aniruddh Raghu, John V. Guttag, Katherine Young, Eugene Pomerantsev, Adrian V. Dalca, Collin M. Stultz:
Learning to predict with supporting evidence: applications to clinical risk prediction. CHIL 2021: 95-104 - [c93]Harini Suresh, John V. Guttag:
A Framework for Understanding Sources of Harm throughout the Machine Learning Life Cycle. EAAMO 2021: 17:1-17:9 - [c92]Divya Shanmugam, Davis W. Blalock, Guha Balakrishnan, John V. Guttag:
Better Aggregation in Test-Time Augmentation. ICCV 2021: 1194-1203 - [c91]Davis W. Blalock, John V. Guttag:
Multiplying Matrices Without Multiplying. ICML 2021: 992-1004 - [c90]Maggie Makar, Lauren West, David Hooper, Eric Horvitz, Erica Shenoy, John V. Guttag:
Exploiting structured data for learning contagious diseases under incomplete testing. ICML 2021: 7348-7357 - [c89]Andrew Hoopes, Malte Hoffmann, Bruce Fischl, John V. Guttag, Adrian V. Dalca:
HyperMorph: Amortized Hyperparameter Learning for Image Registration. IPMI 2021: 3-17 - [i33]Andrew Hoopes, Malte Hoffmann, Bruce Fischl, John V. Guttag, Adrian V. Dalca:
HyperMorph: Amortized Hyperparameter Learning for Image Registration. CoRR abs/2101.01035 (2021) - [i32]Harini Suresh, Kathleen M. Lewis, John V. Guttag, Arvind Satyanarayan:
Intuitively Assessing ML Model Reliability through Example-Based Explanations and Editing Model Inputs. CoRR abs/2102.08540 (2021) - [i31]Aniruddh Raghu, John V. Guttag, Katherine Young, Eugene Pomerantsev, Adrian V. Dalca, Collin M. Stultz:
Learning to Predict with Supporting Evidence: Applications to Clinical Risk Prediction. CoRR abs/2103.02768 (2021) - [i30]Davis W. Blalock, John V. Guttag:
Multiplying Matrices Without Multiplying. CoRR abs/2106.10860 (2021) - 2020
- [c88]Kathleen M. Lewis, Natalia S. Rost, John V. Guttag, Adrian V. Dalca:
Fast learning-based registration of sparse 3D clinical images. CHIL 2020: 90-98 - [c87]Amy Zhao, Guha Balakrishnan, Kathleen M. Lewis, Frédo Durand, John V. Guttag, Adrian V. Dalca:
Painting Many Pasts: Synthesizing Time Lapse Videos of Paintings. CVPR 2020: 8432-8442 - [c86]Maggie Makar, Fredrik D. Johansson, John V. Guttag, David A. Sontag:
Estimation of Bounds on Potential Outcomes For Decision Making. ICML 2020: 6661-6671 - [c85]Davis W. Blalock, Jose Javier Gonzalez Ortiz, Jonathan Frankle, John V. Guttag:
What is the State of Neural Network Pruning? MLSys 2020 - [i29]Amy Zhao, Guha Balakrishnan, Kathleen M. Lewis, Frédo Durand, John V. Guttag, Adrian V. Dalca:
Painting Many Pasts: Synthesizing Time Lapse Videos of Paintings. CoRR abs/2001.01026 (2020) - [i28]Davis W. Blalock, Jose Javier Gonzalez Ortiz, Jonathan Frankle, John V. Guttag:
What is the State of Neural Network Pruning? CoRR abs/2003.03033 (2020) - [i27]Marianne Rakic, John V. Guttag, Adrian V. Dalca:
Anatomical Predictions using Subject-Specific Medical Data. CoRR abs/2006.00090 (2020) - [i26]Roshni Sahoo, Divya Shanmugam, John V. Guttag:
Unsupervised Domain Adaptation in the Absence of Source Data. CoRR abs/2007.10233 (2020) - [i25]Divya Shanmugam, Davis W. Blalock, Guha Balakrishnan, John V. Guttag:
When and Why Test-Time Augmentation Works. CoRR abs/2011.11156 (2020)
2010 – 2019
- 2019
- [j39]Adrian V. Dalca, Guha Balakrishnan, John V. Guttag, Mert R. Sabuncu:
