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Message Passing Neural Network

Message Passing Neural Networks, commonly abbreviated as MPNN, is a type of neural network framework that is used for machine learning on graph data. MPNN can be applied to undirected graphs with node features and edge features. This approach can also be extended to directed multigraphs as well. Two Phases of MPNN The MPNN framework operates in two phases: message passing phase and readout phase. During message passing phase, the hidden states of all nodes in the graph are updated based on me

Meta-augmentation

What is Meta-Augmentation? Meta-Augmentation is a technique used in machine learning to generate more varied tasks for a single example in meta-learning. This technique differs from data augmentation in classical machine learning, which generates more varied examples within a single task. The aim of Meta-augmentation is to generate more varied tasks for a single example, which is used to force the learner to quickly learn a new task from feedback. The Importance of Meta-Augmentation Meta-Aug

Meta Face Recognition

Understanding Meta Face Recognition (MFR) If you've ever used facial recognition software, you've likely noticed that it's not always perfect. The technology can struggle to identify people in certain situations, like when lighting conditions aren't ideal or when the person is wearing a disguise. This is where Meta Face Recognition (MFR) comes in. MFR is a method of facial recognition that uses a process called meta-learning. Essentially, this means that the technology is able to dynamically a

Meta Pseudo Labels

Understanding Meta Pseudo Labels Meta Pseudo Labels is a semi-supervised learning method that can help train machine learning models. In simple terms, it is a technique that uses a teacher network to generate pseudo-labels for unlabeled data to teach a student network. Basically, it is a way to teach a machine learning algorithm without having humans manually label all of the data. The Role of Teacher and Student Networks In order to understand how Meta Pseudo Labels work, it is necessary to

Meta Reward Learning

What is MeRL? Meta Reward Learning (MeRL) is an advanced machine learning technique that allows agents to learn from sparse and underspecified rewards. In simple terms, it is a method for training robots, virtual assistants, and other AI agents to perform complex tasks with minimal guidance. The main challenge that MeRL seeks to overcome is the problem of "spurious trajectories and programs." Essentially, when an agent is only given binary feedback, it may learn to achieve successful outcomes

MetaFormer

In the world of computer science and technology, MetaFormer is a buzzword that has been gaining popularity lately. So, what exactly is MetaFormer? It is a general architecture that is abstracted from Transformers by not specifying the token mixer. What is Transformers? If you are not familiar with Transformers, it is a neural network architecture that has been widely used in natural language processing (NLP) tasks, such as language translation, text generation, and sentiment analysis. One of

Metric mixup

In the world of deep learning, accuracy is essential. One way to improve accuracy is by using Metrix, a powerful technique that allows for the representation and interpolation of labels. Metrix is useful for deep metric learning and can work with a wide range of loss functions. What is Metrix? Metrix is an innovative technique that facilitates deep metric learning. Essentially, it allows labels to be represented in a more generic manner, which makes it easier to extend various kinds of mixup.

Metropolis Hastings

Metropolis-Hastings is an important algorithm for approximate inference in statistics. It is a Markov Chain Monte Carlo (MCMC) algorithm that allows for sampling from a probability distribution where direct sampling is difficult due to the presence of an intractable integral. How Metropolis-Hastings works Metropolis-Hastings consists of a proposal distribution to draw a parameter value. This is denoted as q(θ’|θ). To decide whether θ’ is accepted or rejected, we then calculate a ratio of: $$

MEUZZ

What is MEUZZ? MEUZZ is a machine learning-based hybrid fuzzer that uses supervised machine learning to determine adaptive and generalizable seed scheduling in determining the yields of hybrid fuzzing. It determines which new seeds are likely to produce better fuzzing yields based on the knowledge learned from past seed scheduling decisions made on the same or similar programs. MEUZZ uses a series of features extracted via code reachability and dynamic analysis to establish its learning, which

Micro-Expression Recognition

Facial Micro-Expression Recognition: Understanding the Subtle Language of Emotion Facial micro-expression recognition, also known as micro-expressions, is the science of analyzing very brief and fleeting facial expressions, usually lasting no more than 1/25th of a second, to understand the subtle language of emotion. This technology has become increasingly popular in scientific research, security, recruitment, and clinical practices, as it allows professionals to see facial expressions that the

Micro-Expression Spotting

Facial Micro-Expression Spotting: What is it? Facial Micro-Expression Spotting is the process of identifying short and quick facial expressions that occur on a person's face. These expressions can last for fractions of a second and are often unconscious, meaning the person displaying the expression may not even know they are doing it. Micro-expressions can provide important clues about a person's emotions and intent. They can reveal a person's true feelings, even when they are attempting to hi

MinCut Pooling

MinCutPool Overview If you're interested in computer science, you might have heard of MinCutPool. It's a fancy way of saying a trainable pooling operator for graphs. Confused? Don't worry, we'll break it down for you. Essentially, MinCutPool is a tool that takes a graph and learns to group nodes into clusters. What is a Graph? Before we dive into MinCutPool, let's make sure we understand what a graph is. A graph is a collection of nodes (sometimes called vertices) and edges. Each edge connec

Mini-Batch Gradient Descent

Understanding Mini-Batch Gradient Descent: Definition, Explanations, Examples & Code Mini-Batch Gradient Descent is an optimization algorithm used in the field of machine learning. It is a variation of the gradient descent algorithm that splits the training dataset into small batches. These batches are then used to calculate the error of the model and update its coefficients. Mini-Batch Gradient Descent is used to minimize the cost function of a model and is a commonly used algorithm in deep le

Minibatch Discrimination

Minibatch Discrimination is a technique used in generative adversarial networks (GANs) to better differentiate between whole minibatches of samples instead of individual ones. This approach helps to prevent the 'collapse' of the generator, which can happen when the generator produces very similar outputs, minimizing the variance of the model. What is a GAN? Before we dive into what minibatch discrimination is, it is essential to understand what a generative adversarial network (GAN) is. A GAN

Minimum Description Length

Minimum Description Length (MDL) is a principle for selecting models without assuming that the data is from a perfect distribution. Models are used to understand real-world phenomena, but there is no guarantee that any given model is "true" or the most effective model for every situation. MDL provides a standard for choosing models that are the best fit for a given set of data, regardless of their complexity. The History of MDL The idea of MDL dates back to the 1970s, when Jorma Rissanen, a F

Mirror-BERT

Introduction to Mirror-BERT: A Simple Yet Effective Text Encoder Language is the primary tool humans use to communicate, and it is not surprising that advancements in technology have led to great strides in natural language processing. Pretrained language models like BERT (Bidirectional Encoder Representations from Transformers) have been widely adopted and used to improve language-related tasks like language translation, sentiment analysis, and text classification. However, converting such mod

Mirror Descent Policy Optimization

Overview of MDPO: A Trust-Region Method for Reinforcement Learning If you are interested in reinforcement learning, you have probably heard about the Mirror Descent Policy Optimization (MDPO) algorithm. MDPO is a policy gradient algorithm based on the trust-region method that iteratively solves a problem that minimizes a sum of two terms: a linearization of the standard reinforcement learning objective function and a proximity function that restricts two consecutive updates to be close to each

Mish

When it comes to neural networks, activation functions are a fundamental component. They are responsible for determining whether a neuron should be activated or not based on the input signals. One such activation function is called Mish. What is Mish? Mish is a newly proposed activation function that was introduced in a 2019 research paper. It stands for "Mish - A Self-Regularized Non-Monotonic Neural Activation Function" and it is defined by the following formula: $$ f\left(x\right) = x\cdo

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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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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