RAG, short for Retriever-Augmented Generation is a language generation model that is a combination of pre-trained parametric and non-parametric memories. With RAG, users are presented with an efficient and comprehensive system for generating language content.
What is RAG?
RAG is a language generation model that can generate human-like text, even out of context, by combining a pre-trained seq2seq model, and a dense vector index of information from Wikipedia accessed through a pre-trained neura
Rainbow DQN: An Improved Learning Algorithm for Reinforcement Learning
Reinforcement learning is a subfield of machine learning that deals with how an agent interacts with an environment to achieve a specific goal. One of the most popular methods for reinforcement learning is Deep Q-Networks (DQN). However, DQN has been found to have certain limitations, including overestimation bias and inefficiency in prioritizing experiences. A team of researchers sought to improve upon the performance of DQ
RandAugment: A Method for Automated Data Augmentation
Data augmentation is a technique used in machine learning where additional training data is created from existing data by applying various transformations, such as flipping, rotating, or changing contrast. This helps to improve the performance of machine learning models by providing them with more diverse and representative examples to learn from. However, manually applying these transformations to a large dataset can be time-consuming and e
What is REM?
If you have ever heard of machine learning or deep reinforcement learning, you may have come across a term called Random Ensemble Mixture (REM). But what is REM and how does it work? In simple terms, REM is an extension of the Deep Q-Network (DQN) algorithm for deep reinforcement learning inspired by a technique called Dropout.
DQN is a popular algorithm in deep reinforcement learning that uses artificial neural networks to learn a policy that maximizes the expected reward in a gi
What is Random Erasing in Machine Learning?
Random Erasing is a data augmentation technique used in machine learning to train computer models to recognize objects in images. Specifically, it is a method used for training convolutional neural networks (CNN). It randomly selects a rectangular region in an image and erases the pixels in that region with random values. This creates a level of occlusion in the images, forcing the network to recognize objects even when they are partially obscured. In
Understanding Random Forest: Definition, Explanations, Examples & Code
Random Forest is an ensemble machine learning method that operates by constructing a multitude of decision trees at training time and outputting the class that is the mode of the classes of the individual trees. It falls under the category of supervised learning.
Random Forest: Introduction
Domains
Learning Methods
Type
Machine Learning
Supervised
Ensemble
The Random Forest algorithm is a popular and effective
If you are interested in photography or image processing, you might have heard of a technique called Random Gaussian Blur. This technique can be used to enhance images or create new data for machine learning applications. In this article, we will explore what Gaussian Blur is, how Random Gaussian Blur works, and where it can be applied.
What is Gaussian Blur?
Gaussian Blur is a type of image filter that is used to reduce the noise or detail in an image. It works by averaging the pixel values
Random grayscale is a technique used in image processing and machine learning that can help improve the accuracy and diversity of image datasets. It involves converting a color image into grayscale with a certain probability, which can help prevent overfitting and make the data more robust.
What is Random Grayscale?
Random grayscale is a type of image data augmentation that can help improve the accuracy of machine learning models that are trained on image data. Image data augmentation is a te
Random Horizontal Flip: A Guide to Image Data Augmentation
In the world of machine learning and computer vision, image data augmentation is an important technique used to improve the performance of image-based algorithms. Random Horizontal Flip is one such data augmentation technique that flips images horizontally with a certain probability. In this article, we'll delve deeper into what Random Horizontal Flip is, how it works, and its applications.
What is Random Horizontal Flip?
Random Hori
Overview of R-Mix
R-Mix is a data augmentation technique used in machine learning that combines two different types of Mix-up methods. Mix-up methods aim to improve the accuracy and reliability of neural networks by generating more data for the model to learn from. The two methods that are combined in R-Mix are random Mix-up and Saliency-guided Mix-up. By blending these two techniques, R-Mix produces a procedure that is both fast and effective.
What is Mix-up?
Before diving into the details
When it comes to training machine learning models to recognize images, having a diverse set of training data can be crucial for good performance. However, collecting a large and diverse dataset can be difficult and time-consuming. This is where data augmentation comes in, which is a technique used to artificially increase the size and diversity of a dataset. One popular type of data augmentation is Random Resized Crop.
What is Random Resized Crop?
Random Resized Crop is a type of image data a
Random Scaling is a technique used to modify images by changing their size in a random manner. This image data augmentation technique is used in machine learning and deep learning applications to improve the performance of image recognition algorithms. In this article, we will explore what random scaling is, how it works, and its benefits.
What is Random Scaling?
Random Scaling is a type of image data augmentation that involves changing the scale of an image randomly. This means that the size
Random Search is a way to optimize the performance of machine learning algorithms by randomly selecting combinations of hyperparameters. This technique can be used in discrete, continuous, and mixed settings and is especially effective when the optimization problem has a low intrinsic dimensionality.
What is Hyperparameter Optimization?
Before diving into Random Search, it’s important to understand hyperparameters and why optimization is necessary for machine learning algorithms to perform at
What is Random Synthesized Attention?
Random Synthesized Attention is a type of attention used in machine learning models. It is different from other types of attention because it does not depend on the input tokens. Instead, the attention weights are initialized randomly.
This attention method was introduced with the Synthesizer architecture. Random Synthesized Attention is used to improve the performance of these models by learning a task-specific alignment that works well globally across ma
In the world of machine learning, there is a concept called activation functions. These functions help to determine the output of a neural network. One popular activation function is called Randomized Leaky Rectified Linear Units, or RReLU for short.
What is RReLU?
RReLU is a type of activation function that randomly samples the negative slope for activation values. The function was first introduced and used in the Kaggle NDSB Competition. During training, a random number is sampled from a un
Image data augmentation is the process of artificially increasing the size of our dataset by applying various transformations to the images. These transformations include rotation, flipping, zooming, and many more. One of these transformations called "RandomRotate" randomly rotates an image by a degree.
What is RandomRotate?
RandomRotate is a type of image data augmentation that randomly rotates an image by a degree. It is a common technique used in machine learning and computer vision for im
The world of artificial intelligence and machine learning is expanding at an incredible pace with new concepts and technologies emerging every day. One such technology is RandWire, which is a type of convolutional neural network that is randomly wired using a stochastic network generator. The RandWire model is an exciting development in the field of artificial intelligence that has the potential to revolutionize the way that convolutional neural networks are constructed and operate.
What is Ra
Rational Activation Function: An Introduction
Activation functions are an integral part of a deep neural network. They define how the input signal in a node should be transformed into an output signal. The most commonly used activation functions are Sigmoid, ReLU, and Tanh. Rational activation functions are a recent addition to the family of activation functions, and they are ratio of polynomials as learnable functions. Let's dive deeper into rational activation functions and understand their b