| Machine-learning approaches to substance-abuse research: emerging trends and their implications E Barenholtz, ND Fitzgerald, WE Hahn Current opinion in psychiatry 33 (4), 334-342, 2020 | 61 | 2020 |
| Predicting binding from screening assays with transformer network embeddings P Morris, R St. Clair, WE Hahn, E Barenholtz Journal of Chemical Information and Modeling 60 (9), 4191-4199, 2020 | 34 | 2020 |
| Online surveillance of novel psychoactive substances (NPS): monitoring Reddit discussions as a predictor of increased NPS-related exposures E Barenholtz, AJ Krotulski, P Morris, ND Fitzgerald, A Le, DM Papsun, ... International Journal of Drug Policy 98, 103393, 2021 | 32 | 2021 |
| Predicting nepse index price using deep learning models NR Pokhrel, KR Dahal, R Rimal, HN Bhandari, RKC Khatri, B Rimal, ... Machine Learning with Applications 9, 100385, 2022 | 31 | 2022 |
| Deep learning human actions from video via sparse filtering and locally competitive algorithms WE Hahn, S Lewkowitz, DC Lacombe, E Barenholtz Multimedia Tools and Applications 74, 10097-10110, 2015 | 18 | 2015 |
| Using conditional generative adversarial networks to reduce the effects of latency in robotic telesurgery N Sachdeva, M Klopukh, RS Clair, WE Hahn Journal of Robotic Surgery 15, 635-641, 2021 | 16 | 2021 |
| A systematic comparison of deep learning architectures in an autonomous vehicle M Teti, WE Hahn, S Martin, C Teti, E Barenholtz arXiv preprint arXiv:1803.09386, 2018 | 7 | 2018 |
| Gender perception from gait: A comparison between biological, biomimetic and non-biomimetic learning paradigms V Sarangi, A Pelah, WE Hahn, E Barenholtz Frontiers in human neuroscience 14, 320, 2020 | 6 | 2020 |
| Convolutional neural networks for predicting molecular binding affinity to HIV-1 proteins P Morris, Y DaSilva, E Clark, WE Hahn, E Barenholtz Proceedings of the 2018 ACM International Conference on Bioinformatics …, 2018 | 5 | 2018 |
| A controlled investigation of behaviorally-cloned deep neural network behaviors in an autonomous steering task M Teti, WE Hahn, S Martin, C Teti, E Barenholtz Robotics and Autonomous Systems 142, 103780, 2021 | 3 | 2021 |
| Predicting residues involved in anti-DNA autoantibodies with limited neural networks R St. Clair, M Teti, M Pavlovic, W Hahn, E Barenholtz Medical & Biological Engineering & Computing 60 (5), 1279-1293, 2022 | 2 | 2022 |
| The Role of Bio-Inspired Modularity in General Learning RA StClair, W Edward Hahn, E Barenholtz Artificial General Intelligence: 14th International Conference, AGI 2021 …, 2022 | 1 | 2022 |
| Neural and neuromimetic perception: a comparative study of gender classification from human gait V Sarangi, A Pelah, W Hahn, E Barenholtz Journal of Perceptual Imaging, 2020 | 1 | 2020 |
| Advances in deep learning and their applied utility toward chemical informatics & drug discovery E Clark, W Hahn, R St Clair, P Morris, M Teti ABSTRACTS OF PAPERS OF THE AMERICAN CHEMICAL SOCIETY 257, 2019 | 1 | 2019 |
| Compressed Topological Data Analysis B Rimal, W Hahn 2020 Fall Southeastern Virtual Sectional Meeting, 2020 | | 2020 |
| Virtual high-throughput screening: A combined deep-learning approach P Morris, R St Clair, M Teti, E Clark, W Hahn ABSTRACTS OF PAPERS OF THE AMERICAN CHEMICAL SOCIETY 257, 2019 | | 2019 |
| Self-Organizing Map Methodology for Sorting Differential Expression Data of MMP-9 Inhibition R St. Clair, M Teti, A Knapinska, G Fields, W Hahn, E Barenholtz bioRxiv, 586628, 2019 | | 2019 |
| Target Binding and Sequence Prediction With LSTMs M Teti, RS Clair, M Pavlovic, E Barenholtz, W Hahn bioRxiv, 504415, 2018 | | 2018 |
| Saliency Map Classification Using Capsule-based CNNs M Kleiman, W Hahn, E Barenholtz Journal of Vision 18 (10), 1209-1209, 2018 | | 2018 |
| Sparse coding and compressed sensing: Locally competitive algorithms and random projections WE Hahn Florida Atlantic University, 2016 | | 2016 |