Unsupervised learning of probabilistic diffeomorphic registration for images and surfaces. Medical Image Anal. 57: 226-236 (2019) - [j38]Guha Balakrishnan, Amy Zhao, Mert R. Sabuncu, John V. Guttag, Adrian V. Dalca:
VoxelMorph: A Learning Framework for Deformable Medical Image Registration. IEEE Trans. Medical Imaging 38(8): 1788-1800 (2019) - [c84]Amy Zhao, Guha Balakrishnan, Frédo Durand, John V. Guttag, Adrian V. Dalca:
Data Augmentation Using Learned Transformations for One-Shot Medical Image Segmentation. CVPR 2019: 8543-8553 - [c83]Guha Balakrishnan, Adrian V. Dalca, Amy Zhao, John V. Guttag, Frédo Durand, William T. Freeman:
Visual Deprojection: Probabilistic Recovery of Collapsed Dimensions. ICCV 2019: 171-180 - [c82]Divya Shanmugam, Davis W. Blalock, John V. Guttag:
Multiple Instance Learning for ECG Risk Stratification. MLHC 2019: 124-139 - [c81]Jose Javier Gonzalez Ortiz, Daryush D. Mehta, Jarrad H. Van Stan, Robert E. Hillman, John V. Guttag, Marzyeh Ghassemi:
Learning from Few Subjects with Large Amounts of Voice Monitoring Data. MLHC 2019: 704-720 - [c80]Adrian V. Dalca, Marianne Rakic, John V. Guttag, Mert R. Sabuncu:
Learning Conditional Deformable Templates with Convolutional Networks. NeurIPS 2019: 804-816 - [i24]Harini Suresh, John V. Guttag:
A Framework for Understanding Unintended Consequences of Machine Learning. CoRR abs/1901.10002 (2019) - [i23]Amy Zhao, Guha Balakrishnan, Frédo Durand, John V. Guttag, Adrian V. Dalca:
Data augmentation using learned transforms for one-shot medical image segmentation. CoRR abs/1902.09383 (2019) - [i22]Adrian V. Dalca, John V. Guttag, Mert R. Sabuncu:
Anatomical Priors in Convolutional Networks for Unsupervised Biomedical Segmentation. CoRR abs/1903.03148 (2019) - [i21]Adrian V. Dalca, John V. Guttag, Mert R. Sabuncu:
Unsupervised Data Imputation via Variational Inference of Deep Subspaces. CoRR abs/1903.03503 (2019) - [i20]Adrian V. Dalca, Guha Balakrishnan, John V. Guttag, Mert R. Sabuncu:
Unsupervised Learning of Probabilistic Diffeomorphic Registration for Images and Surfaces. CoRR abs/1903.03545 (2019) - [i19]Adrian V. Dalca, Marianne Rakic, John V. Guttag, Mert R. Sabuncu:
Learning Conditional Deformable Templates with Convolutional Networks. CoRR abs/1908.02738 (2019) - [i18]Guha Balakrishnan, Adrian V. Dalca, Amy Zhao, John V. Guttag, Frédo Durand, William T. Freeman:
Visual Deprojection: Probabilistic Recovery of Collapsed Dimensions. CoRR abs/1909.00475 (2019) - [i17]Maggie Makar, Fredrik D. Johansson, John V. Guttag, David A. Sontag:
Estimation of Utility-Maximizing Bounds on Potential Outcomes. CoRR abs/1910.04817 (2019) - [i16]Ava P. Soleimany, Harini Suresh, Jose Javier Gonzalez Ortiz, Divya Shanmugam, Nil Gural, John V. Guttag, Sangeeta N. Bhatia:
Image segmentation of liver stage malaria infection with spatial uncertainty sampling. CoRR abs/1912.00262 (2019) - 2018
- [j37]Davis W. Blalock, Samuel Madden, John V. Guttag:
Sprintz: Time Series Compression for the Internet of Things. Proc. ACM Interact. Mob. Wearable Ubiquitous Technol. 2(3): 93:1-93:23 (2018) - [c79]Maggie Makar, John V. Guttag, Jenna Wiens:
Learning the Probability of Activation in the Presence of Latent Spreaders. AAAI 2018: 134-141 - [c78]Guha Balakrishnan, Amy Zhao, Adrian V. Dalca, Frédo Durand, John V. Guttag:
Synthesizing Images of Humans in Unseen Poses. CVPR 2018: 8340-8348 - [c77]Guha Balakrishnan, Amy Zhao, Mert R. Sabuncu, John V. Guttag, Adrian V. Dalca:
