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Locally-Grouped Self-Attention

A Computation-Friendly Attention Mechanism: Locally-Grouped Self-Attention Locally-Grouped Self-Attention (LSA) is a type of attention mechanism used in the Twins-SVT architecture. The purpose of this mechanism is to reduce the computational cost of self-attention in neural networks. How LSA Works LSA is designed based on the concept of dividing the feature maps of an input image into smaller sub-windows. The image is divided into M x N sub-windows of equal size, and self-attention is applie

Locally Weighted Learning

Understanding Locally Weighted Learning: Definition, Explanations, Examples & Code Locally Weighted Learning (LWL) is an instance-based supervised learning algorithm that uses nearest neighbors for predictions. It applies a weighting function that gives more influence to nearby points, making it useful for non-linear regression problems. Locally Weighted Learning: Introduction Domains Learning Methods Type Machine Learning Supervised Instance-based Locally Weighted Learning, or LW

LocalViT

Understanding LocalViT: Enhancing ViTs through Depthwise Convolutions LocalViT is a new network that aims to improve the modeling capability of ViTs by introducing depthwise convolutions. ViTs, or Vision Transformers, are neural networks used in computer vision tasks like image classification and object detection. However, ViTs have been limited in their ability to model local features. To overcome this issue, LocalViT brings localist mechanisms into transformers by using depthwise convolution

Location-based Attention

Understanding Location-Based Attention Mechanism Location-based attention is an advanced artificial intelligence (AI) mechanism that provides a powerful tool to computers and machines that mimics and elaborates human cognition, in order to achieve a similar level of precision and responsiveness. Location-based attention aims to help computers and machines to understand the context and make informed decisions based on the geographical location of certain events, landmarks, or groups of people.1

Location Sensitive Attention

Location Sensitive Attention: An Overview Location Sensitive Attention is a mechanism that extends the additive attention mechanism to use cumulative attention weights from previous decoder time steps as an additional feature. This allows the model to move forward consistently through the input, mitigating potential failure modes where some subsequences are repeated or ignored by the decoder. The attention mechanism is a critical component of sequence-to-sequence models, enabling the model to

Log-time and Log-space Extreme Classification

LTLS is a powerful technique used for multilabel and multiclass prediction. This method can perform training and inference in logarithmic time and space. Although it may sound complex, it is a strategy used to solve extreme multi-class classification problems, particularly those with an extensive output space. What is LTLS? LTLS stands for Logarithmic Time and Space Learning. With the ability to embed extensive classification problems with simple structured prediction problems, LTLS employs e

LOGAN

The topic of LOGAN pertains to the use of deep learning techniques to generate high-quality images. Specifically, LOGAN is a generative adversarial network that uses a latent optimization approach called natural gradient descent (NGD). What is NGD? NGD stands for natural gradient descent, which is an optimization algorithm used in deep learning. Natural gradient descent takes into account the geometry of the loss function, which can make optimization more efficient. This algorithm uses the Fi

Logical Reasoning Question Answering

In recent years, there has been a growing interest in developing artificial intelligence (AI) models that can understand and answer natural language questions. However, these models often struggle with questions that require logical reasoning, such as those that involve quantifiers like "all" and "some," or those that involve complex relationships between different entities. What is Logical Reasoning? Logical reasoning refers to the ability to think critically and systematically in order to s

Logical Reasoning Reading Comprehension

Logical Reasoning Reading Comprehension: Improving Machine Comprehension Skills Logical reasoning reading comprehension is an important task that measures the level of logical reasoning skills for machine reading comprehension. A dataset named ReClor (ICLR 2020) was proposed to evaluate the logical reasoning ability of the machine reading comprehension models. The dataset helps to improve the comprehension performance of the machine models by evaluating their ability to read and retain informat

Logistic Regression

What is Logistic Regression? Logistic Regression is a statistical method used for binary classification. This means that it allows us to predict one of two possible outcomes based on a set of input variables. It is similar to linear regression, but instead of predicting a continuous output value, logistic regression predicts the probability of a certain outcome. Despite its name, logistic regression is not used for regression, but rather for classification. It is a popular algorithm in the fie

Long Form Question Answering

What is Long Form Question Answering? Long form question answering is a type of natural language processing that involves answering open-ended questions with elaborate and in-depth responses. This can include questions about history, science, literature, and other topics. The goal of long form question answering is to provide accurate and informative responses that are able to meet the needs of the person asking the question. Why is Long Form Question Answering Important? Long form question

Long-range modeling

Overview of Long-Range Modeling Long-range modeling is a process that involves the use of language models to generate predictions or outputs over long sequences of text. This technique is widely used in the field of natural language processing (NLP) and has various applications, including language translation, text summarization, and speech recognition. The primary goal of long-range modeling is to improve the performance of language models when dealing with long texts, which can range from hu

Long Short-Term Memory Network

Understanding Long Short-Term Memory Network: Definition, Explanations, Examples & Code The Long Short-Term Memory Network (LSTM) is a type of deep learning algorithm capable of learning order dependence in sequence prediction problems. As a type of recurrent neural network, LSTM is particularly useful in tasks that require the model to remember and selectively forget information over an extended period. LSTM is trained using supervised learning methods and is useful in a wide range of natural

