Mixture of Agents
Mixture of Agents (MoA) is a multi-model collaboration framework that combines multiple large language models (LLMs) in a layered architecture
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Mixture of Depths
Mixture of Depths (MoD) is a technique for dynamically allocating computation to individual tokens within transformer-based language models.
Mixture of Experts (MoE)
A mixture of experts (MoE) is a machine-learning architecture that contains multiple component functions called experts and a gating or routing mechanism that determines how their outputs contribute to a…
Modality
A modality in machine learning and artificial intelligence is a distinct type, form, or structure of data that a model can process, learn from, or generate.
Model
A model in machine learning is a representation used to map inputs to outputs, assign probabilities, describe relationships, or choose actions.
Model Capacity
Model capacity is the size and richness of the family of functions a machine learning model can represent and learn, which determines how complex a pattern the model can fit.
Model Evaluation
Model evaluation is the process of measuring how well a machine learning model performs on data it was not trained on, in order to estimate how it will generalize to new, unseen inputs.
Model Parallelism
Model parallelism is a distributed training and inference technique that splits a single neural network across multiple processing units so that no individual accelerator has to hold the entire model.
Model collapse
Model collapse is a degenerative process in which generative AI models trained recursively on data produced by previous-generation models progressively lose information, especially the rare events in the tails…
Model extraction attack
A model extraction attack is a class of machine learning security attacks in which an adversary, restricted to black-box query access to a target model (typically through a paid prediction API)
Model merging
Model merging combines the parameters of multiple trained neural networks into a single unified model without any additional training.
Model soups
Model soups is a weight-averaging technique (a form of model merging) that combines several independently fine-tuned neural networks into a single model by averaging their parameters.
Model stealing
Model stealing (also known as model extraction, model functionality extraction, or model theft) is an adversarial machine learning attack in which an adversary queries a black-box model through its prediction…
Model training
Model training is the process by which a machine learning system learns parameter values, model structure, or both from data or interaction.
ModelScope
ModelScope is an open-source Model-as-a-Service (MaaS) platform developed by Alibaba Cloud and DAMO Academy and launched on November 3, 2022, that functions as China's largest AI model and dataset hub…
Multi-Class Classification
Multi-class classification is a supervised learning task in machine learning that assigns each input to exactly one of three or more mutually exclusive classes.
Multi-Class Logistic Regression
Multi-class logistic regression, also known as multinomial logistic regression, softmax regression, or the maximum entropy (MaxEnt) classifier
Multi-Head Self-Attention
Multi-head self-attention is the core sequence-mixing mechanism of the Transformer architecture: it runs several scaled dot-product attention operations ("heads") in parallel over different learned projections…
Multi-armed bandit
A multi-armed bandit is a sequential decision problem in which a learner repeatedly picks one action from a fixed set, receives a random reward for that action only, and tries to accumulate as much reward as…
Multi-head Latent Attention
Multi-head Latent Attention (MLA) is an attention mechanism for transformer models that achieves a 93.3% reduction in key-value cache size while maintaining or exceeding the performance of traditional…
Multi-task learning
Multi-task learning (MTL) is a machine learning approach in which one model is trained on several tasks at the same time, so that what it learns for one task helps it learn the others.
Multi-token prediction
Multi-token prediction (often abbreviated MTP) is a language modeling training objective in which the model is trained to predict several future tokens at each context position rather than only the next token.
Multimodal Model
A multimodal model is a machine learning model, or a model-based system, that processes, relates, or produces information across more than one kind of data. Each kind is called a modality.
Multinomial classification
Multinomial classification, also called multiclass or multi-class classification, is the supervised learning task of assigning each input to exactly one of K possible classes, where K is greater than two.
Multinomial regression
Multinomial regression is a statistical model that predicts which one of K possible categories an observation belongs to, given a vector of input features.
Multiple sequence alignment
A multiple sequence alignment (MSA) arranges three or more biological sequences, usually proteins, DNA or RNA
N-gram
An n-gram is a contiguous sequence of n items extracted from a given sample of text or speech, where the items can be characters, syllables, words, or other linguistic units.
NOSA (Native and Offloadable Sparse Attention)
NOSA (Native and Offloadable Sparse Attention) is a trainable sparse attention mechanism designed so that most of a language model's KV cache can live in CPU memory during decoding without the CPU-to-GPU…
NVFP4
NVFP4 (NVIDIA FP4) is a 4-bit floating-point number format introduced by Nvidia with the Blackwell GPU architecture.