An Unsupervised Learning Model for Deformable Medical Image Registration. CVPR 2018: 9252-9260 - [c76]Adrian V. Dalca, John V. Guttag, Mert R. Sabuncu:
Anatomical Priors in Convolutional Networks for Unsupervised Biomedical Segmentation. CVPR 2018: 9290-9299 - [c75]Harini Suresh, Jen J. Gong, John V. Guttag:
Learning Tasks for Multitask Learning: Heterogenous Patient Populations in the ICU. KDD 2018: 802-810 - [c74]Adrian V. Dalca, Guha Balakrishnan, John V. Guttag, Mert R. Sabuncu:
Unsupervised Learning for Fast Probabilistic Diffeomorphic Registration. MICCAI (1) 2018: 729-738 - [c73]Jen J. Gong, John V. Guttag:
Learning to Summarize Electronic Health Records Using Cross-Modality Correspondences. MLHC 2018: 551-570 - [i15]Guha Balakrishnan, Amy Zhao, Mert R. Sabuncu, John V. Guttag, Adrian V. Dalca:
An Unsupervised Learning Model for Deformable Medical Image Registration. CoRR abs/1802.02604 (2018) - [i14]Guha Balakrishnan, Amy Zhao, Adrian V. Dalca, Frédo Durand, John V. Guttag:
Synthesizing Images of Humans in Unseen Poses. CoRR abs/1804.07739 (2018) - [i13]Adrian V. Dalca, Guha Balakrishnan, John V. Guttag, Mert R. Sabuncu:
Unsupervised Learning for Fast Probabilistic Diffeomorphic Registration. CoRR abs/1805.04605 (2018) - [i12]Dina Levy-Lambert, Jen J. Gong, Tristan Naumann, Tom J. Pollard, John V. Guttag:
Visualizing Patient Timelines in the Intensive Care Unit. CoRR abs/1806.00397 (2018) - [i11]Harini Suresh, Jen J. Gong, John V. Guttag:
Learning Tasks for Multitask Learning: Heterogenous Patient Populations in the ICU. CoRR abs/1806.02878 (2018) - [i10]Davis W. Blalock, Samuel Madden, John V. Guttag:
Sprintz: Time Series Compression for the Internet of Things. CoRR abs/1808.02515 (2018) - [i9]Guha Balakrishnan, Amy Zhao, Mert R. Sabuncu, John V. Guttag, Adrian V. Dalca:
VoxelMorph: A Learning Framework for Deformable Medical Image Registration. CoRR abs/1809.05231 (2018) - [i8]Divya Shanmugam, Davis W. Blalock, Jen J. Gong, John V. Guttag:
Multiple Instance Learning for ECG Risk Stratification. CoRR abs/1812.00475 (2018) - [i7]Kathleen M. Lewis, Guha Balakrishnan, Natalia S. Rost, John V. Guttag, Adrian V. Dalca:
Fast Learning-based Registration of Sparse Clinical Images. CoRR abs/1812.06932 (2018) - 2017
- [j36]Neal Wadhwa, Hao-Yu Wu, Abe Davis, Michael Rubinstein, Eugene Shih, Gautham J. Mysore, Justin G. Chen, Oral Büyüköztürk, John V. Guttag, William T. Freeman, Frédo Durand:
Eulerian video magnification and analysis. Commun. ACM 60(1): 87-95 (2017) - [c72]Davis W. Blalock, John V. Guttag:
Bolt: Accelerated Data Mining with Fast Vector Compression. KDD 2017: 727-735 - [c71]Jen J. Gong, Tristan Naumann, Peter Szolovits, John V. Guttag:
Predicting Clinical Outcomes Across Changing Electronic Health Record Systems. KDD 2017: 1497-1505 - [c70]Ronnachai Jaroensri, Amy Zhao, Guha Balakrishnan, Derek Lo, Jeremy D. Schmahmann, Frédo Durand, John V. Guttag:
A Video-Based Method for Automatically Rating Ataxia. MLHC 2017: 204-216 - [i6]Davis W. Blalock, John V. Guttag:
Bolt: Accelerated Data Mining with Fast Vector Compression. CoRR abs/1706.10283 (2017) - [i5]Maggie Makar, John V. Guttag, Jenna Wiens:
Learning the Probability of Activation in the Presence of Latent Spreaders. CoRR abs/1712.00643 (2017) - 2016
- [j35]Jenna Wiens, John V. Guttag, Eric Horvitz:
