MelGAN is an exciting development in audio waveform generation using a GAN setup. It is a fully convolutional feed-forward network that takes a mel-spectrogram as input and outputs raw waveform.
What is Mel-spectrogram?
A mel-spectrogram represents the frequency content of a signal at different points in time. In other words, it is a visual representation of sound that shows how much energy is present in a particular frequency band at a particular time. The y-axis of a mel-spectrogram represe
What is MAD Learning?
MAD Learning is a new method of learning that makes use of our brain's ability to remember information in order to make predictions about new information. This is done by inferring from the memorized facts we already know to predict what we want to know. Developed by researchers, MAD Learning is a powerful tool that allows individuals to learn complex information more efficiently than traditional learning methods.
How does MAD Learning work?
When we learn something new,
Understanding Memory Network: Improving Neural Networks with Extended Memory
With the advent of artificial intelligence, neural networks have proved to be extremely useful in various fields such as speech recognition, image classification, and natural language processing. However, most traditional neural networks lack a long-term memory component, which can hinder their performance. Memory Network is a novel architecture that aims to address the limitations of traditional neural networks by usi
Overview of Mesh-TensorFlow
Mesh-TensorFlow is a programming language used to distribute tensor computations. Like data-parallelism that splits tensors and operations along the "batch" dimension, Mesh-TensorFlow can split any dimensions of a multi-dimensional mesh of processors. This allows users to specify the exact dimensions to be split across any dimensions of the mesh of processors.
What is Tensor Computation?
Tensor computation is a concept in which matrices and higher-dimensional arra
Introduction to MeshGraphNet
MeshGraphNet is a framework that helps machines learn about a new form of simulations to produce accurate results. This framework comprises graph neural networks that execute message passing on a mesh graph and adapt the mesh discretization during forward simulation. The MeshGraphNet model is taught using one-step supervision and an Encode-Process-Decode architecture. This model can generate long pathways inferences iteratively. The framework's primary focus is to l
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
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
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
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
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
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
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 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:
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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
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
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
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
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