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Ben Lengerich
Ben Lengerich
MIT, Broad Institute
Adresă de e-mail confirmată pe mit.edu - Pagina de pornire
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Opportunities and obstacles for deep learning in biology and medicine
T Ching, DS Himmelstein, BK Beaulieu-Jones, AA Kalinin, BT Do, ...
Journal of the Royal Society Interface 15 (141), 2018
19002018
Neural additive models: Interpretable machine learning with neural nets
R Agarwal, L Melnick, N Frosst, X Zhang, B Lengerich, R Caruana, ...
Advances in neural information processing systems 34, 4699-4711, 2021
3552021
Precision lasso: Accounting for correlations and linear dependencies in high-dimensional genomic data
H Wang, BJ Lengerich, B Aragam, EP Xing
Bioinformatics, 2018
1312018
How interpretable and trustworthy are GAMs?
CH Chang, S Tan, B Lengerich, A Goldenberg, R Caruana
Proceedings of the 27th ACM SIGKDD Conference on Knowledge Discovery & Data …, 2021
632021
Retrofitting Distributional Embeddings to Knowledge Graphs with Functional Relations
BJ Lengerich, AL Maas, C Potts
International Conference on Computational Linguistics (COLING) 27, 2423-2436, 2018
372018
Purifying interaction effects with the functional anova: An efficient algorithm for recovering identifiable additive models
B Lengerich, S Tan, CH Chang, G Hooker, R Caruana
International Conference on Artificial Intelligence and Statistics, 2402-2412, 2020
302020
Towards visual explanations for convolutional neural networks via input resampling
BJ Lengerich, S Konam, EP Xing, S Rosenthal, M Veloso
arXiv preprint arXiv:1707.09641, 2017
252017
Ten quick tips for deep learning in biology
BD Lee, A Gitter, CS Greene, S Raschka, F Maguire, AJ Titus, MD Kessler, ...
PLoS computational biology 18 (3), e1009803, 2022
222022
Experimental and computational mutagenesis to investigate the positioning of a general base within an enzyme active site
JP Schwans, P Hanoian, BJ Lengerich, F Sunden, A Gonzalez, Y Tsai, ...
Biochemistry 53 (15), 2541-2555, 2014
222014
Personalized Regression Enables Sample-Specific Pan-Cancer Analysis
BJ Lengerich, B Aragam, EP Xing
Bioinformatics 34 (13), i178-i186, 2018
212018
Dropout as a regularizer of interaction effects
BJ Lengerich, E Xing, R Caruana
International Conference on Artificial Intelligence and Statistics, 7550-7564, 2022
18*2022
Learning sample-specific models with low-rank personalized regression
B Lengerich, B Aragam, EP Xing
Advances in Neural Information Processing Systems 32, 2019
172019
Opportunities and obstacles for deep learning in biology and medicine. JR Soc. Interface 15
T Ching, DS Himmelstein, BK Beaulieu-Jones, AA Kalinin, BT Do, ...
172017
Opportunities and obstacles for deep learning in biology and medicine. JR Soc Interface. 2018; 15 (141): 20170387
T Ching, DS Himmelstein, BK Beaulieu-Jones, AA Kalinin, BT Do, ...
112017
Using Interpretable Machine Learning to Predict Maternal and Fetal Outcomes
TM Bosschieter, Z Xu, H Lan, BJ Lengerich, H Nori, K Sitcov, V Souter, ...
arXiv preprint arXiv:2207.05322, 2022
62022
Automated interpretable discovery of heterogeneous treatment effectiveness: A COVID-19 case study
BJ Lengerich, ME Nunnally, Y Aphinyanaphongs, C Ellington, R Caruana
Journal of biomedical informatics 130, 104086, 2022
52022
LLMs understand glass-box models, discover surprises, and suggest repairs
BJ Lengerich, S Bordt, H Nori, ME Nunnally, Y Aphinyanaphongs, ...
arXiv preprint arXiv:2308.01157, 2023
32023
Death by Round Numbers: Glass-Box Machine Learning Uncovers Biases in Medical Practice
BJ Lengerich, R Caruana, ME Nunnally, M Kellis
medRxiv, 2022.04. 30.22274520, 2022
3*2022
Hybrid subspace learning for high-dimensional data
M Marchetti-Bowick, BJ Lengerich, AP Parikh, EP Xing
arXiv preprint arXiv:1808.01687, 2018
22018
Contextualized machine learning
B Lengerich, CN Ellington, A Rubbi, M Kellis, EP Xing
arXiv preprint arXiv:2310.11340, 2023
12023
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