Patient Risk Stratification with Time-Varying Parameters: A Multitask Learning Approach. J. Mach. Learn. Res. 17: 79:1-79:23 (2016) - [j34]Joel Brooks, Matthew Kerr, John V. Guttag:
Using machine learning to draw inferences from pass location data in soccer. Stat. Anal. Data Min. 9(5): 338-349 (2016) - [c69]Davis W. Blalock, John V. Guttag:
EXTRACT: Strong Examples from Weakly-Labeled Sensor Data. ICDM 2016: 799-804 - [c68]Jen J. Gong, Maryann Gong, Dina Levy-Lambert, Jordan R. Green, Tiffany P. Hogan, John V. Guttag:
Towards an Automated Screening Tool for Developmental Speech and Language Impairments. INTERSPEECH 2016: 112-116 - [c67]Joel Brooks, Matthew Kerr, John V. Guttag:
Developing a Data-Driven Player Ranking in Soccer Using Predictive Model Weights. KDD 2016: 49-55 - [c66]Yun Liu, Collin M. Stultz, John V. Guttag, Kun-Ta Chuang, Fu-Wen Liang, Huey-Jen Su:
Transferring Knowledge from Text to Predict Disease Onset. MLHC 2016: 150-163 - [c65]Marzyeh Ghassemi, Zeeshan Syed, Daryush D. Mehta, Jarrad H. Van Stan, Robert E. Hillman, John V. Guttag:
Uncovering Voice Misuse Using Symbolic Mismatch. MLHC 2016: 239-252 - [i4]Yun Liu, Kun-Ta Chuang, Fu-Wen Liang, Huey-Jen Su, Collin M. Stultz, John V. Guttag:
Transferring Knowledge from Text to Predict Disease Onset. CoRR abs/1608.02071 (2016) - [i3]Marzyeh Ghassemi, Zeeshan Syed, Daryush D. Mehta, Jarrad H. Van Stan, Robert E. Hillman, John V. Guttag:
Uncovering Voice Misuse Using Symbolic Mismatch. CoRR abs/1608.02301 (2016) - [i2]Davis W. Blalock, John V. Guttag:
EXTRACT: Strong Examples from Weakly-Labeled Sensor Data. CoRR abs/1609.09196 (2016) - [i1]Ronnachai Jaroensri, Amy Zhao, Guha Balakrishnan, Derek Lo, Jeremy D. Schmahmann, John V. Guttag, Frédo Durand:
A Video-Based Method for Objectively Rating Ataxia. CoRR abs/1612.04007 (2016) - 2015
- [j33]Anima Singh, Girish N. Nadkarni, Omri Gottesman, Stephen B. Ellis, Erwin P. Bottinger, John V. Guttag:
Incorporating temporal EHR data in predictive models for risk stratification of renal function deterioration. J. Biomed. Informatics 53: 220-228 (2015) - [j32]Marzyeh Ghassemi, Jarrad H. Van Stan, Daryush D. Mehta, Matías Zanartu, Harold A. Cheyne II, Robert E. Hillman, John V. Guttag:
Corrections to "Learning to Detect Vocal Hyperfunction From Ambulatory Neck-Surface Acceleration Features: Initial Results For Vocal Fold Nodules". IEEE Trans. Biomed. Eng. 62(10): 2544 (2015) - [j31]Guha Balakrishnan, Frédo Durand, John V. Guttag:
Video diff: highlighting differences between similar actions in videos. ACM Trans. Graph. 34(6): 194:1-194:10 (2015) - [c64]Amy Zhao, Frédo Durand, John V. Guttag:
Estimating a Small Signal in the Presence of Large Noise. ICCV Workshops 2015: 671-676 - [c63]Jen J. Gong, Thoralf M. Sundt, James D. Rawn, John V. Guttag:
Instance Weighting for Patient-Specific Risk Stratification Models. KDD 2015: 369-378 - 2014
- [j30]Jenna Wiens, John V. Guttag, Eric Horvitz:
A study in transfer learning: leveraging data from multiple hospitals to enhance hospital-specific predictions. J. Am. Medical Informatics Assoc. 21(4): 699-706 (2014) - [j29]Marzyeh Ghassemi, Jarrad H. Van Stan, Daryush D. Mehta, Matías Zanartu, Harold A. Cheyne II, Robert E. Hillman, John V. Guttag:
