| Efficient photometric selection of quasars from the Sloan Digital Sky Survey. II.∼ 1, 000, 000 quasars from Data Release 6 GT Richards, AD Myers, AG Gray, RN Riegel, RC Nichol, RJ Brunner, ... The Astrophysical Journal Supplement Series 180 (1), 67, 2008 | 364 | 2008 |
| Quasar classification using color and variability CM Peters, GT Richards, AD Myers, MA Strauss, KB Schmidt, Ž Ivezić, ... The Astrophysical Journal 811 (2), 95, 2015 | 80 | 2015 |
| Bayesian high-redshift quasar classification from optical and mid-IR photometry GT Richards, AD Myers, CM Peters, CM Krawczyk, G Chase, NP Ross, ... The Astrophysical Journal Supplement Series 219 (2), 39, 2015 | 76 | 2015 |
| Logical neural networks R Riegel, A Gray, F Luus, N Khan, N Makondo, IY Akhalwaya, H Qian, ... arXiv preprint arXiv:2006.13155, 2020 | 66 | 2020 |
| Configurable Machine Learning Method Selection and Parameter Optimization System and Method M Gibiansky, R Riegel, Y Yang, P Ram, A Gray US Patent App. 14/883,522, 2016 | 64 | 2016 |
| Leveraging abstract meaning representation for knowledge base question answering P Kapanipathi, I Abdelaziz, S Ravishankar, S Roukos, A Gray, R Astudillo, ... arXiv preprint arXiv:2012.01707, 2020 | 46* | 2020 |
| Massive-scale kernel discriminant analysis: Mining for quasars R Riegel, A Gray, G Richards Proceedings of the 2008 SIAM International Conference on Data Mining, 208-218, 2008 | 11 | 2008 |
| Large-scale kernel discriminant analysis with application to quasar discovery A Gray, R Riegel Proceedings of Computational Statistics, 2006 | 6 | 2006 |
| Logic embeddings for complex query answering F Luus, P Sen, P Kapanipathi, R Riegel, N Makondo, T Lebese, A Gray arXiv preprint arXiv:2103.00418, 2021 | 5 | 2021 |
| A parallel N-body data mining framework GF Boyer, RN Riegel, AG Gray NIPS Workshop on Efficient Machine Learning 1304, 2007 | 5 | 2007 |
| Neuro-symbolic inductive logic programming with logical neural networks P Sen, BWSR de Carvalho, R Riegel, A Gray Proceedings of the AAAI Conference on Artificial Intelligence 36 (8), 8212-8219, 2022 | 4 | 2022 |
| Foundations of reasoning with uncertainty via real-valued logics R Fagin, R Riegel, A Gray arXiv preprint arXiv:2008.02429, 2020 | 3 | 2020 |
| Generalized N-body problems: a Framework for Scalable Computation RN Riegel Georgia Institute of Technology, 2013 | 3 | 2013 |
| Multitree algorithms for large-scale astrostatistics WB March, A Ozakin, D Lee, R Riegel, AG Gray Advances in Machine Learning and Data Mining for Astronomy, 463-483, 2012 | 3 | 2012 |
| Training Logical Neural Networks by Primal–Dual Methods for Neuro-Symbolic Reasoning S Lu, N Khan, IY Akhalwaya, R Riegel, L Horesh, A Gray ICASSP 2021-2021 IEEE International Conference on Acoustics, Speech and …, 2021 | 1 | 2021 |
| Photometric Quasars: The One Million Mark and 9-D SDSS+ Spitzer Selection GT Richards, A Myers, R Brunner, N Strand, R Nichol, A Gray, R Riegel, ... American Astronomical Society Meeting Abstracts 211, 142.02, 2007 | 1 | 2007 |
| Word sense disambiguation using a deep logico-neural network IY Akhalwaya, NA Khan, FP Luus, N Makondo, RN Riegel, A Gray US Patent App. 17/039,133, 2022 | | 2022 |
| A Benchmark for Generalizable and Interpretable Temporal Question Answering over Knowledge Bases S Neelam, U Sharma, H Karanam, S Ikbal, P Kapanipathi, I Abdelaziz, ... arXiv preprint arXiv:2201.05793, 2022 | | 2022 |
| First-order logical neural networks with bidirectional inference RN Riegel, FP Luus, IY Akhalwaya, NA Khan, N Makondo, F Barahona, ... US Patent App. 17/063,899, 2021 | | 2021 |
| Optimizing capacity and learning of weighted real-valued logic FP Luus, RN Riegel, IY Akhalwaya, NA Khan, EE Vos, N Makondo US Patent App. 15/931,223, 2021 | | 2021 |
Alexander GrayIBM Research AIVerified email at cc.gatech.edu
