| Deep molecular dreaming: inverse machine learning for de-novo molecular design and interpretability with surjective representations C Shen, M Krenn, S Eppel, A Aspuru-Guzik Machine Learning: Science and Technology 2 (3), 03LT02, 2021 | 63 | 2021 |
| Computer vision for recognition of materials and vessels in chemistry lab settings and the vector-labpics data set S Eppel, H Xu, M Bismuth, A Aspuru-Guzik ACS central science 6 (10), 1743-1752, 2020 | 60 | 2020 |
| Forecasting the future of artificial intelligence with machine learning-based link prediction in an exponentially growing knowledge network M Krenn, L Buffoni, B Coutinho, S Eppel, JG Foster, A Gritsevskiy, H Lee, ... Nature Machine Intelligence 5 (11), 1326-1335, 2023 | 52 | 2023 |
| Seeing glass: joint point cloud and depth completion for transparent objects H Xu, YR Wang, S Eppel, A Aspuru-Guzik, F Shkurti, A Garg arXiv preprint arXiv:2110.00087, 2021 | 52 | 2021 |
| Computer vision-based recognition of liquid surfaces and phase boundaries in transparent vessels, with emphasis on chemistry applications S Eppel, T Kachman arXiv preprint arXiv:1404.7174v6, 0 | 43* | |
| Setting an attention region for convolutional neural networks using region selective features, for recognition of materials within glass vessels S Eppel arXiv preprint arXiv:1708.08711, 2017 | 42 | 2017 |
| Statistical survey of hydrogen-bond motifs in crystallographic special symmetry positions, and the influence of chirality of molecules in the crystal on the formation of … S Eppel, J Bernstein Structural Science 64 (1), 50-56, 2008 | 40 | 2008 |
| Amide-templated iodoplumbates: extending lead-iodide based hybrid semiconductors S Eppel, N Fridman, G Frey Crystal Growth & Design 15 (9), 4363-4371, 2015 | 36 | 2015 |
| Statistics-based design of multicomponent molecular crystals with the three-center hydrogen bond S Eppel, J Bernstein Crystal Growth and Design 9 (4), 1683-1691, 2009 | 33 | 2009 |
| Tracing liquid level and material boundaries in transparent vessels using the graph cut computer vision approach S Eppel arXiv preprint arXiv:1602.00177, 2016 | 23 | 2016 |
| Mvtrans: Multi-view perception of transparent objects YR Wang, Y Zhao, H Xu, S Eppel, A Aspuru-Guzik, F Shkurti, A Garg 2023 IEEE International Conference on Robotics and Automation (ICRA), 3771-3778, 2023 | 20 | 2023 |
| Classifying a specific image region using convolutional nets with an ROI mask as input S Eppel arXiv preprint arXiv:1812.00291, 2018 | 18 | 2018 |
| Predicting the Future of AI with AI: High-quality link prediction in an exponentially growing knowledge network M Krenn, L Buffoni, B Coutinho, S Eppel, JG Foster, A Gritsevskiy, H Lee, ... arXiv preprint arXiv:2210.00881, 2022 | 16 | 2022 |
| Predicting 3D shapes, masks, and properties of materials inside transparent containers, using the TransProteus CGI dataset S Eppel, H Xu, YR Wang, A Aspuru-Guzik Digital Discovery 1 (1), 45-60, 2022 | 14 | 2022 |
| One-pot esterification-click (CuAAC) and esterification–acetylene coupling (Glaser/Eglinton) for functionalization of Wang polystyrene resin S Eppel, M Portnoy Tetrahedron Letters 54 (37), 5056-5060, 2013 | 14 | 2013 |
| Computer vision for liquid samples in hospitals and medical labs using hierarchical image segmentation and relations prediction S Eppel, H Xu, A Aspuru-Guzik arXiv preprint arXiv:2105.01456, 2021 | 13 | 2021 |
| Hierarchical semantic segmentation using modular convolutional neural networks S Eppel arXiv preprint arXiv:1710.05126, 2017 | 11 | 2017 |
| One-shot recognition of any material anywhere using contrastive learning with physics-based rendering MS Drehwald, S Eppel, J Li, H Hao, A Aspuru-Guzik Proceedings of the IEEE/CVF International Conference on Computer Vision …, 2023 | 10 | 2023 |
| Tracing the boundaries of materials in transparent vessels using computer vision S Eppel arXiv preprint arXiv:1501.04691, 2015 | 9 | 2015 |
| Class-independent sequential full image segmentation, using a convolutional net that finds a segment within an attention region, given a pointer pixel within this segment S Eppel arXiv preprint arXiv:1902.07810, 2019 | 8 | 2019 |
