[D] Neural nets that refuse to converge

[D] Neural nets that refuse to converge : r/MachineLearning - Reddit

If the network is doing better than chance but is just not becoming much better than chance, then the set of things to try becomes a lot bigger ...

Things to try when Neural Network not Converging - Stack Overflow

This question is intended to give some pointers to people stuck with neural nets which are not converging.

When does the problem arise of neural networks not converging?

There can be a number of situations where this occurs. It can be that the series that elapses the flow diverges - meaning it does not ...

What should I do when my neural network doesn't generalize well?

Why is your model not generalizing properly? ... The most important part is understanding why your network doesn't generalize well. High-capacity ...

Poor convergence of a neural network, which implements NMF

I'd like to understand why this simple network fails to converge. The resulting MSE error is an order of 10^4 - 10^5 bigger than what could ...

Bayesian Neural Network unable to converge on simple model

Construct BNN 1 input → 4 hidden → 4 hidden → 1 output · Training with ADVI I use ADVI since my actual problem has a huge data set and MCMC is ...

When does a neural network fail to converge?

Why does a neural net fail to converge? Most of the neural network fails to converge because of an error in the modelling. Let us say the data ...

Neural Network Weights Do Not Converge to Stationary Points

Lobacheva, E., Kodryan, M., Chirkova, N., Malinin, A., and. Vetrov, D. On the periodic behavior of neural network training with batch normalization and weight ...

Neural Networks: Optimization Part 1

– While not converged: • w , , = w , , − ∆w , ,. • D l,i,j = ( , , ). , ,. • If sign prevD l,i,j == sign D l,i,j : – ∆w , , = min (𝛼∆w , , ,∆. ) – prevD l,i,j = ...

Non-convergence of stochastic gradient descent in the training of ...

However, the training of a deep neural network (DNN) is a non-convex problem, and questions about guarantees and convergence rates of SGD in this context are ...

Neural Network Weights Do Not Converge to Stationary Points

Specifically, we provide numerical evidence that in large-scale neural network training (e.g., ImageNet + ResNet101, and WT103 + TransformerXL models), the ...

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.

Neural Network Weights Do Not Converge to Stationary Points

Lobacheva, E., Kodryan, M., Chirkova, N., Malinin, A., and. Vetrov, D. On the periodic behavior of neural network training with batch normalization and weight ...

Improve Shallow Neural Network Generalization and Avoid Overfitting

For early stopping, you must be careful not to use an algorithm that converges too rapidly. If you are using a fast algorithm (like trainlm ), set the ...

How to efficiently and precisely fit a function with neural networks?

(And there is not a lot of theory behind their convergence rate. See ... Sounds to me like you'd want gradients to vanish near the optimum for ...

What is a Neural Network? - IBM

The decision to go or not to go is our predicted outcome, or y-hat. ... With each training example, the parameters of the model adjust to gradually converge at ...

Convergence of gradient descent for learning linear neural networks

We show that under suitable conditions on the stepsizes gradient descent converges to a critical point of the loss function, i.e., the square ...

On the rate of convergence of fully connected deep neural network ...

In particular, in [31] the topology of the neural network was not completely ... D. 3. Approximation of smooth functions by fully connected deep neural networks ...

A Convergence Analysis of Gradient Descent for Deep Linear Neural...

Keywords: Deep Learning, Learning Theory, Non-Convex Optimization · TL;DR: We analyze gradient descent for deep linear neural networks, providing ...

Uniform convergence may be unable to explain generalization in ...

Guided by our observations, we then present examples of overparameterized linear classifiers and neural networks trained by gradient descent (GD) where uniform ...