Mar 3, 2025 · Unconstrained optimization plays a crucial role in the training of neural networks. Unlike constrained optimization, where the solution must satisfy certain constraints, unconstrained optimization seeks to minimize (or maximize) an objective function without any …

We will focus on one particular case of optimization: Finding the parameters θ of a neural network that significantly reduces a cost function J(θ). first order optimization algorithms. First order algorithms are optimal for neural network training since the target loss functions can be decomposed to a sum over training data.

Dec 19, 2019 · This article provides an overview of optimization algorithms and theory for training neural networks. First, we discuss the issue of gradient explosion/vanishing and the more general issue of undesirable spectrum, and then discuss practical solutions including careful initialization and normalization methods.

May 14, 2024 · Optimization algorithms play a crucial role in training deep learning models. They control how a neural network is incrementally changed to model the complex relationships encoded in the training data.

Jun 13, 2020 · First, we discuss the issue of gradient explosion/vanishing and the more general issue of undesirable spectrum and then discuss practical solutions including careful initialization, normalization methods and skip connections.

Sep 4, 2024 · We answer this question by discussing how optimizers have evolved from traditional methods like gradient descent to more advanced techniques to address challenges posed by high-dimensional and non-convex problem space.

May 14, 2024 · Deep learning models exhibit excellent performance but require high computational resources. Optimization techniques like pruning, quantization, and knowledge distillation are vital for improving computational efficiency: Pruning reduces model size by removing less important neurons, involving identification, elimination, and optional fine-tuning.

Jan 17, 2025 · Large-scale, high-quality data are considered an essential factor for the successful application of many deep learning techniques. Meanwhile, numerous real-world deep learning tasks still have to contend with the lack of sufficient amounts of high-quality ...

The scale of deep learning models has grown tremendously in recent years. State-of-the-art models have reached billions of parameters and terabyte-scale model sizes. Training of these models demands memory bandwidth and capacity that can only be ...

Apr 26, 2025 · First-order optimization methods remain the standard for training deep neural networks (DNNs). Optimizers like Adam incorporate limited curvature information by preconditioning the stochastic gradient with a diagonal matrix. Despite the widespread adoption of first-order methods, second-order optimization algorithms often exhibit superior convergence compared to methods like Adam and SGD ...