|
Mixture Of Experts
Mixture of experts (MoE) refers to a machine learning technique where multiple expert networks (learners) are used to divide a problem space into homogeneous regions. It differs from ensemble techniques in that typically only a few, or 1, expert model will be run, rather than combining results from all models. An example from computer vision is combining one neural network model for human detection with another for pose estimation. Hierarchical mixture If the output is conditioned on multiple levels of (probabilistic) gating functions, the mixture is called a hierarchical mixture of experts. A gating network decides which expert to use for each input region. Learning thus consists of learning the parameters of: * individual learners and * gating network. Applications Meta uses MoE in its NLLB-200 system. It uses multiple MoE models that share capacity for use by low-resource language models A language model is a probability distribution over sequences of words. Given ... [...More Info...] [...Related Items...] OR: [Wikipedia] [Google] [Baidu] |
Machine Learning
Machine learning (ML) is a field of inquiry devoted to understanding and building methods that 'learn', that is, methods that leverage data to improve performance on some set of tasks. It is seen as a part of artificial intelligence. Machine learning algorithms build a model based on sample data, known as training data, in order to make predictions or decisions without being explicitly programmed to do so. Machine learning algorithms are used in a wide variety of applications, such as in medicine, email filtering, speech recognition, agriculture, and computer vision, where it is difficult or unfeasible to develop conventional algorithms to perform the needed tasks.Hu, J.; Niu, H.; Carrasco, J.; Lennox, B.; Arvin, F.,Voronoi-Based Multi-Robot Autonomous Exploration in Unknown Environments via Deep Reinforcement Learning IEEE Transactions on Vehicular Technology, 2020. A subset of machine learning is closely related to computational statistics, which focuses on making predicti ... [...More Info...] [...Related Items...] OR: [Wikipedia] [Google] [Baidu] |
Ensemble Learning
In statistics and machine learning, ensemble methods use multiple learning algorithms to obtain better predictive performance than could be obtained from any of the constituent learning algorithms alone. Unlike a statistical ensemble in statistical mechanics, which is usually infinite, a machine learning ensemble consists of only a concrete finite set of alternative models, but typically allows for much more flexible structure to exist among those alternatives. Overview Supervised learning algorithms perform the task of searching through a hypothesis space to find a suitable hypothesis that will make good predictions with a particular problem. Even if the hypothesis space contains hypotheses that are very well-suited for a particular problem, it may be very difficult to find a good one. Ensembles combine multiple hypotheses to form a (hopefully) better hypothesis. The term ''ensemble'' is usually reserved for methods that generate multiple hypotheses using the same base learne ... [...More Info...] [...Related Items...] OR: [Wikipedia] [Google] [Baidu] |
Computer Vision
Computer vision is an interdisciplinary scientific field that deals with how computers can gain high-level understanding from digital images or videos. From the perspective of engineering, it seeks to understand and automate tasks that the human visual system can do. Computer vision tasks include methods for acquiring, processing, analyzing and understanding digital images, and extraction of high-dimensional data from the real world in order to produce numerical or symbolic information, e.g. in the forms of decisions. Understanding in this context means the transformation of visual images (the input of the retina) into descriptions of the world that make sense to thought processes and can elicit appropriate action. This image understanding can be seen as the disentangling of symbolic information from image data using models constructed with the aid of geometry, physics, statistics, and learning theory. The scientific discipline of computer vision is concerned with the theory ... [...More Info...] [...Related Items...] OR: [Wikipedia] [Google] [Baidu] |
Neural Network
A neural network is a network or circuit of biological neurons, or, in a modern sense, an artificial neural network, composed of artificial neurons or nodes. Thus, a neural network is either a biological neural network, made up of biological neurons, or an artificial neural network, used for solving artificial intelligence (AI) problems. The connections of the biological neuron are modeled in artificial neural networks as weights between nodes. A positive weight reflects an excitatory connection, while negative values mean inhibitory connections. All inputs are modified by a weight and summed. This activity is referred to as a linear combination. Finally, an activation function controls the amplitude of the output. For example, an acceptable range of output is usually between 0 and 1, or it could be −1 and 1. These artificial networks may be used for predictive modeling, adaptive control and applications where they can be trained via a dataset. Self-learning resulting from e ... [...More Info...] [...Related Items...] OR: [Wikipedia] [Google] [Baidu] |
Pose Estimation
3D pose estimation is a process of predicting the transformation of an object from a user-defined reference pose, given an image or a 3D scan. It arises in computer vision or robotics where the pose or transformation of an object can be used for alignment of a computer-aided design models, identification, grasping, or manipulation of the object. The image data from which the pose of an object is determined can be either a single image, a stereo image pair, or an image sequence where, typically, the camera is moving with a known velocity. The objects which are considered can be rather general, including a living being or body parts, e.g., a head or hands. The methods which are used for determining the pose of an object, however, are usually specific for a class of objects and cannot generally be expected to work well for other types of objects. From an uncalibrated 2D camera It is possible to estimate the 3D rotation and translation of a 3D object from a single 2D photo, if a ... [...More Info...] [...Related Items...] OR: [Wikipedia] [Google] [Baidu] |
Meta Platforms
Meta Platforms, Inc., (file no. 3835815) trade name, doing business as Meta and formerly named Facebook, Inc., and TheFacebook, Inc., is an American multinational technology conglomerate based in Menlo Park, California. The company owns Facebook, Instagram, and WhatsApp, among other products and services. Meta was once one of the List of public corporations by market capitalization, world's most valuable companies, but as of 2022 is not one of the top twenty biggest companies in the United States. It is considered one of the Big Tech, Big Five American information technology companies, alongside Alphabet Inc., Alphabet, Amazon (company), Amazon, Apple Inc., Apple, and Microsoft. As of 2022, it is the least profitable of the five. Meta's products and services include Facebook, Messenger (software), Messenger, Facebook Watch, and Meta Portal. It has also acquired Oculus (brand), Oculus, Giphy, Mapillary, Kustomer, Presize and has a 9.99% stake in Jio Platforms. In 2021, the compa ... [...More Info...] [...Related Items...] OR: [Wikipedia] [Google] [Baidu] |
Language Models
A language model is a probability distribution over sequences of words. Given any sequence of words of length , a language model assigns a probability P(w_1,\ldots,w_m) to the whole sequence. Language models generate probabilities by training on text corpora in one or many languages. Given that languages can be used to express an infinite variety of valid sentences (the property of digital infinity), language modeling faces the problem of assigning non-zero probabilities to linguistically valid sequences that may never be encountered in the training data. Several modelling approaches have been designed to surmount this problem, such as applying the Markov assumption or using neural architectures such as recurrent neural networks or transformers. Language models are useful for a variety of problems in computational linguistics; from initial applications in speech recognition to ensure nonsensical (i.e. low-probability) word sequences are not predicted, to wider use in machine tra ... [...More Info...] [...Related Items...] OR: [Wikipedia] [Google] [Baidu] |
_(cropped).png "Click for more on -> Computer Vision")
