Kyle Swanson
Cited by
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A deep learning approach to antibiotic discovery
JM Stokes, K Yang, K Swanson, W Jin, A Cubillos-Ruiz, NM Donghia, ...
Cell 180 (4), 688-702. e13, 2020
Analyzing learned molecular representations for property prediction
K Yang, K Swanson, W Jin, C Coley, P Eiden, H Gao, A Guzman-Perez, ...
Journal of chemical information and modeling 59 (8), 3370-3388, 2019
Mammographic breast density assessment using deep learning: clinical implementation
CD Lehman, A Yala, T Schuster, B Dontchos, M Bahl, K Swanson, ...
Radiology 290 (1), 52-58, 2019
Uncertainty quantification using neural networks for molecular property prediction
L Hirschfeld, K Swanson, K Yang, R Barzilay, CW Coley
Journal of Chemical Information and Modeling 60 (8), 3770-3780, 2020
Deep learning for automated classification and characterization of amorphous materials
K Swanson, S Trivedi, J Lequieu, K Swanson, R Kondor
Soft matter 16 (2), 435-446, 2020
Rationalizing text matching: Learning sparse alignments via optimal transport
K Swanson, L Yu, T Lei
arXiv preprint arXiv:2005.13111, 2020
Message passing neural networks for molecular property prediction
K Swanson
Massachusetts Institute of Technology, 2019
Improving molecular design by stochastic iterative target augmentation
K Yang, W Jin, K Swanson, R Barzilay, T Jaakkola
International Conference on Machine Learning, 10716-10726, 2020
Building a Production Model for Retrieval-Based Chatbots
K Swanson, L Yu, C Fox, J Wohlwend, T Lei
1st Workshop on NLP for Conversational AI at the 57th Annual Meeting of the …, 2019
Minimal brain adaptation for representational prioritization in non-native parsing: Evidence from a time-frequency analysis of recursion in Wh-dependencies in French
L Dekydtspotter, AK Miller, M Iverson, Y Xiong, K Swanson, C Gilbert
Journal of Neurolinguistics 59, 101002, 2021
Neurocognition and Non-Native Grammatical Processing: The View from Anaphora in Successive-Cyclic Wh-Movement in L1-Mandarin/L2-English
KR Swanson
Indiana University, 2021
Iterative Target Augmentation for Effective Conditional Generation
K Yang, W Jin, K Swanson, R Barzilay, T Jaakkola
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