Machine Learning

Explore learning methods, model architectures, datasets, and practical applications.

873 articlesSearch and filter Follow updates by RSS

Explore articles

Browse subtopics (65)

Articles that also belong to these categories. Counts cover all of Machine Learning.

All articles (873)Deep Learning (188)Natural Language Processing (92)Statistics (90)Training & Optimization (70)Data & Datasets (65)Neural Networks (58)Reinforcement Learning (58)Model Evaluation (56)Artificial Intelligence (54)Large Language Models (52)Mathematics (48)Computer Vision (42)AI Ethics (35)AI Infrastructure (29)AI Safety (26)Open Source AI (26)Data Science (24)AI Benchmarks (22)Developer Tools (22)Algorithms (20)AI Hardware (19)AI Tools & Products (17)Google (16)Information Retrieval (16)AI Inference (15)Model Architecture (15)Generative AI (14)People (14)Robotics (14)AI Research (13)AI Agents (12)Interpretability (12)MLOps (11)Transformer Models (11)AI Companies (9)AI Models (9)AI Compute (8)Computer Science (8)AI Code Generation (7)Multimodal AI (7)Speech & Audio AI (7)AI for Science (6)Chinese AI (6)AI Policy & Regulation (5)Meta AI (5)Programming Languages (5)AI History (4)Embodied AI (4)Prompt Engineering (4)AI Alignment (3)AI Events (3)Drug Discovery (3)Healthcare AI (3)Reasoning Models (3)Enterprise AI (2)Software Development (2)World Models (2)2025 in artificial intelligence (1)Anthropic (1)Diffusion Models (1)Education AI (1)Image Generation (1)NVIDIA (1)Research Organizations (1)Small Language Models (1)

Showing 481-540 of 873 articles

MemGPT

MemGPT (short for Memory-GPT) is a system and agent design pattern that gives large language model agents long-term memory by managing data between the model's bounded context window and external storage

AI Agents

Membership Inference Attack

A Membership Inference Attack (MIA) is a privacy attack against a trained machine learning model in which an adversary, given a candidate data record and access to the model

AI Safety

Meta Prompting

Meta prompting (also spelled meta-prompting) is an advanced prompt engineering technique where large language models (LLMs) are used to generate, refine, critique, select, or optimize prompts for themselves or…

Artificial Intelligence Natural Language Processing

Meta-Learning

Meta-learning, often called "learning to learn", is a branch of machine learning in which a model is trained across many different tasks so that it can master a new task from only a handful of examples or a…

Deep Learning

Metric

In machine learning, a metric is a quantitative measure used to evaluate how well a model or algorithm performs a task.

Metrics API (tf.metrics)

The Metrics API in TensorFlow is a collection of classes and utilities for computing evaluation scores that summarize how well a model is doing.

Microscaling formats

Microscaling (MX) formats are a family of low-precision number formats for machine learning in which a small block of values, normally 32 of them, shares one common scale factor while each value is stored in a…

AI Compute AI Hardware

Mini-batch

A mini-batch is a small, randomly sampled subset of the training dataset used to compute a single update to a model's parameters during training.

Minimax Loss

Minimax loss is a loss function rooted in game theory and decision theory that measures the worst-case performance of a strategy, algorithm, or model.

Generative AI Mathematics

Minority class

In an imbalanced classification problem, the minority class is the class label with far fewer training examples than the others, and it is almost always the outcome you actually care about (fraud, disease…

Mixed-Precision Training

Mixed-precision training is a technique for training deep learning models using lower-precision floating-point formats for most computations while maintaining a higher-precision copy of the model weights for…

Deep Learning

Mixture of Agents

Mixture of Agents (MoA) is a multi-model collaboration framework that combines multiple large language models (LLMs) in a layered architecture

AI Agents Artificial Intelligence

Mixture of Depths

Mixture of Depths (MoD) is a technique for dynamically allocating computation to individual tokens within transformer-based language models.

Deep Learning Model Architecture

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…

AI Inference Deep Learning

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.

Deep Learning

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.

AI Infrastructure Deep Learning

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…

AI Safety

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)

AI Safety

Model merging

Model merging combines the parameters of multiple trained neural networks into a single unified model without any additional training.

Deep Learning Large Language Models

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.

Reinforcement Learning

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…

AI Safety

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…

Chinese AI Developer Tools

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

Statistics

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…

Deep Learning Model Architecture

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…

Algorithms Reinforcement Learning

Multi-head Latent Attention

Multi-head Latent Attention (MLA) is an attention mechanism for transformer models that compresses the keys and values of all attention heads into one shared low-rank latent vector, so that only this vector

Deep Learning Model Architecture

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.

Deep Learning Training & Optimization

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.

AI Inference Large Language Models

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.

Computer Vision Deep Learning

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

AI for Science Data Science

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.

Natural Language Processing

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…

AI Inference Chinese AI

NVFP4

NVFP4 (NVIDIA FP4) is a 4-bit floating-point number format introduced by Nvidia with the Blackwell GPU architecture.

AI Compute AI Hardware

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…

Deep Learning Training & Optimization

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…

Statistics

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.

Artificial Intelligence Natural Language Processing