Understanding Weighted Average: Definition, Explanations, Examples & Code
The Weighted Average algorithm is an ensemble method of calculation that assigns different levels of importance to different data points. It can be used in both supervised learning and unsupervised learning scenarios.
Weighted Average: Introduction
Domains
Learning Methods
Type
Machine Learning
Supervised, Unsupervised
Ensemble
The Weighted Average algorithm is a powerful calculation method that assigns diff
Introduction to Weighted Recurrent Quality Enhancement (WRQE)
Video compression has become an essential part of our daily lives. It is the technology behind streaming videos, social media, movies, and TV shows on our devices. Video compression reduces the size of video files, making it easier to transport and store. It also saves bandwidth and makes it possible to stream higher resolution videos. However, compressing videos can result in a loss of quality, and this is where Weighted Recurrent Q
Understanding WenLan: A Cross-Modal Pre-Training Model
WenLan is a two-tower pre-training model proposed within the cross-modal contrastive learning framework. The goal of this model is to effectively retrieve images and texts by learning two encoders that embed them into the same space. This is done by introducing contrastive learning with the InfoNCE loss into the BriVL model.
Cross-Modal Pre-Training Model Based on Image-Text Retrieval Task
A cross-modal pre-training model is defined base
Overview of WGAN-GP Loss
Generative Adversarial Networks (GANs) are a popular machine learning model used in various applications such as image generation, style transfer, and super-resolution. GANs consist of two neural networks, a generator, and a discriminator. The generator generates samples that attempt to mimic real samples, while the discriminator attempts to distinguish between real samples and the generated samples. The two networks are trained together in a min-max game where the disc
What is a Wide Residual Block?
A Wide Residual Block is a type of residual block that is designed to have a wider structure than other variants of residual blocks. This type of block is commonly used in convolutional neural networks (CNNs) to process images, videos or other similar data. Wide Residual Blocks were introduced in the WideResNet CNN architecture.
What is a Residual Block?
A Residual Block is a building block of a CNN that allows the network to skip over certain layers, making it
Wide&Deep: Combining Memorization and Generalization for Recommender Systems
Wide&Deep is a method used to train wide linear models and deep neural networks. The method combines the benefits of memorization and generalization for real-world recommender systems.
Before we dive into how Wide&Deep works, let's define some terms. Recommender systems are algorithms used to predict what a user might like based on their past behavior. A wide linear model is a type of machine learning model that can l
WideResNet: A High-Performing Variant on Residual Networks
In recent years, the field of deep learning has seen tremendous progress with the development of convolutional neural networks (CNNs). They have been used in various applications such as image recognition, natural language processing, and speech recognition, to name a few.
One of the most successful deep architectures, ResNets, was introduced in 2015. Since its inception, ResNets have consistently outperformed the previous state-of-the
Understanding Wildly Unsupervised Domain Adaptation
In the world of machine learning, domain adaptation is a technique used to train models to work accurately across different data domains. In other words, domain adaptation is a way of adjusting machine learning models so that they can work well even when they are presented with data that is slightly different from the data they were initially trained on.
Domain adaptation is important because the real world is not static. Data is always chang
Overview of Window-based Discriminator
Window-based Discriminator is a type of discriminator for generative adversarial networks that is designed to classify between distributions of small audio chunks. This method is analogous to a PatchGAN but is specifically created for audio. The aim of a window-based discriminator is to maintain coherence of audio signal across patches. In this article we will discuss what is a discriminator, what is a generative adversarial network, how a window-based dis
Word Alignment: A Fundamental Concept in Machine Translation
When we speak different languages, it can be difficult to accurately translate a sentence from one language to another. Word alignment is the task of finding the correspondence between source and target words in a pair of sentences that are translations of each other. Machine translation systems use word alignment to help them translate text from one language to another. It is a fundamental concept in natural language processing (NLP)
Have you ever wondered how it might be possible to change the gender of a word? This is where word attribute transfer comes in handy. Word attribute transfer is a technique that allows one to change attributes of a word to modify its meaning, without changing the word itself. This technique is used for text processing and is efficient for various applications, like machine translation, text analysis, language modeling, and many more.
What is Word Attribute Transfer?
Word Attribute Transfer is
Word Sense Disambiguation: An Overview
In natural language processing, Word Sense Disambiguation (WSD) is the process of identifying the correct meaning of a word in its context. This is important because many words in a language can have multiple meanings, and understanding the intended meaning is crucial for accurate understanding of text.
To solve this problem, a pre-defined sense inventory, a collection of word senses, is used to disambiguate the meaning of the word. One of the most popula
Understanding Word Sense Induction
Have you ever come across a word that has multiple meanings depending on the context in which it is used? For instance, the word "cold" could mean a low temperature, a sickness, or even an unsympathetic attitude. This creates ambiguity and poses a significant challenge in natural language processing. This is where Word Sense Induction (WSI) comes in handy.
WSI is an essential technique in Natural Language Processing (NLP) that helps in determining the context
What is WordPiece?
WordPiece is an algorithm used in natural language processing to break down words into smaller, more manageable subwords. This subword segmentation method is a type of unsupervised learning, which means that it does not require human annotation or pre-defined rules to work.
The WordPiece algorithm starts by initializing a word unit inventory with all the characters in the language. A language model is then built using this inventory, which allows the algorithm to identify th
What is Workflow Discovery?
Workflow Discovery (WD) is a technique used to extract workflows from task-oriented dialogues between two people, as introduced by the paper 'Workflow Discovery from Dialogues in the Low Data Regime'.
Simply put, a workflow is like a roadmap that guides people through a process. It consists of a series of actions that need to be taken in order to achieve a particular goal. WD aims to extract these workflows from conversations, providing a summary of the key actions
Xavier Initialization for Neural Networks
Xavier Initialization, also known as Glorot Initialization, is an important technique used for initializing the weights of neural networks. It determines how the weights of a network should be initialized, which can have a major impact on the final performance of the network. It was introduced by Xavier Glorot and Yoshua Bengio in their 2010 paper "Understanding the difficulty of training deep feedforward neural networks".
Initializations schemes are c
Xception is a convolutional neural network architecture that is increasingly gaining popularity because of its efficiency and effectiveness. The structure of this neural network is different from other standard convolutional neural networks, as it solely relies on depthwise separable convolution layers, which significantly reduces the computational requirements and memory footprint of the network.
The Need for Xception
Before understanding what Xception is, one first needs to understand the n
What is an XCiT Layer?
An XCiT Layer is a fundamental component of the XCiT (eX- tra large Convolutional Transformer) architecture. This architecture is an adaptation of the Transformer architecture, which is popular in natural language processing (NLP), to the field of computer vision.
The XCiT layer uses cross-covariance attention (XCA) as its primary operation. This is a type of self-attention mechanism that involves comparing different elements within a data set, rather than comparing each