NaN Trap
A NaN trap (short for "Not a Number" trap) is a failure mode in machine learning training where arithmetic operations produce the special IEEE 754 value NaN, which then propagates through all subsequent…
Naive Bayes
Naive Bayes is a family of probabilistic classification algorithms that apply Bayes' theorem under a strong ("naive") assumption that every feature is conditionally independent of every other feature given the…
Named entity recognition
Named entity recognition (NER) is the natural language processing task of locating spans of text that name real-world things, such as people, organizations, and locations, and classifying each span into a…
Natural Language Processing
Natural language processing (NLP) is the study and development of computational methods for analyzing, representing, retrieving, transforming, and generating human language.
Natural Language Understanding
Natural language understanding (NLU) is the branch of artificial intelligence and computational linguistics that enables machines to read human language and extract its meaning, intent, and structure.
Natural language generation
Natural language generation (NLG) is the subfield of natural language processing and artificial intelligence concerned with building systems that produce understandable text in English or other human…
Negative class
In binary classification, the negative class is the outcome the model treats as the default or "no" result: the label assigned to instances that do not possess the target characteristic the model is testing…
Netflix Prize
The Netflix Prize was an open machine learning competition, run by Netflix from October 2, 2006 to September 21, 2009, that offered US$1,000,000 to the first team that could improve the accuracy of Netflix's…
NeurIPS
NeurIPS is the short name used by the annual Conference on Neural Information Processing Systems.
Neural Machine Translation
Neural machine translation (NMT) is an approach to machine translation in which a single artificial neural network, trained end to end on bilingual text
Neural Network
A neural network (also called an artificial neural network or ANN) is a computational model, loosely inspired by the networks of biological neurons in animal brains
Neural Tangent Kernel
The neural tangent kernel (NTK) is a kernel function built from the parameter gradients of a neural network.
Neural architecture search
Neural architecture search (NAS) is a technique for automating the design of neural network architectures.
Neuron
A neuron (also called a node or unit) is the fundamental computational element of an artificial neural network: it takes one or more numeric inputs, multiplies each by a learned weight, sums them with a bias…
Next-token prediction
Next-token prediction is the training objective used by most modern language models: the model reads a prefix of tokenized text, outputs a probability distribution over which token comes next, and training…
Node (decision tree)
A node is the basic building block of a decision tree: it is a single point in the tree that is either a condition (an internal node that tests a feature and branches) or a leaf (a terminal node that holds a…
Noise
Noise in machine learning is any unwanted, irrelevant, or random variation in data that obscures the true underlying patterns a model is trying to learn.
Non-Response Bias
Non-response bias is the error that arises when the people or units that do not respond to a survey, study, or data collection process differ systematically from those that do
Non-binary condition
In decision tree learning, a non-binary condition is a test at a node that has more than two possible outcomes, routing each example to one of three or more child nodes and creating a multi-way split.
Nonlinear
Nonlinear describes any function, model, or relationship that does not satisfy the property of linearity.
Nonstationarity
Nonstationarity refers to the condition in which the statistical properties of a data-generating process change over time.
Normalization
Normalization is the process of scaling numerical data to a standard range or distribution so that features and activations are comparable and downstream computation behaves predictably.
Novelty Detection
Novelty detection is a branch of machine learning concerned with identifying test data that differ in some meaningful way from the data available during training.
NumPy
NumPy (short for Numerical Python) is the foundational open-source library for numerical and scientific computing in Python, providing an n-dimensional array object called ndarray along with a large collection…
Numerical Data
Numerical data (also called quantitative data) is information expressed as numbers on a continuous or discrete scale that supports arithmetic operations such as addition, subtraction, multiplication, and…
OCR Models
OCR Models are artificial intelligence (AI) systems that convert images of typed, handwritten, or printed text into machine-readable digital text through Optical Character Recognition (OCR).
ONNX
ONNX (Open Neural Network Exchange) is an open standard file format for representing machine learning models so they can be moved between different frameworks, runtimes, and hardware platforms without being…
ORPO
ORPO (Odds Ratio Preference Optimization) is a preference alignment algorithm for large language models that merges supervised fine-tuning and preference alignment into a single training stage, eliminating the…
Object detection
Object detection is a computer vision task that finds instances of interest in an image and assigns each one a category.
Objective
In machine learning, an objective (or objective function) is the scalar function that a learning algorithm optimizes during training: the single number whose gradients drive every parameter update.
Oblique condition
An oblique condition is a decision tree split test that involves more than one feature, comparing a linear combination of several numerical features to a threshold rather than testing a single feature on its…