Learning to Detect Vocal Hyperfunction From Ambulatory Neck-Surface Acceleration Features: Initial Results for Vocal Fold Nodules. IEEE Trans. Biomed. Eng. 61(6): 1668-1675 (2014) - [c62]Anima Singh, Girish N. Nadkarni, John V. Guttag, Erwin P. Bottinger:
Leveraging hierarchy in medical codes for predictive modeling. BCB 2014: 96-103 - 2013
- [c61]Anima Singh, John V. Guttag:
Collaborative Filtering for Identifying Prescription Omissions in an ICU. HEALTHINF 2013: 58-64 - [c60]Guha Balakrishnan, Frédo Durand, John V. Guttag:
Detecting Pulse from Head Motions in Video. CVPR 2013: 3430-3437 - [c59]Gartheeban Ganeshapillai, John V. Guttag, Andrew Lo:
Learning Connections in Financial Time Series. ICML (2) 2013: 109-117 - [c58]Gartheeban Ganeshapillai, John V. Guttag:
A data-driven method for in-game decision making in MLB: when to pull a starting pitcher. KDD 2013: 973-979 - 2012
- [j28]Gartheeban Ganeshapillai, John V. Guttag:
Real Time Reconstruction of Multi Parameter Physiological Signals. EURASIP J. Adv. Signal Process. 2012: 173 (2012) - [j27]Hao-Yu Wu, Michael Rubinstein, Eugene Shih, John V. Guttag, Frédo Durand, William T. Freeman:
Eulerian video magnification for revealing subtle changes in the world. ACM Trans. Graph. 31(4): 65:1-65:8 (2012) - [c57]Jenna Wiens, John V. Guttag, Eric Horvitz:
Patient Risk Stratification for Hospital-Associated C. diff as a Time-Series Classification Task. NIPS 2012: 476-484 - 2011
- [j26]Zeeshan Syed, John V. Guttag:
Unsupervised Similarity-Based Risk Stratification for Cardiovascular Events Using Long-Term Time-Series Data. J. Mach. Learn. Res. 12: 999-1024 (2011) - [c56]Gartheeban Ganeshapillai, John V. Guttag:
Weighted Time Warping for Temporal Segmentation of Multi-parameter Physiological Signals. BIOSIGNALS 2011: 125-131 - [c55]Anima Singh, John V. Guttag:
A comparison of non-symmetric entropy-based classification trees and support vector machine for cardiovascular risk stratification. EMBC 2011: 79-82 - [c54]Ali H. Shoeb, Alaa Kharbouch, Jacqueline Soegaard, Steven Schachter, John V. Guttag:
An algorithm for detecting seizure termination in scalp EEG. EMBC 2011: 1443-1446 - [c53]Gartheeban Ganeshapillai, Jessica F. Liu, John V. Guttag:
Reconstruction of ECG signals in presence of corruption. EMBC 2011: 3764-3767 - [c52]Jenna Wiens, John V. Guttag:
Patient-specific ventricular beat classification without patient-specific expert knowledge: A transfer learning approach. EMBC 2011: 5876-5879 - 2010
- [j25]Naveen Verma, Ali H. Shoeb, Jose L. Bohorquez, Joel L. Dawson, John V. Guttag, Anantha P. Chandrakasan:
A Micro-Power EEG Acquisition SoC With Integrated Feature Extraction Processor for a Chronic Seizure Detection System. IEEE J. Solid State Circuits 45(4): 804-816 (2010) - [j24]Zeeshan Syed, Collin M. Stultz, Manolis Kellis, Piotr Indyk, John V. Guttag:
Motif discovery in physiological datasets: A methodology for inferring predictive elements. ACM Trans. Knowl. Discov. Data 4(1): 2:1-2:23 (2010) - [c51]Ali H. Shoeb, John V. Guttag:
Application of Machine Learning To Epileptic Seizure Detection. ICML 2010: 975-982 - [c50]Zeeshan Syed, John V. Guttag:
Identifying Patients at Risk of Major Adverse Cardiovascular Events Using Symbolic Mismatch. NIPS 2010: 2262-2270 - [c49]Jenna Wiens, John V. Guttag:
Active Learning Applied to Patient-Adaptive Heartbeat Classification. NIPS 2010: 2442-2450
2000 – 2009
- 2009
- [j23]