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Data augmentation using Polya-Gamma latent variables.

Overview of Polya-Gamma Augmentation If you've ever studied Bayesian inference, then you know that it can be quite complex. One of the most difficult tasks in Bayesian inference is finding the full-conditional distributions of posterior distributions in sampling algorithms like Markov chain Monte Carlo (MCMC). Luckily, there is a method called Polya-Gamma augmentation that can help simplify this task. In this article, we will discuss the basics of Polya-Gamma augmentation, how it is applied in

Data-efficient Image Transformer

What is DeiT? DeiT stands for Data-Efficient Image Transformer. It is a type of Vision Transformer, which is a machine learning model used for image classification tasks. The DeiT model is designed specifically to train using a teacher-student strategy that relies on a distillation token. This token ensures that the student learns from the teacher through attention. How does DeiT Work? The DeiT model works by using a teacher-student strategy that relies on attention. The teacher is a larger,

Data Mining

Data mining is a fascinating process that involves discovering patterns and useful information from large sets of data. It is an essential technique used in industries such as finance, retail, healthcare, and telecommunications to make informed decisions and improve business operations. What is Data Mining? Data mining is a process of extracting insights and knowledge from vast amounts of data. It involves using various methods, including statistical techniques, machine learning, and artifici

Data-to-Text Generation

Data-to-Text Generation: A Comprehensive Overview Introduction: Data-to-Text Generation is a challenging task in natural language understanding and generation that involves the conversion of structured data into fluently described text. In this form of NLG, the system takes input data, such as a table, and produces unambiguous and logically coherent text that adequately describes the data as output. Data-to-Text Generation is widely used in various fields, from assisting visually impaired peo

DBlock

Understanding DBlock in GAN-TTS Architecture DBlock is a specialized residual block that is utilized in the discriminator phase of the GAN-TTS architecture. This technique is similar to GBlocks used in the generation phase, however, DBlock does not integrate batch normalization in its implementation. What is GAN-TTS Architecture? Before diving into the dynamics of DBlock and its functions, let's understand what GAN-TTS architecture is. GAN-TTS stands for Generative Adversarial Network - Text

DCN-V2

What is DCN-V2? DCN-V2 is a type of architecture that is used in learning-to-rank. It is an improvement over the original DCN model. The main idea behind DCN-V2 is to learn explicit feature interactions through cross layers and combine them with a deep network to learn other implicit interactions. This architecture is capable of learning bounded-degree cross features. How Does DCN-V2 Work? The architecture of DCN-V2 involves two important components: explicit and implicit feature interaction

De-aliasing

What is De-aliasing? De-aliasing is a problem that arises while acquiring images. Sometimes, while acquiring an image, high-frequency information is lost, or rather, the original information gets distorted, and the image gets overexposed. So, the task of recovering the original high-frequency information and improving the image quality is known as de-aliasing. The technique is very popular in numerous areas, including computer vision, medical imaging, remote sensing, and many more. Why do we

DE-GAN: A Conditional Generative Adversarial Network for Document Enhancement

What is DE-GAN and How Does it Work? DE-GAN, or Document Enhancement Generative Adversarial Networks, is an end-to-end framework that uses conditional GANs to restore severely degraded document images. Document degradation can occur due to various factors such as old age of a document, water damage, or poor quality scans which make it difficult to read and process with OCR (Optical Character Recognition) technology. DE-GAN uses a deep neural network system to restore the degraded document image

Deactivable Skip Connection

Deactivable Skip Connection Explained What is a Skip Connection? In the field of computer vision, Skip Connections have been an important aspect of various image segmentation models. They help the models to bypass certain convolutional layers and create a shortcut between the input and output layers. This helps to reduce the complexity of the model and also accelerates the training process. Without this skip connection, the deep neural networks may fail to improve beyond a certain point. St

DeBERTa

DeBERTa is an advanced neural language model that aims to improve upon the popular BERT and RoBERTa models. It achieves this through the use of two innovative techniques: a disentangled attention mechanism and an enhanced mask decoder. Disentangled Attention Mechanism The disentangled attention mechanism is where each word is represented using two vectors that encode its content and position, respectively. This allows the attention weights among words to be computed using disentangle matrices

