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Why does deep and cheap learning work so well?
v1v2v3v4 (latest)

Why does deep and cheap learning work so well?

29 August 2016
Henry W. Lin
Max Tegmark
David Rolnick
ArXiv (abs)PDFHTML

Papers citing "Why does deep and cheap learning work so well?"

50 / 236 papers shown
DNNs, Dataset Statistics, and Correlation Functions
DNNs, Dataset Statistics, and Correlation Functions
Robert W. Batterman
James F. Woodward
55
0
0
18 Nov 2025
Learning Fair Representations with Kolmogorov-Arnold Networks
Learning Fair Representations with Kolmogorov-Arnold Networks
Amisha Priyadarshini
Sergio Gago Masagué
FaML
525
0
0
14 Nov 2025
Neurosymbolic Deep Learning Semantics
Neurosymbolic Deep Learning Semantics
Artur Garcez
S. Odense
NAI
76
0
0
04 Nov 2025
Comparison of generalised additive models and neural networks in applications: A systematic review
Comparison of generalised additive models and neural networks in applications: A systematic review
Jessica Doohan
Lucas Kook
Kevin Burke
102
0
0
28 Oct 2025
Symmetry and Generalisation in Neural Approximations of Renormalisation Transformations
Symmetry and Generalisation in Neural Approximations of Renormalisation Transformations
Cassidy Ashworth
Pietro Lio
Francesco Caso
AI4CE
109
0
0
18 Oct 2025
Attention to Order: Transformers Discover Phase Transitions via Learnability
Attention to Order: Transformers Discover Phase Transitions via Learnability
Şener Özönder
133
0
0
08 Oct 2025
Multi-Agent Design Assistant for the Simulation of Inertial Fusion Energy
Multi-Agent Design Assistant for the Simulation of Inertial Fusion Energy
Meir H. Shachar
D. Sterbentz
Harshitha Menon
C. Jekel
M. Giselle Fernández-Godino
...
Kevin Korner
Robert Rieben
D. White
William J. Schill
Jonathan L. Belof
AI4CE
183
0
0
02 Oct 2025
Latent Twins
Latent Twins
Matthias Chung
Deepanshu Verma
Max Collins
Amit N. Subrahmanya
Varuni Katti Sastry
Vishwas Rao
SyDaAI4CE
179
2
0
24 Sep 2025
Should We Always Train Models on Fine-Grained Classes?
Should We Always Train Models on Fine-Grained Classes?
Davide Pirovano
Federico Milanesio
Michele Caselle
Piero Fariselli
Matteo Osella
127
0
0
05 Sep 2025
AI LLM Proof of Self-Consciousness and User-Specific Attractors
AI LLM Proof of Self-Consciousness and User-Specific Attractors
Jeffrey Camlin
73
0
0
22 Aug 2025
Quantum-Inspired Differentiable Integral Neural Networks (QIDINNs): A Feynman-Based Architecture for Continuous Learning Over Streaming Data
Quantum-Inspired Differentiable Integral Neural Networks (QIDINNs): A Feynman-Based Architecture for Continuous Learning Over Streaming Data
Oscar Boullosa Dapena
219
0
0
13 Jun 2025
On the creation of narrow AI: hierarchy and nonlocality of neural network skills
On the creation of narrow AI: hierarchy and nonlocality of neural network skills
Eric J. Michaud
Asher Parker-Sartori
Max Tegmark
459
2
0
21 May 2025
A Mathematical Philosophy of Explanations in Mechanistic Interpretability -- The Strange Science Part I.i
A Mathematical Philosophy of Explanations in Mechanistic Interpretability -- The Strange Science Part I.i
Kola Ayonrinde
Louis Jaburi
MILM
510
4
0
01 May 2025
The Quantum LLM: Modeling Semantic Spaces with Quantum Principles
Timo Aukusti Laine
186
2
0
13 Apr 2025
Compositionality Unlocks Deep Interpretable Models
Compositionality Unlocks Deep Interpretable Models
Thomas Dooms
Ward Gauderis
Geraint A. Wiggins
José Oramas
FAttCoGeAI4CE
227
2
0
03 Apr 2025
KAC: Kolmogorov-Arnold Classifier for Continual Learning
KAC: Kolmogorov-Arnold Classifier for Continual LearningComputer Vision and Pattern Recognition (CVPR), 2025
Yusong Hu
Zichen Liang
Fei Yang
Qibin Hou
Xialei Liu
Ming-Ming Cheng
CLL
327
5
0
27 Mar 2025
Feature Qualification by Deep Nets: A Constructive Approach
Feature Qualification by Deep Nets: A Constructive Approach
Feilong Cao
Shao-Bo Lin
MLT
180
0
0
24 Mar 2025
Do We Always Need the Simplicity Bias? Looking for Optimal Inductive Biases in the WildComputer Vision and Pattern Recognition (CVPR), 2025
Damien Teney
Liangze Jiang
Florin Gogianu
Ehsan Abbasnejad
1.1K
5
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13 Mar 2025
Multilevel Generative Samplers for Investigating Critical PhenomenaInternational Conference on Learning Representations (ICLR), 2025
Ankur Singha
E. Cellini
K. Nicoli
K. Jansen
Stefan Kühn
Shinichi Nakajima
374
6
0
11 Mar 2025
Aligning Generalisation Between Humans and Machines
Aligning Generalisation Between Humans and Machines
Filip Ilievski
Barbara Hammer
F. V. Harmelen
Benjamin Paassen
S. Saralajew
...
