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Statistical Mechanics and Artificial Neural Networks: Principles,
  Models, and Applications

Statistical Mechanics and Artificial Neural Networks: Principles, Models, and Applications

5 April 2024
Lucas Böttcher
Gregory R. Wheeler
ArXivPDFHTML

Papers citing "Statistical Mechanics and Artificial Neural Networks: Principles, Models, and Applications"

13 / 13 papers shown
Title
Visualizing high-dimensional loss landscapes with Hessian directions
Visualizing high-dimensional loss landscapes with Hessian directions
Lucas Böttcher
Gregory R. Wheeler
54
14
0
28 Aug 2022
Interpretable Polynomial Neural Ordinary Differential Equations
Interpretable Polynomial Neural Ordinary Differential Equations
Colby Fronk
Linda R. Petzold
55
27
0
09 Aug 2022
Spectrally Adapted Physics-Informed Neural Networks for Solving
  Unbounded Domain Problems
Spectrally Adapted Physics-Informed Neural Networks for Solving Unbounded Domain Problems
Mingtao Xia
Lucas Böttcher
T. Chou
58
21
0
06 Feb 2022
Hopfield Networks is All You Need
Hopfield Networks is All You Need
Hubert Ramsauer
Bernhard Schafl
Johannes Lehner
Philipp Seidl
Michael Widrich
...
David P. Kreil
Michael K Kopp
Günter Klambauer
Johannes Brandstetter
Sepp Hochreiter
83
429
0
16 Jul 2020
Neural Ordinary Differential Equation Control of Dynamics on Graphs
Neural Ordinary Differential Equation Control of Dynamics on Graphs
Thomas Asikis
Lucas Böttcher
Nino Antulov-Fantulin
52
42
0
17 Jun 2020
The gap between theory and practice in function approximation with deep
  neural networks
The gap between theory and practice in function approximation with deep neural networks
Ben Adcock
N. Dexter
40
93
0
16 Jan 2020
Learning the Ising Model with Generative Neural Networks
Learning the Ising Model with Generative Neural Networks
Francesco DÁngelo
Lucas Böttcher
AI4CE
33
28
0
15 Jan 2020
On Empirical Comparisons of Optimizers for Deep Learning
On Empirical Comparisons of Optimizers for Deep Learning
Dami Choi
Christopher J. Shallue
Zachary Nado
Jaehoon Lee
Chris J. Maddison
George E. Dahl
66
260
0
11 Oct 2019
Neural Ordinary Differential Equations
Neural Ordinary Differential Equations
T. Chen
Yulia Rubanova
J. Bettencourt
David Duvenaud
AI4CE
341
5,081
0
19 Jun 2018
A high-bias, low-variance introduction to Machine Learning for
  physicists
A high-bias, low-variance introduction to Machine Learning for physicists
Pankaj Mehta
Marin Bukov
Ching-Hao Wang
A. G. Day
C. Richardson
Charles K. Fisher
D. Schwab
AI4CE
79
873
0
23 Mar 2018
On Large-Batch Training for Deep Learning: Generalization Gap and Sharp
  Minima
On Large-Batch Training for Deep Learning: Generalization Gap and Sharp Minima
N. Keskar
Dheevatsa Mudigere
J. Nocedal
M. Smelyanskiy
P. T. P. Tang
ODL
398
2,935
0
15 Sep 2016
Qualitatively characterizing neural network optimization problems
Qualitatively characterizing neural network optimization problems
Ian Goodfellow
Oriol Vinyals
Andrew M. Saxe
ODL
105
522
0
19 Dec 2014
Deep Learning in Neural Networks: An Overview
Deep Learning in Neural Networks: An Overview
Jürgen Schmidhuber
HAI
222
16,336
0
30 Apr 2014
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