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No Wrong Turns: The Simple Geometry Of Neural Networks Optimization Paths

No Wrong Turns: The Simple Geometry Of Neural Networks Optimization Paths

Charles Guille-Escuret
Hiroki Naganuma
Kilian Fatras
Ioannis Mitliagkas
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Papers citing "No Wrong Turns: The Simple Geometry Of Neural Networks Optimization Paths"

5 / 5 papers shown
Title
Geometric Inductive Biases of Deep Networks: The Role of Data and Architecture
Geometric Inductive Biases of Deep Networks: The Role of Data and Architecture
Sajad Movahedi
Antonio Orvieto
Seyed-Mohsen Moosavi-Dezfooli
AI4CE
AAML
189
0
0
15 Oct 2024
Analyzing Monotonic Linear Interpolation in Neural Network Loss Landscapes
Analyzing Monotonic Linear Interpolation in Neural Network Loss Landscapes
James Lucas
Juhan Bae
Michael Ruogu Zhang
Stanislav Fort
R. Zemel
Roger C. Grosse
MoMe
164
28
0
22 Apr 2021
Linear Convergence of Gradient and Proximal-Gradient Methods Under the Polyak-Łojasiewicz Condition
Linear Convergence of Gradient and Proximal-Gradient Methods Under the Polyak-Łojasiewicz Condition
Hamed Karimi
J. Nutini
Mark W. Schmidt
139
1,201
0
16 Aug 2016
SegNet: A Deep Convolutional Encoder-Decoder Architecture for Image Segmentation
SegNet: A Deep Convolutional Encoder-Decoder Architecture for Image Segmentation
Vijay Badrinarayanan
Alex Kendall
R. Cipolla
SSeg
446
15,645
0
02 Nov 2015
Stochastic Gradient Descent for Non-smooth Optimization: Convergence Results and Optimal Averaging Schemes
Stochastic Gradient Descent for Non-smooth Optimization: Convergence Results and Optimal Averaging Schemes
Ohad Shamir
Tong Zhang
104
571
0
08 Dec 2012