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Multiplicative Attention

Multiplicative Attention is a technique used in neural networks to align source and target words. It calculates an alignment score function which is faster and more preferred in practice because it can be implemented efficiently using matrix multiplication. The technique can also be used to determine the correlation between source and target words by using a matrix. The final scores are calculated using a softmax which ensures that the sum of the alignment scores is equal to one. What is Multi

Multiplicative LSTM

The Multiplicative LSTM (mLSTM) is a neural network architecture used for sequence modelling, combining the power of the long short-term memory (LSTM) and multiplicative recurrent neural network (mRNN) architectures. These two models have been combined by adding connections from the mRNN's intermediate state to each gating unit in the LSTM. This creates an architecture that is more efficient while still being accurate in predicting sequences. What is an LSTM? An LSTM is a type of neural netwo

Multiplicative RNN

A multiplicative RNN (mRNN) is a type of recurrent neural network that uses multiplicative connections to allow the current input to affect the hidden state dynamics by determining the entire hidden-to-hidden matrix, in addition to providing an additive bias. What is an RNN? Before diving into what an mRNN is, it is important to understand Recurrent Neural Networks (RNNs). RNNs are a type of neural network that is useful for processing sequential data. Unlike other types of neural networks th

Multiscale Attention ViT with Late fusion

What is MAVL? MAVL stands for Multiscale Attention ViT with Late fusion. It is a multi-modal neural network that is trained to detect objects using human understandable natural language text queries. The network uses multiple image features and deforms the convolution for late multi-modal fusion. What does MAVL do? MAVL is a class-agnostic object detector that can be used to identify objects in an image. It uses natural language text queries, such as "all objects" or "all entities," to detec

Multiscale Dilated Convolution Block

The Multiscale Dilated Convolution Block is a powerful tool used in deep learning for image recognition. It is motivated by the idea that image features occur at various scales and that a network's ability to express itself is directly related to its range of functions and total number of parameters. This block enables the network to simultaneously learn various features and the relevant scales at which those features occur with a minimal increase in parameters. Multiscale Dilated Convolution

Multiscale Vision Transformer

Multiscale Vision Transformer (MViT): A Breakthrough in Modeling Visual Data Recently, the field of computer vision has witnessed a tremendous development in deep learning techniques, which have brought remarkable improvements in various tasks such as object detection, segmentation, and classification. One of the most significant breakthroughs is the introduction of the transformer architecture, which has shown remarkable performance in natural language processing tasks. The transformer archite

Multivariate Adaptive Regression Splines

Understanding Multivariate Adaptive Regression Splines: Definition, Explanations, Examples & Code Multivariate Adaptive Regression Splines (MARS) is a regression analysis algorithm that models complex data by piecing together simpler functions. It falls under the category of supervised learning methods and is commonly used for predictive modeling and data analysis. Multivariate Adaptive Regression Splines: Introduction Domains Learning Methods Type Machine Learning Supervised Regressi

Multiview Contextual Commonsense Inference

When it comes to understanding different situations, it's important to consider multiple perspectives and potential outcomes. This requires the use of commonsense reasoning to identify valid inferences. Multiview Contextual Commonsense Inference is the task of identifying all possible inferences based on a given context. What is Multiview Contextual Commonsense Inference? Multiview Contextual Commonsense Inference is a process that involves reasoning about a situation from multiple perspectiv

MushroomRL

MushroomRL is a library designed to make it easier for software developers to implement and run experiments in a field known as Reinforcement Learning, or “RL” for short. Reinforcement Learning is a type of machine learning that trains algorithms to learn from experience in order to perform tasks. Although RL is a powerful technique, it can be difficult to implement and experiment with different algorithms. MushroomRL simplifies this process by providing all the necessary components in one simpl

Music Source Separation

Music source separation is a process that allows for the isolation of different parts of music, such as vocals, bass, and drums, from a mixed audio signal. This technique is used in a variety of fields including music production, audio restoration, and speech recognition. The goal of music source separation is to provide a more detailed and customizable audio mixing experience, allowing music producers and audio engineers to adjust individual elements of a song to create a more polished and refi

MUSIQ

What is MUSIQ? MUSIQ, short for Multi-scale Image Quality Transformer, is a model used for multi-scale image quality assessment. It can process images of varying sizes and aspect ratios while maintaining their native resolution. How does MUSIQ work? MUSIQ constructs a multi-scale image input representation that includes the native resolution image and its ARP resized variants. Each image is split into fixed-size patches that are embedded by a patch encoding module. To handle images with vary

MuVER

What is MuVER? MuVER stands for Multi-View Entity Representations, which is an advanced approach for entity retrieval. In other words, it helps match a word or phrase to the appropriate entity by comparing it with descriptions of different entities. For example, if you were searching for information about Kobe Bryant, MuVER would help match your search query to the appropriate Kobe Bryant, rather than bringing up information about a different person with the same name. How Does MuVER Work?

MuZero

If you are interested in artificial intelligence and reinforcement learning, then you have probably heard of MuZero. It is one of the latest models for learning decision-making procedures in a range of contexts, including simple games, difficult board games like Go, and even arcade games. MuZero was introduced in December 2019, as a successor to DeepMind's earlier model-based success, AlphaZero. MuZero builds upon AlphaZero's search and search-based policy iteration algorithms, but with the adde

MyGym: Modular Toolkit for Visuomotor Robotic Tasks

Introducing myGym: A Tool for Fast Prototyping of Neural Networks in Robotic Manipulation and Navigation myGym is a toolkit designed to aid in the development and rapid prototyping of neural networks in the field of robotic manipulation and navigation. The modular design of the toolkit means that it can be adapted to different robots, environments, and tasks, making it a versatile tool for machine learning researchers. Features of myGym The features of myGym include pre-trained neural networ

N-step Returns

Understanding N-Step Returns in Reinforcement Learning Reinforcement learning is about teaching machines to learn and improve how they perform certain tasks. One of the techniques used in reinforcement learning is the use of value functions. Value functions help algorithms determine the best actions to take for each state in a particular environment. Value functions are estimates of how good a specific state or action is for a machine or agent. However, estimating value functions is often chall

NADAM

NADAM: A Powerful Optimization Algorithm for Machine Learning Machine learning is a field of computer science that focuses on creating algorithms that can learn from and make predictions on data. One of the most important aspects of machine learning is optimization, which involves finding the best set of parameters for a given model that minimize the error on a dataset. To achieve this, various optimization algorithms have been developed over the years. One of the most popular and effective is

Naive Bayes

Understanding Naive Bayes: Definition, Explanations, Examples & Code Naive Bayes is a Bayesian algorithm used in supervised learning to classify data. It is a simple probabilistic classifier that applies Bayes' theorem with strong independence assumptions between the features. Naive Bayes: Introduction Domains Learning Methods Type Machine Learning Supervised Bayesian Naive Bayes is a popular algorithm used in machine learning for classification tasks. It is a simple probabilistic

NAS-FCOS

NAS-FCOS: An Overview of the State-of-the-Art Object Detection Method Object detection is a computer vision task that involves locating and identifying objects within an image. Recently, NAS-FCOS has emerged as a state-of-the-art object detection method, which makes use of two subnetworks: FPN and set of prediction heads. The focus of this article is to provide an overview of NAS-FCOS and how it is used to detect objects within images. Understanding the Two Subnetworks of NAS-FCOS The two su

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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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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