Vered Shwartz
Gabriella Skitalinskaya
Clemens Stachl
Gido M. van de Ven
T. Villmann
710
5
0
23 Nov 2024
Bio-inspired AI: Integrating Biological Complexity into Artificial
  Intelligence
Bio-inspired AI: Integrating Biological Complexity into Artificial Intelligence
Nima Dehghani
Michael Levin
234
3
0
22 Nov 2024
Dynamic neuron approach to deep neural networks: Decoupling neurons for renormalization group analysis
Dynamic neuron approach to deep neural networks: Decoupling neurons for renormalization group analysis
Donghee Lee
Hye-Sung Lee
Jaeok Yi
460
1
0
01 Oct 2024
Component-based Sketching for Deep ReLU Nets
Component-based Sketching for Deep ReLU Nets
Di Wang
Shao-Bo Lin
Deyu Meng
Feilong Cao
176
1
0
21 Sep 2024
WaterMAS: Sharpness-Aware Maximization for Neural Network Watermarking
WaterMAS: Sharpness-Aware Maximization for Neural Network WatermarkingInternational Conference on Pattern Recognition (ICPR), 2024
Carl De Sousa Trias
Mihai P. Mitrea
Attilio Fiandrotti
Marco Cagnazzo
Sumanta Chaudhuri
Enzo Tartaglione
AAML
232
2
0
05 Sep 2024
MS$^3$D: A RG Flow-Based Regularization for GAN Training with Limited
  Data
MS3^33D: A RG Flow-Based Regularization for GAN Training with Limited DataInternational Conference on Machine Learning (ICML), 2024
Jian Wang
Xin Lan
Yuxin Tian
Jiancheng Lv
AI4CE
187
2
0
20 Aug 2024
Symplectic Neural Networks Based on Dynamical Systems
Symplectic Neural Networks Based on Dynamical Systems
Benjamin K Tapley
286
4
0
19 Aug 2024
KAN: Kolmogorov-Arnold Networks
KAN: Kolmogorov-Arnold Networks
Ziming Liu
Yixuan Wang
Sachin Vaidya
Fabian Ruehle
James Halverson
Marin Soljacic
Thomas Y. Hou
Max Tegmark
993
1,319
0
30 Apr 2024
Extracting Formulae in Many-Valued Logic from Deep Neural Networks
Extracting Formulae in Many-Valued Logic from Deep Neural NetworksIEEE Transactions on Signal Processing (IEEE TSP), 2024
Yani Zhang
Helmut Bölcskei
178
0
0
22 Jan 2024
Reverse Engineering Deep ReLU Networks An Optimization-based Algorithm
Reverse Engineering Deep ReLU Networks An Optimization-based Algorithm
Mehrab Hamidi
254
0
0
07 Dec 2023
Autonomous Learning of Generative Models with Chemical Reaction Network
  Ensembles
Autonomous Learning of Generative Models with Chemical Reaction Network EnsemblesJournal of the Royal Society Interface (J. R. Soc. Interface), 2023
William G. Poole
T. Ouldridge
Manoj Gopalkrishnan
178
8
0
02 Nov 2023
Deep Neural Networks for Automatic Speaker Recognition Do Not Learn
  Supra-Segmental Temporal Features
Deep Neural Networks for Automatic Speaker Recognition Do Not Learn Supra-Segmental Temporal FeaturesPattern Recognition Letters (PR), 2023
Daniel Neururer
Volker Dellwo
Thilo Stadelmann
282
3
0
01 Nov 2023
The Evolution of the Interplay Between Input Distributions and Linear
  Regions in Networks
The Evolution of the Interplay Between Input Distributions and Linear Regions in Networks
Xuan Qi
Yi Wei
192
0
0
28 Oct 2023
A Hyperparameter Study for Quantum Kernel Methods
A Hyperparameter Study for Quantum Kernel MethodsQuantum Machine Intelligence (QMI), 2023
Sebastian Egginger
Alona Sakhnenko
J. M. Lorenz
223
12
0
18 Oct 2023
Fundamental Limits of Deep Learning-Based Binary Classifiers Trained with Hinge Loss
Fundamental Limits of Deep Learning-Based Binary Classifiers Trained with Hinge Loss
T. Getu
Georges Kaddoum
M. Bennis
317
1
0
13 Sep 2023
Renormalizing Diffusion Models
Renormalizing Diffusion Models
Jordan S. Cotler
Semon Rezchikov
DiffMAI4CE
253
15
0
23 Aug 2023
Scale-Preserving Automatic Concept Extraction (SPACE)
Scale-Preserving Automatic Concept Extraction (SPACE)Machine-mediated learning (ML), 2023
Andres Felipe Posada-Moreno
Lukas Kreisköther
T. Glander
Sebastian Trimpe
112
