Neural Network Weights Do Not Converge to Stationary Points

Neural Network Weights Do Not Converge to Stationary Points - arXiv

This work examines the deep disconnect between existing theoretical analyses of gradient-based algorithms and the practice of training deep neural networks.

Neural Network Weights Do Not Converge to Stationary Points: An Invariant Measure Perspective. Jingzhao Zhang 1 Haochuan Li 2 Suvrit Sra 2 Ali Jadbabaie 2.

Neural Network Weights Do Not Converge to Stationary Points

Generalization analyses of deep learning typically assume that the training converges to a fixed point. But, recent results indicate that in ...

arXiv:2110.06256v2 [cs.LG] 17 Jun 2022

Neural Network Weights Do Not Converge to Stationary Points: An Invariant Measure Perspective. Jingzhao Zhang 1 Haochuan Li 2 Suvrit Sra 2 ...

Neural Network Weights Do Not Converge to Stationary Points - TUM

Neural Network Weights Do Not Converge to Stationary Points: An Invariant Measure Perspective · Tsinghua University · Massachusetts Institute of Technology.

Convergence of neural network weights - Cross Validated

Is your data properly normalized? · Usually the number of neuron in the hidden layer should be of the same size than in your input layer. · @ ...

[D] How to Mathematically Prove that a Neural Network is ... - Reddit

Neural networks do not in general converge to the best set of weights. It's currently an open question whether there is a high probability ...

Weights not converging while cost function has converged in neural ...

There is nothing strange about the effect you observe: As long as the loss is not zero, the gradient will not be zero, and thus the gradient ...

On Convergence of Training Loss Without Reaching Stationary Points

Remarkably, however, we observe that while weights do not converge to stationary points, the value of the loss function converges. Inspired by ...

On the generalization of learning algorithms that do not converge

Abstract: Generalization analyses of deep learning typically assume that the training converges to a fixed point.

Track: OPT: Non-Convex

Neural Network Weights Do Not Converge to Stationary Points: An Invariant Measure Perspective ... This work examines the deep disconnect between existing ...

Dynamics and symmetries in neural network learning

Non-convergence in NN training. • “we observe that even though the weights do not converge to stationary points, the progress in minimizing the loss function ...

On Convergence of Training Loss Without Reaching Stationary Points

... neural network training, such as in ImageNet, ResNet, and WT103 + TransformerXL models, the Neural Network weight variables do not converge to stationary points ...

Jingzhao Zhang | Papers With Code

Neural Network Weights Do Not Converge to Stationary Points: An Invariant Measure Perspective ... do not match. 0. Paper · Code · Quantifying Exposure Bias for ...

On the generalization of learning algorithms that do not converge

But, recent results indicate that in practice, the weights of deep neural networks optimized with stochastic gradient descent often oscillate indefinitely. To ...

Stationary Points of a Shallow Neural Network with Quadratic ...

It is worth noting that neural networks with random weights is an ... can escape the saddle points and converge to the global minima.

‪Haochuan Li‬ - ‪Google Scholar‬

2022. Neural network weights do not converge to stationary points: An invariant measure perspective. J Zhang, H Li, S Sra, A Jadbabaie. International ...

Convergence in deep learning - Medium

A neural network can be considered to have converged when the training error (or loss) stops decreasing or has reached a minimum level of acceptable error.

On the generalization of learning algorithms that do not converge

Neural network weights do not converge to stationary points: An invariant measure perspective. In K. Chaudhuri, S. Jegelka, L. Song, C ...

Chapter 4 - A primer on optimization - Chinmay Hegde

... is a non-negative measure of label fit, and $f_w$ is the function represented by the neural network with weight/bias parameters $w$. We are abusing notation ...