Quasi-Hyperbolic Momentum (QHM) is a technique used in stochastic optimization to improve momentum SGD (Stochastic Gradient Descent). This is achieved by combining an SGD step with a momentum step. In other words, QHM changes momentum SGD by averaging the SGD step and momentum step.
Understanding QHM
Before delving into QHM, it is necessary to understand what momentum SGD is. Momentum SGD is a popular optimization algorithm used in machine learning that accelerates SGD by adding momentum. Thi
Rectified Adam, also known as RAdam, is a modification of the Adam stochastic optimizer, which aims to solve the bad convergence problem experienced by Adam. It does so by rectifying the variance of the adaptive learning rate.
The Problem with Adam
The authors of RAdam contend that the primary issue with Adam is its adaptive learning rate's undesirably high variance in the early stages of model training due to the low number of training samples. This characteristic of Adam often leads to bad
RMSProp: A Better Way to Optimize Neural Network Models
Neural network models can be incredibly powerful tools for solving complex problems, but training them can be a challenge. One of the biggest issues is determining the learning rate - the size of the steps the model takes when adjusting its weights during the training process. Traditionally, a single global learning rate was used, but this can create problems if the magnitudes of the gradients for different weights vary or change during th
Deep learning is a type of artificial intelligence that uses neural networks to analyze data and solve complicated problems. To train these networks, we need optimizers like stochastic gradient descent (SGD) that help us find the minimum weights and biases at which the model loss is lowest. However, SGD has some issues when it comes to non-convex cost function graphs, and this is why we use SGD with Momentum as an optimizer.
Reasons why SGD does not work perfectly
The three main reasons why S
Stochastic Gradient Descent with Weight Decay (SGDW) is an optimization technique that can help in training machine learning models more efficiently. This technique decouples weight decay from the gradient update. It involves the use of several mathematical equations to help in updating the model parameters to achieve better model performance.
What is Stochastic Gradient Descent?
Before diving into what SGDW is, let's first discuss what stochastic gradient descent (SGD) means.
SGD is an opti
The Slime Mould Algorithm, commonly referred to as SMA, is a new and innovative stochastic optimizer with a unique mathematical model inspired by the oscillation mode of slime moulds in nature. This algorithm uses adaptive weights to simulate the process of producing feedback in the form of positive and negative propagation waves, which ultimately forms the optimal path for connecting food sources. SMA has excellent exploratory abilities and high exploitation propensity, making it a powerful too
SM3 is a memory-efficient adaptive optimization method used in machine learning. It helps reduce the memory overhead of the optimizer, allowing for larger models and batch sizes. This new approach has retained the benefits of standard per-parameter adaptivity while reducing the memory requirements, making it a popular choice in modern machine learning.
Why traditional methods don't work for large scale applications
Standard adaptive gradient-based optimizers, such as AdaGrad and Adam, tune th
Stochastic Gradient Descent (SGD): A Simple Overview
Machine learning models are essential in predicting outcomes, identifying trends, and giving insights from data. We use mathematical techniques like optimization to train models, making them more accurate in making predictions for future data. One popular optimization technique is stochastic gradient descent (SGD).
What is SGD?
SGD is an iterative optimization technique that uses mini-batches of data to calculate the gradient of the loss f
Stochastic Weight Averaging (SWA) is an optimization procedure used in machine learning that involves averaging multiple points along the trajectory of stochastic gradient descent (SGD). It involves averaging weights and using a cyclical or constant learning rate to discover broader optima.
What is Optimization in Machine Learning?
Before delving into the topic of Stochastic Weight Averaging, it is important to understand what optimization is in machine learning. Optimization involves finding
YellowFin: An Efficient Learning Rate and Momentum Tuner
YellowFin is a state-of-the-art optimization algorithm that automatically tunes the learning rate and momentum in deep learning models. It is motivated by a robustness analysis of quadratic objectives and aims to improve the convergence rate of deep neural networks by optimizing hyperparameters.
The significance of YellowFin lies in the fact that it extends the notion of tuning learning rates and momentum to non-convex objectives. This a