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1903.03488
Cited By
Is Deeper Better only when Shallow is Good?
8 March 2019
Eran Malach
Shai Shalev-Shwartz
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Papers citing
"Is Deeper Better only when Shallow is Good?"
28 / 28 papers shown
On the Expressive Power of Tree-Structured Probabilistic Circuits
Neural Information Processing Systems (NeurIPS), 2024
Lang Yin
Han Zhao
TPM
324
4
0
07 Oct 2024
Depth Separations in Neural Networks: Separating the Dimension from the Accuracy
Annual Conference Computational Learning Theory (COLT), 2024
Itay Safran
Daniel Reichman
Paul Valiant
280
1
0
11 Feb 2024
Provable Guarantees for Nonlinear Feature Learning in Three-Layer Neural Networks
Neural Information Processing Systems (NeurIPS), 2023
Eshaan Nichani
Alexandru Damian
Jason D. Lee
MLT
496
16
0
11 May 2023
When Deep Learning Meets Polyhedral Theory: A Survey
Joey Huchette
Gonzalo Muñoz
Thiago Serra
Calvin Tsay
AI4CE
672
44
0
29 Apr 2023
Depth Separation with Multilayer Mean-Field Networks
International Conference on Learning Representations (ICLR), 2023
Y. Ren
Mo Zhou
Rong Ge
OOD
223
3
0
03 Apr 2023
Exploring Generalizable Distillation for Efficient Medical Image Segmentation
IEEE journal of biomedical and health informatics (IEEE JBHI), 2022
Xingqun Qi
Zhuo Wu
Min Ren
Muyi Sun
Caifeng Shan
Zhe Sun
206
9
0
26 Jul 2022
Hidden Progress in Deep Learning: SGD Learns Parities Near the Computational Limit
Neural Information Processing Systems (NeurIPS), 2022
Boaz Barak
Benjamin L. Edelman
Surbhi Goel
Sham Kakade
Eran Malach
Cyril Zhang
367
155
0
18 Jul 2022
An initial alignment between neural network and target is needed for gradient descent to learn
International Conference on Machine Learning (ICML), 2022
Emmanuel Abbe
Elisabetta Cornacchia
Jan Hązła
Christopher Marquis
333
16
0
25 Feb 2022
Random Feature Amplification: Feature Learning and Generalization in Neural Networks
Journal of machine learning research (JMLR), 2022
Spencer Frei
Niladri S. Chatterji
Peter L. Bartlett
MLT
301
32
0
15 Feb 2022
Interplay between depth of neural networks and locality of target functions
Takashi Mori
Masakuni Ueda
145
0
0
28 Jan 2022
Optimization-Based Separations for Neural Networks
Annual Conference Computational Learning Theory (COLT), 2021
Itay Safran
Jason D. Lee
729
16
0
04 Dec 2021
Expressivity of Neural Networks via Chaotic Itineraries beyond Sharkovsky's Theorem
Clayton Sanford
Vaggos Chatziafratis
155
1
0
19 Oct 2021
The staircase property: How hierarchical structure can guide deep learning
Neural Information Processing Systems (NeurIPS), 2021
Emmanuel Abbe
Enric Boix-Adserà
Matthew Brennan
Guy Bresler
Dheeraj M. Nagaraj
215
64
0
24 Aug 2021
Bayesian Deep Learning Hyperparameter Search for Robust Function Mapping to Polynomials with Noise
Nidhin Harilal
Udit Bhatia
A. Ganguly
OOD
97
0
0
23 Jun 2021
Fractal Structure and Generalization Properties of Stochastic Optimization Algorithms
Neural Information Processing Systems (NeurIPS), 2021
A. Camuto
George Deligiannidis
Murat A. Erdogdu
Mert Gurbuzbalaban
Umut cSimcsekli
Lingjiong Zhu
144
30
0
09 Jun 2021
Depth separation beyond radial functions
Journal of machine learning research (JMLR), 2021
Luca Venturi
Samy Jelassi
Tristan Ozuch
Joan Bruna
247
16
0
02 Feb 2021
The Connection Between Approximation, Depth Separation and Learnability in Neural Networks
Annual Conference Computational Learning Theory (COLT), 2021
Eran Malach
Gilad Yehudai
Shai Shalev-Shwartz
Ohad Shamir
211
21
0
31 Jan 2021
A simple geometric proof for the benefit of depth in ReLU networks
Asaf Amrami
Yoav Goldberg
211
1
0
18 Jan 2021
Depth-Width Trade-offs for Neural Networks via Topological Entropy
Kaifeng Bu
Yaobo Zhang
Qingxian Luo
138
8
0
15 Oct 2020
When Hardness of Approximation Meets Hardness of Learning
Eran Malach
Shai Shalev-Shwartz
157
10
0
18 Aug 2020
Activation function dependence of the storage capacity of treelike neural networks
Jacob A. Zavatone-Veth
Cengiz Pehlevan
232
15
0
21 Jul 2020
Hausdorff Dimension, Heavy Tails, and Generalization in Neural Networks
Umut Simsekli
Ozan Sener
George Deligiannidis
Murat A. Erdogdu
244
67
0
16 Jun 2020
Is deeper better? It depends on locality of relevant features
Takashi Mori
Masahito Ueda
OOD
231
5
0
26 May 2020
Better Depth-Width Trade-offs for Neural Networks through the lens of Dynamical Systems
International Conference on Machine Learning (ICML), 2020
Vaggos Chatziafratis
Sai Ganesh Nagarajan
Ioannis Panageas
190
15
0
02 Mar 2020
A Deep Conditioning Treatment of Neural Networks
International Conference on Algorithmic Learning Theory (ALT), 2020
Naman Agarwal
Pranjal Awasthi
Satyen Kale
AI4CE
362
18
0
04 Feb 2020
Depth-Width Trade-offs for ReLU Networks via Sharkovsky's Theorem
International Conference on Learning Representations (ICLR), 2019
Vaggos Chatziafratis
Sai Ganesh Nagarajan
Ioannis Panageas
Tianlin Li
146
22
0
09 Dec 2019
Learning Boolean Circuits with Neural Networks
Eran Malach
Shai Shalev-Shwartz
178
4
0
25 Oct 2019
Expression of Fractals Through Neural Network Functions
IEEE Journal on Selected Areas in Information Theory (JSAIT), 2019
Nadav Dym
B. Sober
Ingrid Daubechies
138
15
0
27 May 2019
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