Papers
Communities
Events
Blog
Pricing
Search
Open menu
Home
Papers
2008.00646
Cited By
Interpretable Sequence Learning for COVID-19 Forecasting
3 August 2020
Sercan Ö. Arik
Chun-Liang Li
Jinsung Yoon
Rajarishi Sinha
Arkady Epshteyn
Long T. Le
V. Menon
Shashank Singh
Leyou Zhang
Nate Yoder
Martin Nikoltchev
Yash Sonthalia
Hootan Nakhost
Elli Kanal
Tomas Pfister
AI4TS
Re-assign community
ArXiv
PDF
HTML
Papers citing
"Interpretable Sequence Learning for COVID-19 Forecasting"
22 / 22 papers shown
Title
DemOpts: Fairness corrections in COVID-19 case prediction models
N. Awasthi
S. Abrar
Daniel Smolyak
Vanessa Frias-Martinez
29
0
0
15 May 2024
Scalable Continuous-time Diffusion Framework for Network Inference and Influence Estimation
Keke Huang
Ruize Gao
Bogdan Cautis
Xiaokui Xiao
23
3
0
05 Mar 2024
MSGNN: Multi-scale Spatio-temporal Graph Neural Network for Epidemic Forecasting
Mingjie Qiu
Zhiyi Tan
Bingkun Bao
38
6
0
30 Aug 2023
Machine Learning for Infectious Disease Risk Prediction: A Survey
Mutong Liu
Yang Liu
Jiming Liu
LM&MA
AI4CE
24
0
0
06 Aug 2023
Deep COVID-19 Forecasting for Multiple States with Data Augmentation
C. Fong
Dit-Yan Yeung
25
0
0
02 Feb 2023
Deep Grey-Box Modeling With Adaptive Data-Driven Models Toward Trustworthy Estimation of Theory-Driven Models
Naoya Takeishi
Alexandros Kalousis
AAML
40
3
0
24 Oct 2022
Differentiable Agent-based Epidemiology
Ayush Chopra
Alexander Rodríguez
J. Subramanian
Arnau Quera-Bofarull
Balaji Krishnamurthy
B. Prakash
Ramesh Raskar
AI4CE
25
19
0
20 Jul 2022
Data-Centric Epidemic Forecasting: A Survey
Alexander Rodríguez
Harshavardhan Kamarthi
Pulak Agarwal
Javen Ho
Mira Patel
Suchet Sapre
B. Prakash
OOD
31
18
0
19 Jul 2022
Indirect Active Learning
Shashank Singh
18
0
0
03 Jun 2022
Robust Probabilistic Time Series Forecasting
Taeho Yoon
Youngsuk Park
Ernest K. Ryu
Yuyang Wang
AAML
AI4TS
20
18
0
24 Feb 2022
EINNs: Epidemiologically-informed Neural Networks
Alexander Rodríguez
Jiaming Cui
Naren Ramakrishnan
B. Adhikari
B. Prakash
30
28
0
21 Feb 2022
Predicting infections in the Covid-19 pandemic -- lessons learned
Sharare Zehtabian
Siavash Khodadadeh
D. Turgut
Ladislau Bölöni
20
2
0
02 Dec 2021
A spatiotemporal machine learning approach to forecasting COVID-19 incidence at the county level in the USA
Benjamin Lucas
Behzad Vahedi
M. Karimzadeh
OOD
37
31
0
24 Sep 2021
Global and Local Interpretation of black-box Machine Learning models to determine prognostic factors from early COVID-19 data
Ananya Jana
Carlos D Minacapelli
V. Rustgi
Dimitris N. Metaxas
20
1
0
10 Sep 2021
Back2Future: Leveraging Backfill Dynamics for Improving Real-time Predictions in Future
Harshavardhan Kamarthi
Alexander Rodríguez
B. Prakash
AI4TS
30
14
0
08 Jun 2021
Deep Bayesian Active Learning for Accelerating Stochastic Simulation
D. Wu
Ruijia Niu
Matteo Chinazzi
Alessandro Vespignani
Yi Ma
Rose Yu
AI4CE
33
8
0
05 Jun 2021
SIMLR: Machine Learning inside the SIR model for COVID-19 Forecasting
R. Vega
Leonardo Flores
Russell Greiner
17
31
0
03 Jun 2021
MAGI-X: Manifold-Constrained Gaussian Process Inference for Unknown System Dynamics
Chaofan Huang
Simin Ma
Shihao Yang
22
0
0
27 May 2021
Breiman's two cultures: You don't have to choose sides
Andrew C. Miller
N. Foti
E. Fox
36
11
0
25 Apr 2021
Physics-Integrated Variational Autoencoders for Robust and Interpretable Generative Modeling
Naoya Takeishi
Alexandros Kalousis
DRL
AI4CE
30
54
0
25 Feb 2021
Inter-Series Attention Model for COVID-19 Forecasting
Xiaoyong Jin
Yu-Xiang Wang
Xifeng Yan
AI4TS
24
36
0
25 Oct 2020
Lagrangian Neural Networks
M. Cranmer
S. Greydanus
Stephan Hoyer
Peter W. Battaglia
D. Spergel
S. Ho
PINN
139
424
0
10 Mar 2020
1