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2020 – today
- 2023
[i11]Anna Wong, Shu Ge, Nassim Oufattole, Adam Dejl, Megan Su, Ardavan Saeedi, Li-Wei H. Lehman:
A Knowledge Distillation Approach for Sepsis Outcome Prediction from Multivariate Clinical Time Series. CoRR abs/2311.09566 (2023)- [i10]Joel Stremmel, Ardavan Saeedi, Hamid Hassanzadeh, Sanjit S. Batra, Jeffrey Hertzberg, Jaime Murillo, Eran Halperin:
XAIQA: Explainer-Based Data Augmentation for Extractive Question Answering. CoRR abs/2312.03567 (2023) - 2022
- [c13]Ardavan Saeedi, Yuria Utsumi, Li Sun, Kayhan Batmanghelich, Li-Wei H. Lehman:
Knowledge Distillation via Constrained Variational Inference. AAAI 2022: 8132-8140 - [i9]Adam Dejl, Harsh Deep, Jonathan Fei, Ardavan Saeedi, Li-Wei H. Lehman:
Treatment-RSPN: Recurrent Sum-Product Networks for Sequential Treatment Regimes. CoRR abs/2211.07052 (2022) - 2021
- [c12]Ardavan Saeedi, Payman Yadollahpour, Sumedha Singla, Brian Pollack, William M. Wells III, Frank C. Sciurba, Kayhan Batmanghelich:
Incorporating External Information in Tissue Subtyping: A Topic Modeling Approach. MLHC 2021: 478-505 - 2020
- [c11]Igor Lovchinsky, Alon Daks, Israel Malkin, Pouya Samangouei, Ardavan Saeedi, Yang Liu, Swami Sankaranarayanan, Tomer Gafner, Ben Sternlieb, Patrick Maher, Nathan Silberman:
Discrepancy Ratio: Evaluating Model Performance When Even Experts Disagree on the Truth. ICLR 2020
2010 – 2019
- 2019
- [c10]Ryutaro Tanno, Ardavan Saeedi, Swami Sankaranarayanan, Daniel C. Alexander
, Nathan Silberman:
Learning From Noisy Labels by Regularized Estimation of Annotator Confusion. CVPR 2019: 11244-11253 - [i8]Ryutaro Tanno, Ardavan Saeedi, Swami Sankaranarayanan, Daniel C. Alexander, Nathan Silberman:
Learning From Noisy Labels By Regularized Estimation Of Annotator Confusion. CoRR abs/1902.03680 (2019) - 2018
- [b1]Ardavan Saeedi:
Latent variable models for understanding user behavior in software applications. Massachusetts Institute of Technology, Cambridge, USA, 2018 - [c9]Ardavan Saeedi, Matthew D. Hoffman, Stephen J. DiVerdi, Asma Ghandeharioun, Matthew J. Johnson, Ryan P. Adams:
Multimodal Prediction and Personalization of Photo Edits with Deep Generative Models. AISTATS 2018: 1309-1317 - [c8]Pouya Samangouei, Ardavan Saeedi, Liam Nakagawa, Nathan Silberman:
ExplainGAN: Model Explanation via Decision Boundary Crossing Transformations. ECCV (10) 2018: 681-696 - 2017
- [j1]Ardavan Saeedi, Tejas D. Kulkarni, Vikash K. Mansinghka, Samuel J. Gershman:
Variational Particle Approximations. J. Mach. Learn. Res. 18: 69:1-69:29 (2017) - [i7]Ardavan Saeedi, Matthew D. Hoffman, Stephen J. DiVerdi, Asma Ghandeharioun, Matthew J. Johnson, Ryan P. Adams:
Multimodal Prediction and Personalization of Photo Edits with Deep Generative Models. CoRR abs/1704.04997 (2017) - 2016
- [c7]Kayhan N. Batmanghelich, Ardavan Saeedi, Karthik Narasimhan, Samuel Gershman:
Nonparametric Spherical Topic Modeling with Word Embeddings. ACL (2) 2016 - [c6]Ardavan Saeedi, Matthew D. Hoffman, Matthew J. Johnson, Ryan P. Adams:
The Segmented iHMM: A Simple, Efficient Hierarchical Infinite HMM. ICML 2016: 2682-2691 - [c5]Nematollah Kayhan Batmanghelich, Ardavan Saeedi, Raúl San José Estépar, Michael H. Cho, William M. Wells III:
Inferring Disease Status by Non-parametric Probabilistic Embedding. MCV/BAMBI@MICCAI 2016: 49-57 - [c4]Tejas D. Kulkarni, Karthik Narasimhan, Ardavan Saeedi, Josh Tenenbaum:
Hierarchical Deep Reinforcement Learning: Integrating Temporal Abstraction and Intrinsic Motivation. NIPS 2016: 3675-3683 - [i6]Kayhan N. Batmanghelich, Ardavan Saeedi, Karthik Narasimhan, Samuel Gershman:
Nonparametric Spherical Topic Modeling with Word Embeddings. CoRR abs/1604.00126 (2016) - [i5]Tejas D. Kulkarni, Karthik Narasimhan, Ardavan Saeedi, Joshua B. Tenenbaum:
Hierarchical Deep Reinforcement Learning: Integrating Temporal Abstraction and Intrinsic Motivation. CoRR abs/1604.06057 (2016) - [i4]Tejas D. Kulkarni, Ardavan Saeedi, Simanta Gautam, Samuel J. Gershman:
Deep Successor Reinforcement Learning. CoRR abs/1606.02396 (2016) - 2015
- [c3]Jonathan H. Huggins, Karthik Narasimhan, Ardavan Saeedi, Vikash Mansinghka:
JUMP-Means: Small-Variance Asymptotics for Markov Jump Processes. ICML 2015: 693-701 - [c2]Nematollah Kayhan Batmanghelich, Ardavan Saeedi, Michael H. Cho, Raúl San José Estépar, Polina Golland:
Generative Method to Discover Genetically Driven Image Biomarkers. IPMI 2015: 30-42 - [i3]Jonathan H. Huggins, Ardavan Saeedi, Matthew J. Johnson:
Detailed Derivations of Small-Variance Asymptotics for some Hierarchical Bayesian Nonparametric Models. CoRR abs/1501.00052 (2015) - [i2]Jonathan H. Huggins, Karthik Narasimhan, Ardavan Saeedi, Vikash K. Mansinghka:
JUMP-Means: Small-Variance Asymptotics for Markov Jump Processes. CoRR abs/1503.00332 (2015) - 2014
- [i1]Tejas D. Kulkarni, Ardavan Saeedi, Samuel Gershman:
Variational Particle Approximations. CoRR abs/1402.5715 (2014) - 2011
- [c1]Ardavan Saeedi, Alexandre Bouchard-Côté:
Priors over Recurrent Continuous Time Processes. NIPS 2011: 2052-2060
Coauthor Index
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