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Chi-squared Automatic Interaction Detection

Understanding Chi-squared Automatic Interaction Detection: Definition, Explanations, Examples & Code Chi-squared Automatic Interaction Detection, commonly known as CHAID, is a decision tree technique that falls under the category of supervised learning. It is based on adjusted significance testing and is utilized to identify the most significant predictors of a particular outcome. This algorithm is a popular tool for data mining and statistical analysis, as it allows for the creation of a decis

Child-Tuning

Understanding Child-Tuning: Fine-Tuning Technique for Pretrained Models If you're interested in the world of machine learning, chances are you have heard of child-tuning. It is a fine-tuning technique that is used to update a subset of parameters of large pre-trained models in order to effectively adapt them to a range of tasks while maintaining their generalization ability. In simple terms, child-tuning allows you to take an already-existing deep learning model and make it better suited for yo

Chimera

Understanding Chimera: A Pipeline Model Parallelism Scheme Chimera is a model parallelism scheme designed to train large-scale models efficiently. Its unique feature is the combination of bidirectional pipelines, namely down and up pipelines, to accomplish the task. The aim is to execute a large number of micro-batches by each worker within a training iteration with the minimum of four pipeline stages. How Chimera Pipeline Works? Chimera pipeline, as shown in the figure, consists of four pip

Chinchilla

Since 2020, manufacturers have been steadily releasing bigger and bigger models like the GPT-3 (175B), LaMDA (137B), Jurassic-1 (178B), Megatron-Turing NLG (530B), and Gopher (280B). According to Kaplan’s law, these models are an improvement over their predecessors (GPT-2, BERT), but they still fall short of their full potential. In their most recent paper, researchers at DeepMind dissect the conventional wisdom that more complex models equal better performance. The company has uncovered a pre

Chinese Pre-trained Unbalanced Transformer

Introduction to Chinese Pre-trained Unbalanced Transformer Chinese language processing has gained tremendous attention in AI research and development. One of the major challenges in Chinese natural language understanding and generation (NLU and NLG) is that they entail complex syntactical and semantic features. To overcome this challenge and improve the performance of Chinese NLU and NLG, Chinese Pre-trained Unbalanced Transformers (CPT) emerged as an effective solution. What is CPT? CPT is

Chinese Word Segmentation

Chinese Word Segmentation: An Overview Chinese word segmentation is a vital task in natural language processing that involves dividing a sequence of Chinese characters into separate words. The Chinese language does not have spaces between words, which makes this task particularly challenging. The segmentation of text into individual words is an essential process in several NLP applications, such as machine translation, sentiment analysis, text classification, and many others. Successfully segm

Chinese Zero Pronoun Resolution

Chinese zero pronoun resolution is an important aspect of natural language processing for Chinese texts. In the Chinese language, certain pronouns do not have explicit counterparts and are therefore called zero pronouns. These zero pronouns refer to previously mentioned nouns or pronouns and help to maintain coherence and cohesion in the text. It is the task of resolving these zero segments that is known as Chinese zero pronoun resolution. Why Chinese Zero Pronoun Resolution is Important Zero

Circular Smooth Label

Circular Smooth Label (CSL): An Introduction When it comes to object detection in images, there are many algorithms and techniques that can be used. One such method is the Circular Smooth Label (CSL) technique. In this article, we will explore what CSL is and how it is used in object detection. What is CSL? CSL is a rotation detection technique that is used for arbitrary-oriented object detection. In other words, it is a way to detect objects in images that can be rotated at any angle. CSL i

Claim-Evidence Pair Extraction (CEPE)

Introduction to Claim-Evidence Pair Extraction (CEPE) When reading a news article or research paper, you will often come across claims made by the author. Claims are statements or propositions that the author is arguing for or against. In order to support these claims, authors usually provide evidence (facts or data) to back them up. In recent years, there has been an increase in the amount of information available online, making it difficult for people to sift through and find the relevant in

Claim Extraction with Stance Classification (CESC)

Claim Extraction with Stance Classification (CESC) is a technique used in natural language processing to extract claims from articles and determine the stance of the claim towards a specific topic. By identifying sentences with clear stances, the possibility of identifying claims increases, making it easier to extract the claims from the articles. What is Claim Extraction with Stance Classification (CESC)? CESC is an integrated natural language processing technique that combines two subtasks:

ClariNet

ClariNet is a revolutionary text-to-speech architecture that uses an end-to-end approach. It is unlike previous TTS systems as it is fully convolutional and can be trained from scratch. ClariNet uses the WaveNet module which is conditioned on hidden states instead of the traditional mel-spectogram model used in other TTS systems. This new breakthrough in TTS systems is an exciting development for the future of TTS technology. What is ClariNet? ClariNet is an advanced text-to-speech (TTS) arch

Class activation guide

Class Activation Guide (CAG) is an exciting new approach that uses localization information to improve the accuracy of object detection and recognition. This module is designed to work with instrument activation maps, which are generated during the process of training a convolutional neural network (CNN). By using these maps, CAG can guide the recognition of verbs and targets, which increases accuracy and improves the overall speed and efficiency of the CNN. What is CAG? CAG is a method for i

Class Activation Guided Attention Mechanism (CAGAM)

What is Class Activation Guided Attention Mechanism (CAGAM)? Class Activation Guided Attention Mechanism (CAGAM) is a type of spatial attention mechanism that enhances relevant pattern discovery in unknown context features using a known context feature. The known context feature in CAGAM is often a class activation map (CAM). How does CAGAM work? In a nutshell, CAGAM proposes to guide attention from the class activation map (CAM) of a specific class to the unknown context features that contr

Class-activation map

CAM: An Overview In recent years, computer vision has grown exponentially, with machines becoming advanced enough to identify and classify objects through deep learning and neural networks. Consequently, the interpretation of neural network decision making has become a complex task. One such technique to interpret these decisions is CAM, which stands for Class Activation Maps. What is CAM? CAM or Class Activation Maps is a technique that uses Convolutional Neural Networks (CNNs) to visualize

Class-Attention in Image Transformers

What is CaiT? CaiT, short for Class-Attention in Image Transformers, is a type of vision transformer that was designed with enhancements to the original Vision Transformer (ViT) model. Features of CaiT As compared to ViT, CaiT uses a new layer scaling approach called LayerScale. This innovative approach adds a learnable diagonal matrix to the output of each residual block, which is initialized close to but not equal to 0. This added layer enhances the training dynamics. Another feature that

Class Attention

In the field of machine learning, a Class Attention layer or CA layer is a mechanism that is used in vision transformers to extract information from a set of processed patches. It is similar to a self-attention layer, except that it relies on the attention between the class embedding (initialized at CLS in the first CA) and itself plus the set of frozen patch embeddings. What is a Vision Transformer? A Vision Transformer is a type of deep learning model that is designed to process visual data

Class-Incremental Semantic Segmentation

Class-Incremental Semantic Segmentation: What It Is Class-Incremental Semantic Segmentation is a process that involves dividing an image into specific parts, also referred to as segments, and categorizing each segment based on its properties. The process is used in various applications, including autonomous driving, robotics, medical imaging, and computer vision. In traditional segmentation, an image is divided into several segments, and each segment is assigned to a specific class category. Ho

Class-MLP

Class-MLP is a new way for machines to process visual information. It is an alternative to average pooling which is a technique used in machine learning. It's a new adaptation of the class-attention token, first introduced in CaiT. In CaiT, the class token is updated based on the frozen patch embeddings in two layers that resemble the transformer network. In Class-MLP, this same approach is used, but with the addition of a linear layer that aggregates the patches. What is Average Pooling? Bef

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