2
0
11 Aug 2023
Iterative Magnitude Pruning as a Renormalisation Group: A Study in The
  Context of The Lottery Ticket Hypothesis
Iterative Magnitude Pruning as a Renormalisation Group: A Study in The Context of The Lottery Ticket Hypothesis
Abu-Al Hassan
145
0
0
06 Aug 2023
Deep Convolutional Neural Networks with Zero-Padding: Feature Extraction
  and Learning
Deep Convolutional Neural Networks with Zero-Padding: Feature Extraction and Learning
Zhixiong Han
Baichen Liu
Shao-Bo Lin
Ding-Xuan Zhou
168
6
0
30 Jul 2023
Spherical and Hyperbolic Toric Topology-Based Codes On Graph Embedding
  for Ising MRF Models: Classical and Quantum Topology Machine Learning
Spherical and Hyperbolic Toric Topology-Based Codes On Graph Embedding for Ising MRF Models: Classical and Quantum Topology Machine Learning
V. Usatyuk
Sergey Egorov
Denis Sapozhnikov
330
3
0
28 Jul 2023
Bayesian Renormalization
Bayesian Renormalization
D. Berman
Marc S. Klinger
A. G. Stapleton
352
23
0
17 May 2023
Deep neural networks have an inbuilt Occam's razor
Deep neural networks have an inbuilt Occam's razorNature Communications (Nat. Commun.), 2023
Chris Mingard
Henry Rees
Guillermo Valle Pérez
A. Louis
UQCVBDL
307
16
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13 Apr 2023
GenPhys: From Physical Processes to Generative Models
GenPhys: From Physical Processes to Generative Models
Ziming Liu
Di Luo
Yilun Xu
Tommi Jaakkola
M. Tegmark
AI4CE
235
21
0
05 Apr 2023
From Wide to Deep: Dimension Lifting Network for Parameter-efficient
  Knowledge Graph Embedding
From Wide to Deep: Dimension Lifting Network for Parameter-efficient Knowledge Graph EmbeddingIEEE Transactions on Knowledge and Data Engineering (TKDE), 2023
Borui Cai
Yong Xiang
Longxiang Gao
Di Wu
Heng Zhang
Jiongdao Jin
Tom H. Luan
290
5
0
22 Mar 2023
Expressivity of Shallow and Deep Neural Networks for Polynomial
  Approximation
Expressivity of Shallow and Deep Neural Networks for Polynomial Approximation
Itai Shapira
146
0
0
06 Mar 2023
MOSAIC, acomparison framework for machine learning models
MOSAIC, acomparison framework for machine learning models
Mattéo Papin
Yann Beaujeault-Taudiere
F. Magniette
VLM
70
0
0
30 Jan 2023
A prediction and behavioural analysis of machine learning methods for
  modelling travel mode choice
A prediction and behavioural analysis of machine learning methods for modelling travel mode choiceTransportation Research Part C: Emerging Technologies (TRC), 2023
José Ángel Martín-Baos
Julio Alberto López-Gómez
Luis Rodriguez-Benitez
T. Hillel
Ricardo García-Ródenas
245
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11 Jan 2023
Inference on Time Series Nonparametric Conditional Moment Restrictions
  Using General Sieves
Inference on Time Series Nonparametric Conditional Moment Restrictions Using General Sieves
Xiaohong Chen
Yuan Liao
Weichen Wang
198
0
0
31 Dec 2022
Renormalization in the neural network-quantum field theory
  correspondence
Renormalization in the neural network-quantum field theory correspondence
Harold Erbin
Vincent Lahoche
D. O. Samary
250
8
0
22 Dec 2022
Changes from Classical Statistics to Modern Statistics and Data Science
Changes from Classical Statistics to Modern Statistics and Data Science
Kai Zhang
Shan-Yu Liu
M. Xiong
304
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30 Oct 2022
Hierarchical quantum circuit representations for neural architecture
  search
Hierarchical quantum circuit representations for neural architecture searchnpj Quantum Information (NQI), 2022
Matt Lourens
I. Sinayskiy
D. Park
Carsten Blank
Francesco Petruccione
285
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26 Oct 2022
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