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2004.06977
Cited By
On Learning Rates and Schrödinger Operators
Journal of machine learning research (JMLR), 2020
15 April 2020
Bin Shi
Weijie J. Su
Sai Li
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Papers citing
"On Learning Rates and Schrödinger Operators"
36 / 36 papers shown
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Where Do Large Learning Rates Lead Us?
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M. Kodryan
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E. Lobacheva
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29 Oct 2024
Stepping on the Edge: Curvature Aware Learning Rate Tuners
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Mathieu Blondel
Fabian Pedregosa
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08 Jul 2024
Complex fractal trainability boundary can arise from trivial non-convexity
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203
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20 Jun 2024
Primitive Agentic First-Order Optimization
International Conference on Control, Decision and Information Technologies (CoDIT), 2024
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260
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07 Jun 2024
A General Continuous-Time Formulation of Stochastic ADMM and Its Variants
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283
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22 Apr 2024
A simple lower bound for the complexity of estimating partition functions on a quantum computer
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Giacomo Nannicini
266
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03 Apr 2024
Arbitrary Polynomial Separations in Trainable Quantum Machine Learning
Eric R. Anschuetz
Xun Gao
200
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13 Feb 2024
Quantum Langevin Dynamics for Optimization
Communications in Mathematical Physics (CMP), 2023
Zherui Chen
Yuchen Lu
Hao Wang
Yizhou Liu
Tongyang Li
AI4CE
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27 Nov 2023
A quantum-classical performance separation in nonconvex optimization
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Yufan Zheng
Xiaodi Wu
284
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01 Nov 2023
Global Convergence of SGD For Logistic Loss on Two Layer Neural Nets
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Samyak Jha
Anirbit Mukherjee
335
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17 Sep 2023
On Underdamped Nesterov's Acceleration
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Bin Shi
Ya-xiang Yuan
299
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28 Apr 2023
Training a Two Layer ReLU Network Analytically
Italian National Conference on Sensors (INS), 2023
Adrian Barbu
319
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06 Apr 2023
Learning Rate Schedules in the Presence of Distribution Shift
International Conference on Machine Learning (ICML), 2023
Matthew Fahrbach
Adel Javanmard
Vahab Mirrokni
Pratik Worah
298
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27 Mar 2023
Global Convergence of SGD On Two Layer Neural Nets
Information and Inference A Journal of the IMA (JIII), 2022
Pulkit Gopalani
Anirbit Mukherjee
268
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20 Oct 2022
On Quantum Speedups for Nonconvex Optimization via Quantum Tunneling Walks
Quantum (Quantum), 2022
Yizhou Liu
Weijie J. Su
Tongyang Li
357
25
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29 Sep 2022
Gradient Norm Minimization of Nesterov Acceleration:
o
(
1
/
k
3
)
o(1/k^3)
o
(
1/
k
3
)
Shu Chen
Bin Shi
Ya-xiang Yuan
292
20
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19 Sep 2022
On Uniform Boundedness Properties of SGD and its Momentum Variants
Xiaoyu Wang
M. Johansson
242
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25 Jan 2022
Imitating Deep Learning Dynamics via Locally Elastic Stochastic Differential Equations
Neural Information Processing Systems (NeurIPS), 2021
Jiayao Zhang
Hua Wang
Weijie J. Su
326
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11 Oct 2021
On the Hyperparameters in Stochastic Gradient Descent with Momentum
Journal of machine learning research (JMLR), 2021
Bin Shi
309
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09 Aug 2021
Revisiting the Effects of Stochasticity for Hamiltonian Samplers
International Conference on Machine Learning (ICML), 2021
Giulio Franzese
Dimitrios Milios
Maurizio Filippone
Pietro Michiardi
267
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30 Jun 2021
Bandwidth-based Step-Sizes for Non-Convex Stochastic Optimization
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M. Johansson
293
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On the Validity of Modeling SGD with Stochastic Differential Equations (SDEs)
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Sadhika Malladi
Sanjeev Arora
305
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24 Feb 2021
On the Convergence of Step Decay Step-Size for Stochastic Optimization
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Xiaoyu Wang
Sindri Magnússon
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278
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18 Feb 2021
Exploring Deep Neural Networks via Layer-Peeled Model: Minority Collapse in Imbalanced Training
Proceedings of the National Academy of Sciences of the United States of America (PNAS), 2021
Cong Fang
Hangfeng He
Qi Long
Weijie J. Su
FAtt
519
217
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29 Jan 2021
Boundary Conditions for Linear Exit Time Gradient Trajectories Around Saddle Points: Analysis and Algorithm
IEEE Transactions on Information Theory (IEEE Trans. Inf. Theory), 2021
Rishabh Dixit
Mert Gurbuzbalaban
W. Bajwa
280
2
0
07 Jan 2021
Loss Barcode: A Topological Measure of Escapability in Loss Landscapes
S. Barannikov
Daria Voronkova
I. Trofimov
Alexander Korotin
Grigorii Sotnikov
Evgeny Burnaev
Evgeny Burnaev
284
7
0
31 Dec 2020
Approximation of BV functions by neural networks: A regularity theory approach
Analysis and Applications (Anal. Appl.), 2020
B. Avelin
Vesa Julin
148
3
0
15 Dec 2020
Reconciling Modern Deep Learning with Traditional Optimization Analyses: The Intrinsic Learning Rate
Zhiyuan Li
Kaifeng Lyu
Sanjeev Arora
300
78
0
06 Oct 2020
Group Knowledge Transfer: Federated Learning of Large CNNs at the Edge
Chaoyang He
M. Annavaram
A. Avestimehr
FedML
386
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0
28 Jul 2020
On stochastic mirror descent with interacting particles: convergence properties and variance reduction
Anastasia Borovykh
N. Kantas
P. Parpas
G. Pavliotis
246
13
0
15 Jul 2020
Exit Time Analysis for Approximations of Gradient Descent Trajectories Around Saddle Points
Rishabh Dixit
Mert Gurbuzbalaban
W. Bajwa
278
5
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01 Jun 2020
Scaling-up Distributed Processing of Data Streams for Machine Learning
M. Nokleby
Haroon Raja
W. Bajwa
278
18
0
18 May 2020
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