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A Bayesian neural network predicts the dissolution of compact planetary
  systems

A Bayesian neural network predicts the dissolution of compact planetary systems

11 January 2021
M. Cranmer
Daniel Tamayo
H. Rein
Peter W. Battaglia
S. Hadden
P. Armitage
S. Ho
D. Spergel
    BDL
ArXivPDFHTML

Papers citing "A Bayesian neural network predicts the dissolution of compact planetary systems"

25 / 25 papers shown
Title
Sampling from Bayesian Neural Network Posteriors with Symmetric Minibatch Splitting Langevin Dynamics
Sampling from Bayesian Neural Network Posteriors with Symmetric Minibatch Splitting Langevin Dynamics
Daniel Paulin
Peter Whalley
Neil K. Chada
Benedict Leimkuhler
BDL
79
4
0
14 Oct 2024
Hierarchical Inference With Bayesian Neural Networks: An Application to
  Strong Gravitational Lensing
Hierarchical Inference With Bayesian Neural Networks: An Application to Strong Gravitational Lensing
S. Wagner-Carena
Ji Won Park
S. Birrer
P. Marshall
A. Roodman
Risa Wechsler
UQCV
BDL
66
39
0
26 Oct 2020
Is SGD a Bayesian sampler? Well, almost
Is SGD a Bayesian sampler? Well, almost
Chris Mingard
Guillermo Valle Pérez
Joar Skalse
A. Louis
BDL
53
53
0
26 Jun 2020
Discovering Symbolic Models from Deep Learning with Inductive Biases
Discovering Symbolic Models from Deep Learning with Inductive Biases
M. Cranmer
Alvaro Sanchez-Gonzalez
Peter W. Battaglia
Rui Xu
Kyle Cranmer
D. Spergel
S. Ho
AI4CE
67
475
0
19 Jun 2020
Array Programming with NumPy
Array Programming with NumPy
Charles R. Harris
K. Millman
S. Walt
R. Gommers
Pauli Virtanen
...
Tyler Reddy
Warren Weckesser
Hameer Abbasi
C. Gohlke
T. Oliphant
143
14,883
0
18 Jun 2020
Bayesian Deep Learning and a Probabilistic Perspective of Generalization
Bayesian Deep Learning and a Probabilistic Perspective of Generalization
A. Wilson
Pavel Izmailov
UQCV
BDL
OOD
89
652
0
20 Feb 2020
The Case for Bayesian Deep Learning
The Case for Bayesian Deep Learning
A. Wilson
UQCV
BDL
OOD
101
112
0
29 Jan 2020
PyTorch: An Imperative Style, High-Performance Deep Learning Library
PyTorch: An Imperative Style, High-Performance Deep Learning Library
Adam Paszke
Sam Gross
Francisco Massa
Adam Lerer
James Bradbury
...
Sasank Chilamkurthy
Benoit Steiner
Lu Fang
Junjie Bai
Soumith Chintala
ODL
401
42,299
0
03 Dec 2019
Learning Symbolic Physics with Graph Networks
Learning Symbolic Physics with Graph Networks
M. Cranmer
Rui Xu
Peter W. Battaglia
S. Ho
PINN
AI4CE
247
86
0
12 Sep 2019
A Simple Baseline for Bayesian Uncertainty in Deep Learning
A Simple Baseline for Bayesian Uncertainty in Deep Learning
Wesley J. Maddox
T. Garipov
Pavel Izmailov
Dmitry Vetrov
A. Wilson
BDL
UQCV
82
806
0
07 Feb 2019
Kymatio: Scattering Transforms in Python
Kymatio: Scattering Transforms in Python
M. Andreux
Tomás Angles
Georgios Exarchakis
R. Leonarduzzi
G. Rochette
...
M. Hirn
Edouard Oyallon
Sixhin Zhang
Carmine-Emanuele Cella
Michael Eickenberg
68
158
0
28 Dec 2018
Bayesian Layers: A Module for Neural Network Uncertainty
