Ryan Riegel
Cited by
Cited by
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
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
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
Logical neural networks
R Riegel, A Gray, F Luus, N Khan, N Makondo, IY Akhalwaya, H Qian, ...
arXiv preprint arXiv:2006.13155, 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
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
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
Large-scale kernel discriminant analysis with application to quasar discovery
A Gray, R Riegel
Proceedings of Computational Statistics, 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
A parallel N-body data mining framework
GF Boyer, RN Riegel, AG Gray
NIPS Workshop on Efficient Machine Learning 1304, 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
Foundations of reasoning with uncertainty via real-valued logics
R Fagin, R Riegel, A Gray
arXiv preprint arXiv:2008.02429, 2020
Generalized N-body problems: a Framework for Scalable Computation
RN Riegel
Georgia Institute of Technology, 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
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
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
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
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
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
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
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