Deblurring

Deblurring is a process used in computer vision to restore the original, sharp content of images or videos by removing blurring artifacts. Blurring can be caused by several factors, including camera shake, fast motion, and out-of-focus objects, leading to a loss of detail and quality in the captured images. The goal of deblurring is to produce a clear, high-quality image that accurately represents the original scene. Understanding the Importance of Deblurring Blurring can have a significant i

Decentralized Distributed Proximal Policy Optimization

What is DD-PPO? Decentralized Distributed Proximal Policy Optimization, commonly referred to as DD-PPO, is a method for distributed reinforcement learning in resource-intensive simulated environments. It is a policy gradient method for reinforcement learning that can be used with synchronous distribution. It is a distributed mechanism that has the potential to scale very well therefore making implementations very simple. Proximal Policy Optimization (PPO) Proximal Policy Optimization or PPO

Decision Stump

Understanding Decision Stump: Definition, Explanations, Examples & Code The Decision Stump is a type of Decision Tree algorithm used in Supervised Learning. It is a one-level decision tree that is often used as a base classifier in many ensemble methods. Decision Stump: Introduction Domains Learning Methods Type Machine Learning Supervised Decision Tree Decision Stump is a type of decision tree used in supervised learning. It is a one-level decision tree that acts as a base classi

DeCLUTR

What is DeCLUTR? DeCLUTR is an innovative approach to learning universal sentence embeddings without the need for labeled training data. By utilizing a self-supervised objective, DeCLUTR can generate embeddings that represent the meaning of a sentence. These embeddings can then be used in many different natural language processing tasks such as machine translation or text classification. How Does DeCLUTR Work? DeCLUTR works by training an encoder to minimize the distance between embeddings o

DeeBERT

DeeBERT: A Gamechanger for NLP DeeBERT is a method for accelerating BERT inference, which has revolutionized the field of Natural Language Processing (NLP). Named after the famous Sesame Street character Bert, Bidirectional Encoder Representations from Transformers (BERT) is a powerful algorithm that has improved the performance of various NLP tasks. To understand the significance of DeeBERT, let's first understand how BERT works. BERT is a deep neural network that is trained on massive amount

Deep Belief Network

Understanding Deep Belief Networks (DBN) Deep Belief Networks (DBN) are a type of multi-layer generative graphical models that are heavily used in the field of deep learning. Machines have been able to learn over time, and deep learning is based on the concept of the structure of the brain, making it possible for technology to recognize patterns on its own. To understand DBN, it is essential to understand some key concepts. First, graphical models are representations of probability distributio

Deep Belief Networks

Understanding Deep Belief Networks: Definition, Explanations, Examples & Code Deep Belief Networks (DBN) is a type of deep learning algorithm that is widely used in artificial intelligence and machine learning. It is a generative graphical model with many layers of hidden causal variables, designed for unsupervised learning tasks. DBN is capable of learning rich and complex representations of data, making it well-suited for a variety of tasks in the field of AI. Deep Belief Networks: Introduc

Deep Boltzmann Machine

A Deep Boltzmann Machine (DBM) is a type of generative model used in deep learning. It is similar to a Deep Belief Network, but with some differences in structure and function. The specific structure of a DBM involves three layers, rather than the two or more commonly used in other networks. Structure and Function of a DBM The three layers of a DBM consist of an input layer, one or more hidden layers, and an output layer. The hidden layers are the main focus of a DBM, and the bidirectional co

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Detection Models Mean Shift Clustering Medical Medical Image Models Medical waveform analysis Mesh-Based Simulation Models Meshing Meta-Learning Algorithms Methodology Miscellaneous Miscellaneous Components Mixture-of-Experts Model Compression Model Parallel Methods Momentum Rules Monocular Depth Estimation Models Motion Control Motion Prediction Models Multi-Modal Methods Multi-Object Tracking Models Multi-Scale Training Music Music source separation Music Transcription Natural Language Processing Natural Language Processing Courses Negative Sampling Network Shrinking Neural Architecture Search Neural Networks Neural Networks Courses Neural Search No Code AI No Code AI App Builders No Code Courses No Code Tools Non-Parametric Classification Non-Parametric Regression Normalization Numpy Courses Object Detection Models Object Detection Modules OCR Models Off-Policy TD Control Offline Reinforcement Learning Methods On-Policy TD Control One-Stage Object Detection Models Open-Domain 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