Long Short-Term Memory

Long Short-Term Memory (LSTM) is a type of recurrent neural network used in artificial intelligence technology. It helps to solve the vanishing gradient problem that RNN (Recurrent Neural Network) encounters due to the shallow learning model. The vanishing gradient problem occurs when the gradient diminishes too quickly as it passes through multiple layers of a neural network, causing the weights of the first few layers to remain unchanged. LSTM solves this problem by adding extra cells and inpu

Longformer

Introduction to Longformer Longformer is an advanced artificial intelligence (AI) architecture designed using the Transformer technology. It is designed to process long sequences of text, which is something traditional Transformer models struggle with. Due to their self-attention operation, traditional Transformers have a quadratic scaling with the length of a sequence. In contrast, the Longformer replaces this operation with one that scales linearly, making it an ideal tool for processing thou

Lookahead

Overview of Lookahead Optimizer Lookahead is a type of optimizer used in machine learning that helps to improve model training by updating two sets of weights, the "fast" and "slow" weights, in each iteration. This method is probabilistic, meaning there is some randomness involved in the process. However, it has been shown to produce models that perform better than those generated by other optimization techniques. How Lookahead Works The algorithm for Lookahead optimizer is relatively simple

Loop Closure Detection

Loop Closure Detection: Detecting Previously Visited Locations Loop closure detection is a technique used in robotics and computer vision to detect whether an agent, such as a robot or a camera, has returned to a previously visited location. This process is essential in many applications, such as robot navigation, autonomous driving, and augmented reality. Why Loop Closure Detection is Important Loop closure detection is important because it allows agents to accurately estimate their positio

Lovasz-Softmax

Lovasz-Softmax: An Overview The Lovasz-Softmax loss is a special case of the Lovasz extension and has become particularly popular in the neural network community as an effective loss function for multiclass semantic segmentation tasks. It was introduced by Berman et al. in 2018 and has since been used in various computer vision applications like image segmentation, image classification, and object detection. The Need for a New Loss Function In the realm of computer vision, neural networks ar

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2D Parallel Distributed Methods 3D Face Mesh Models 3D Object Detection Models 3D Reconstruction 3D Representations 6D Pose Estimation Models Action Recognition Blocks Action Recognition Models Activation Functions Active Learning Actor-Critic Algorithms Adaptive Computation Adversarial Adversarial Attacks Adversarial Image Data Augmentation Adversarial Training Affinity Functions AI Adult Chatbots AI Advertising Software AI Algorithm AI App Builders AI Art Generator AI Art Generator Anime AI Art Generator Free AI Art Generator From Text AI Art Tools AI Article Writing Tools AI Assistants AI Automation AI Automation Tools AI Blog Content Writing Tools AI Brain Training AI Calendar Assistants AI Character Generators AI Chatbot AI Chatbots Free AI Coding Tools AI Collaboration Platform AI Colorization Tools AI Content Detection Tools AI Content Marketing Tools AI Copywriting Software Free AI Copywriting Tools AI Design Software AI Developer Tools AI Devices AI Ecommerce Tools AI Email 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Dependency Parsers Deraining Models Detection Assignment Rules Dialog Adaptation Dialog System Evaluation Dialogue State Trackers Dimensionality Reduction Discriminators Distillation Distributed Communication Distributed Methods Distributed Reinforcement Learning Distribution Approximation Distributions Document Embeddings Document Summary Evaluation Document Understanding Models Domain Adaptation Downsampling E-signing Efficient Planning Eligibility Traces Ensembling Entity Recognition Models Entity Retrieval Models Environment Design Methods Exaggeration Detection Models Expense Trackers Explainable CNNs Exploration Strategies Face Privacy Face Recognition Models Face Restoration Models Face-to-Face Translation Factorization Machines Feature Extractors Feature Matching Feature Pyramid Blocks Feature Upsampling Feedforward Networks Few-Shot Image-to-Image Translation Fine-Tuning Font Generation Models Fourier-related Transforms Free AI Tools Free Subscription Trackers Gated Linear 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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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Replicated Data Parallel Representation Learning Reversible Image Conversion Models RGB-D Saliency Detection Models RL Transformers Robotic Manipulation Models Robots Robust Training Robustness Methods RoI Feature Extractors Rule-based systems Rule Learners Sample Re-Weighting Scene Text Models scikit-learn Scikit-learn Courses Self-Supervised Learning Self-Training Methods Semantic Segmentation Models Semantic Segmentation Modules Semi-supervised Learning Semi-Supervised Learning Methods Sentence Embeddings Sequence Decoding Methods Sequence Editing Models Sequence To Sequence Models Sequential Blocks Sharded Data Parallel Methods Skip Connection Blocks Skip Connections SLAM Methods Span Representations Sparsetral Sparsity Speaker Diarization Speech Speech Embeddings Speech enhancement Speech Recognition Speech Separation Models Speech Synthesis Blocks Spreadsheet Formula Prediction Models State Similarity Metrics Static Word Embeddings Stereo Depth Estimation Models Stochastic 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Inpainting Models Video Instance Segmentation Models Video Interpolation Models Video Model Blocks Video Object Segmentation Models Video Panoptic Segmentation Models Video Recognition Models Video Super-Resolution Models Video-Text Retrieval Models Vision and Language Pre-Trained Models Vision Transformers VQA Models Webpage Object Detection Pipeline Website Monitoring Whitening Word Embeddings Working Memory Models