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Data-driven approaches for predicting spread of infectious diseases
  through DINNs: Disease Informed Neural Networks

Data-driven approaches for predicting spread of infectious diseases through DINNs: Disease Informed Neural Networks

11 October 2021
Sagi Shaier
M. Raissi
P. Seshaiyer
    PINN
    AI4CE
ArXivPDFHTML

Papers citing "Data-driven approaches for predicting spread of infectious diseases through DINNs: Disease Informed Neural Networks"

13 / 13 papers shown
Title
X-IL: Exploring the Design Space of Imitation Learning Policies
X-IL: Exploring the Design Space of Imitation Learning Policies
Xiaogang Jia
Atalay Donat
Xi Huang
Xuan Zhao
Denis Blessing
...
Han A. Wang
Hanyi Zhang
Qian Wang
Rudolf Lioutikov
Gerhard Neumann
127
1
0
20 Feb 2025
Generative Adversarial Networks
Generative Adversarial Networks
Gilad Cohen
Raja Giryes
GAN
235
30,089
0
01 Mar 2022
EfficientDet: Scalable and Efficient Object Detection
EfficientDet: Scalable and Efficient Object Detection
Mingxing Tan
Ruoming Pang
Quoc V. Le
92
5,024
0
20 Nov 2019
BERT: Pre-training of Deep Bidirectional Transformers for Language
  Understanding
BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding
Jacob Devlin
Ming-Wei Chang
Kenton Lee
Kristina Toutanova
VLM
SSL
SSeg
1.5K
94,511
0
11 Oct 2018
Solving differential equations with unknown constitutive relations as
  recurrent neural networks
Solving differential equations with unknown constitutive relations as recurrent neural networks
Tobias J. Hagge
P. Stinis
Enoch Yeung
A. Tartakovsky
62
23
0
06 Oct 2017
Hidden Physics Models: Machine Learning of Nonlinear Partial
  Differential Equations
Hidden Physics Models: Machine Learning of Nonlinear Partial Differential Equations
M. Raissi
George Karniadakis
AI4CE
PINN
67
1,137
0
02 Aug 2017
Deep learning-based numerical methods for high-dimensional parabolic
  partial differential equations and backward stochastic differential equations
Deep learning-based numerical methods for high-dimensional parabolic partial differential equations and backward stochastic differential equations
Weinan E
Jiequn Han
Arnulf Jentzen
115
797
0
15 Jun 2017
Attention Is All You Need
Attention Is All You Need
Ashish Vaswani
Noam M. Shazeer
Niki Parmar
Jakob Uszkoreit
Llion Jones
Aidan Gomez
Lukasz Kaiser
Illia Polosukhin
3DV
640
130,942
0
12 Jun 2017
The Power of Depth for Feedforward Neural Networks
The Power of Depth for Feedforward Neural Networks
Ronen Eldan
Ohad Shamir
201
732
0
12 Dec 2015
You Only Look Once: Unified, Real-Time Object Detection
You Only Look Once: Unified, Real-Time Object Detection
Joseph Redmon
S. Divvala
Ross B. Girshick
Ali Farhadi
ObjD
658
36,801
0
08 Jun 2015
Automatic differentiation in machine learning: a survey
Automatic differentiation in machine learning: a survey
A. G. Baydin
Barak A. Pearlmutter
Alexey Radul
J. Siskind
PINN
AI4CE
ODL
154
2,796
0
20 Feb 2015
Adam: A Method for Stochastic Optimization
Adam: A Method for Stochastic Optimization
Diederik P. Kingma
Jimmy Ba
ODL
1.5K
149,842
0
22 Dec 2014
Deep Learning in Neural Networks: An Overview
Deep Learning in Neural Networks: An Overview
Jürgen Schmidhuber
HAI
228
16,336
0
30 Apr 2014
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