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Sahil Garg
Sahil Garg
Machine Learning Research, Morgan Stanley
Verified email at morganstanley.com - Homepage
Title
Cited by
Cited by
Year
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
562016
Learning Non-Stationary Space-Time Models for Environmental Monitoring
S Garg, A Singh, F Ramos
AAAI Conference on Artificial Intelligence (AAAI-12), 2012
452012
Persistent monitoring of stochastic spatio-temporal phenomena with a small team of robots
S Garg, N Ayanian
Robotics: Science and Systems (RSS-14), 2014
31*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
232021
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
182023
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
142020
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
102021
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
92019
Stochastic Learning of Nonstationary Kernels for Natural Language Modeling
S Garg, GV Steeg, A Galstyan
arXiv preprint arXiv:1801.03911, 2017
82017
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
72017
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
42012
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
22023
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
22022
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
22019
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
12024
Increased Metaphor Production in Open-Ended Speech Samples of Patients With Prodromal and Developed Schizophrenia Detected with NLP
A Srivastava, Z Bilgrami, S Garg, C Corcoran, G Cecchi, B Nelson, A Yung, ...
Biological Psychiatry 91 (9), S50, 2022
12022
Measuring Metaphors and Bizarreness in Free Speech of Individuals at Clinical High Risk for Psychosis
G Cecchi, A Srivastava, C Corcoran, Z Bilgrami, A Yung, P Wolff, ...
Biological Psychiatry 91 (9), S32-S33, 2022
12022
Adaptive Sensing for Learning Nonstationary Environment Models
S Garg, A Singh, F Ramos
arXiv preprint arXiv:1804.10279, 2018
12018
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
2024
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Articles 1–20