| Extracting Biomolecular Interactions Using Semantic Parsing of Biomedical Text S Garg, A Galstyan, U Hermjakob, D Marcu AAAI Conference on Artificial Intelligence (AAAI-16), 2016 | 59 | 2016 |
| Learning Non-Stationary Space-Time Models for Environmental Monitoring S Garg, A Singh, F Ramos AAAI Conference on Artificial Intelligence (AAAI-12), 2012 | 44 | 2012 |
| Lag-llama: Towards foundation models for time series forecasting K Rasul, A Ashok, AR Williams, A Khorasani, G Adamopoulos, ... arXiv preprint arXiv:2310.08278, 2023 | 39 | 2023 |
| Persistent monitoring of stochastic spatio-temporal phenomena with a small team of robots S Garg, N Ayanian Robotics: Science and Systems (RSS-14), 2014 | 29* | 2014 |
| Negative symptoms and speech pauses in youths at clinical high risk for psychosis ER Stanislawski, ZR Bilgrami, C Sarac, S Garg, S Heisig, GA Cecchi, ... npj Schizophrenia 7 (1), 3, 2021 | 24 | 2021 |
| Linking language features to clinical symptoms and multimodal imaging in individuals at clinical high risk for psychosis SS Haas, GE Doucet, S Garg, SN Herrera, C Sarac, ZR Bilgrami, ... European Psychiatry 63 (1), e72, 2020 | 16 | 2020 |
| NERO: a biomedical named-entity (recognition) ontology with a large, annotated corpus reveals meaningful associations through text embedding K Wang, R Stevens, H Alachram, Y Li, L Soldatova, R King, S Ananiadou, ... NPJ systems biology and applications 7 (1), 38, 2021 | 11 | 2021 |
| Empowering time series analysis with large language models: A survey Y Jiang, Z Pan, X Zhang, S Garg, A Schneider, Y Nevmyvaka, D Song arXiv preprint arXiv:2402.03182, 2024 | 10 | 2024 |
| Lag-llama: Towards foundation models for probabilistic time series forecasting K Rasul, A Ashok, AR Williams, H Ghonia, R Bhagwatkar, A Khorasani, ... Preprint, 2024 | 9 | 2024 |
| Kernelized Hashcode Representations for Relation Extraction S Garg, A Galstyan, G Ver Steeg, I Rish, G Cecchi, S Gao AAAI Conference on Artificial Intelligence (AAAI-19), 2019 | 9 | 2019 |
| Stochastic Learning of Nonstationary Kernels for Natural Language Modeling S Garg, GV Steeg, A Galstyan arXiv preprint arXiv:1801.03911, 2017 | 8 | 2017 |
| Neurogenesis-Inspired Dictionary Learning: Online Model Adaption in a Changing World S Garg, I Rish, G Cecchi, A Lozano International Joint Conference on Artificial Intelligence (IJCAI-17), 2017 | 7 | 2017 |
| Modeling Dialogues with Hashcode Representations: A Nonparametric Approach S Garg, G Cecchi, I Rish, P Goyal, S Ghazarian, S Gao, G Ver Steeg, ... AAAI Conference on Artificial Intelligence (AAAI-20), 2020 | 5* | 2020 |
| Efficient space-time modeling for informative sensing S Garg, A Singh, F Ramos International Workshop on Knowledge Discovery from Sensor Data, colocated …, 2012 | 4 | 2012 |
| In-or out-of-distribution detection via dual divergence estimation S Garg, S Dutta, M Dalirrooyfard, A Schneider, Y Nevmyvaka Uncertainty in Artificial Intelligence, 635-646, 2023 | 3 | 2023 |
| Structural Knowledge Informed Continual Multivariate Time Series Forecasting Z Pan, Y Jiang, D Song, S Garg, K Rasul, A Schneider, Y Nevmyvaka arXiv preprint arXiv:2402.12722, 2024 | 2 | 2024 |
| A case report and first-person account of an individual at risk for psychosis who improved during the COVID-19 pandemic SN Herrera, C Sarac, ZR Bilgrami, MF Dobbs, R Jespersen, SS Haas, ... Psychosis 14 (2), 190-199, 2022 | 2 | 2022 |
| Nearly-Unsupervised Hashcode Representations for Relation Extraction S Garg, A Galstyan, G Ver Steeg, G Cecchi Empirical Methods in Natural Language Processing (EMNLP-19), 2019 | 2 | 2019 |
| Information theoretic clustering via divergence maximization among clusters S Garg, M Dalirrooyfard, A Schneider, Y Adler, Y Nevmyvaka, Y Chen, ... Uncertainty in Artificial Intelligence, 624-634, 2023 | 1 | 2023 |
| Estimating transfer entropy under long ranged dependencies S Garg, U Gupta, Y Chen, SD Gupta, Y Adler, A Schneider, Y Nevmyvaka Uncertainty in Artificial Intelligence, 685-695, 2022 | 1 | 2022 |
Aram GalstyanUSC ISI & Amazon AGI在 isi.edu 的电子邮件经过验证
