Improving Gradient Descent for Better Deep Learning with Natural ...

In this paper, we develop an efficient sketch-based empirical natural gradient method (SENG) for large-scale deep learning problems.

Frédéric Barbaresco on LinkedIn: Improving Gradient Descent for ...

Frédéric Barbaresco's Post · Improving Gradient Descent for Better Deep Learning with Natural Gradients · More Relevant Posts · More videos on ...

Natural Gradient Descent without the Tears : r/reinforcementlearning

A big problem for most policy gradient methods is high variance which leads to unstable training. Ideally, we would want a way to reduce how ...

[2409.16422] Is All Learning (Natural) Gradient Descent? - arXiv

Abstract:This paper shows that a wide class of effective learning rules -- those that improve a scalar performance measure over a given time ...

Natural Gradient Descent: A Deep Dive Using MNIST - AI Mind

In machine learning, optimization techniques play a crucial role in guiding models towards better performance. One such technique is natural ...

Deep Learning — Part 2: Gradient Descent and variants - Medium

Gradient Descent algorithm can be extended to a network of neurons with backpropagation, we need gradients of Loss function with respect to all ...

Enhancing deep neural network training efficiency and performance ...

The representative method of non-adaptive method is SGD, from which a variety of improved algorithms have been derived, DEMON which is a ...

New Insights and Perspectives on the Natural Gradient Method

Improving the convergence of back-propagation learning with second order ... Krylov subspace descent for deep learning. In International. Conference on ...

Deep Learning Optimization: Beyond Stochastic Gradient Descent

Adaptive learning rate algorithms like RMSprop, Adagrad, Adam that adapt the learning rate based on parameter updates to improve convergence ...

An Improved Empirical Fisher Approximation for Natural Gradient...

Approximate Natural Gradient Descent (NGD) methods are an important family of optimisers for deep learning models, which use approximate Fisher information ...

Natural gradients - Andy Jones

When applied to an optimization problem in the form of natural gradient descent, it can greatly improve the convergence speed compared to vanilla gradient ...

Efficient neural codes naturally emerge through gradient descent ...

To interpret these findings, we show mathematically that learning with gradient descent in neural networks preferentially creates ...

Gradient Descent in Machine Learning: A Deep Dive - DataCamp

The gradient descent algorithm is mostly used in the fields of machine learning and deep learning. The latter can be considered as an improved version of ...

Fast Convergence of Natural Gradient Descent for Over ...

A convergence analysis of gradient descent for deep linear neural networks. ... Improving the convergence of backpropagation learning with second order methods.

Improving Generalization Performance of Natural Gradient Learning ...

When a network structure, an error function, and training data are fixed, all learning algorithms based on the gradient-descent method have the same equilibrium ...

Deep Learning Optimization Algorithms - neptune.ai

In this article, we'll survey the most commonly used deep learning optimization algorithms, including Gradient Descent, Stochastic Gradient Descent, and the ...

Gradient descent optimization in AI - Innovatiana

In the field of deep learning, gradient descent is essential for efficiently training deep neural networks, which are complex and often have ...

Improving gradient methods via coordinate transformations

In this paper, we introduce a generic strategy to accelerate and improve the overall performance of machine-learning algorithms, ...

[1301.3584] Revisiting natural gradient for deep networks - ar5iv

Section 7 describes how unlabeled data can be incorporated into natural gradient descent in order to improve generalization error. Section 8 explores ...

Loss of plasticity in deep continual learning - Nature

The most important modern learning methods are based on stochastic gradient descent (SGD) and the backpropagation algorithm, ideas that ...