Bayesian Layers: A Module for Neural Network Uncertainty
Dustin Tran
Michael W. Dusenberry
Mark van der Wilk
Danijar Hafner
UQCV
BDL
99
122
0
10 Dec 2018
Relational inductive biases, deep learning, and graph networks
Relational inductive biases, deep learning, and graph networks
Peter W. Battaglia
Jessica B. Hamrick
V. Bapst
Alvaro Sanchez-Gonzalez
V. Zambaldi
...
Pushmeet Kohli
M. Botvinick
Oriol Vinyals
Yujia Li
Razvan Pascanu
AI4CE
NAI
720
3,119
0
04 Jun 2018
A disciplined approach to neural network hyper-parameters: Part 1 --
  learning rate, batch size, momentum, and weight decay
A disciplined approach to neural network hyper-parameters: Part 1 -- learning rate, batch size, momentum, and weight decay
L. Smith
271
1,028
0
26 Mar 2018
Averaging Weights Leads to Wider Optima and Better Generalization
Averaging Weights Leads to Wider Optima and Better Generalization
Pavel Izmailov
Dmitrii Podoprikhin
T. Garipov
Dmitry Vetrov
A. Wilson
FedML
MoMe
112
1,658
0
14 Mar 2018
Implicit Weight Uncertainty in Neural Networks
Implicit Weight Uncertainty in Neural Networks
Nick Pawlowski
Andrew Brock
Matthew C. H. Lee
Martin Rajchl
Ben Glocker
BDL
UQCV
110
94
0
03 Nov 2017
Super-Convergence: Very Fast Training of Neural Networks Using Large
  Learning Rates
Super-Convergence: Very Fast Training of Neural Networks Using Large Learning Rates
L. Smith
Nicholay Topin
AI4CE
82
522
0
23 Aug 2017
Stochastic Gradient Descent as Approximate Bayesian Inference
Stochastic Gradient Descent as Approximate Bayesian Inference
Stephan Mandt
Matthew D. Hoffman
David M. Blei
BDL
52
597
0
13 Apr 2017
Multiplicative Normalizing Flows for Variational Bayesian Neural
  Networks
Multiplicative Normalizing Flows for Variational Bayesian Neural Networks
Christos Louizos
Max Welling
BDL
144
460
0
06 Mar 2017
TensorFlow: A system for large-scale machine learning
TensorFlow: A system for large-scale machine learning
Martín Abadi
P. Barham
Jianmin Chen
Zhiwen Chen
Andy Davis
...
Vijay Vasudevan
Pete Warden
Martin Wicke
Yuan Yu
Xiaoqiang Zhang
GNN
AI4CE
423
18,334
0
27 May 2016
XGBoost: A Scalable Tree Boosting System
XGBoost: A Scalable Tree Boosting System
Tianqi Chen
Carlos Guestrin
735
38,858
0
09 Mar 2016
Dropout as a Bayesian Approximation: Representing Model Uncertainty in
  Deep Learning
Dropout as a Bayesian Approximation: Representing Model Uncertainty in Deep Learning
Y. Gal
Zoubin Ghahramani
UQCV
BDL
764
9,290
0
06 Jun 2015
Weight Uncertainty in Neural Networks
Weight Uncertainty in Neural Networks
Charles Blundell
Julien Cornebise
Koray Kavukcuoglu
Daan Wierstra
UQCV
BDL
181
1,886
0
20 May 2015
Sequence to Sequence Learning with Neural Networks
Sequence to Sequence Learning with Neural Networks
Ilya Sutskever
Oriol Vinyals
Quoc V. Le
AIMat
394
20,528
0
10 Sep 2014
Speech Recognition with Deep Recurrent Neural Networks
Speech Recognition with Deep Recurrent Neural Networks
Alex Graves
Abdel-rahman Mohamed
Geoffrey E. Hinton
212
8,513
0
22 Mar 2013
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