Time Series Analysis
Time series analysis is the statistical study of ordered observations when dependence across time is part of the signal rather than a nuisance to ignore.
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Timestep
A timestep is a discrete unit of time progression in a sequential process. The term shows up in many corners of machine learning and applied math, and it does not always mean the same thing.
Token
A token is the basic unit of text that a language model reads and writes: a word, a subword fragment, a single character, or a byte, produced by splitting text during a step called tokenization.
Tokenization
Tokenization is the process of breaking text into smaller units called tokens, which serve as the fundamental input to natural language processing (NLP) systems and large language models (LLMs).
Top-p and top-k sampling
Top-p sampling, also called nucleus sampling, is a text-generation decoding method for large language models (LLMs) that, at each step, samples the next token from the smallest set of highest-probability…
Top-p sampling
Top-p sampling, also called nucleus sampling, is a stochastic decoding method for text generation in which the model samples from the smallest possible set of tokens whose cumulative probability mass exceeds a…
TopK SAE
A TopK SAE (TopK sparse autoencoder) is a variant of sparse autoencoder that enforces sparsity by keeping only the K largest latent pre-activations for each input and zeroing all the rest
Topic model
A topic model is a statistical model that discovers the abstract "topics" hidden in a collection of documents, where each document is represented as a mixture of a small number of latent topics and each topic…
Tower
In deep learning, a tower is a self-contained sub-network inside a larger model that encodes one specific input or feature group into an output, usually an embedding
Training
Training in machine learning is the process of fitting a model's parameters to data so that the model can make accurate predictions or generate useful outputs.
Training AI to Paint with Code
Training AI to Paint with Code is an experimental AI art project published by designer and researcher Surya Narreddi in March 2026.
Training Set
A training set is the portion of data used to fit a statistical or machine learning model.
Training run
A training run is a single, deliberate instance of training a neural network from scratch (or from a prior checkpoint) on a specified dataset, with a fixed compute budget, hardware allocation, and time horizon.
Training-Serving Skew
Training-serving skew is a difference between a machine learning model's performance during training and its performance during serving (production inference).
Trajectory (Reinforcement Learning)
A trajectory in reinforcement learning is a sequence of states, actions, and rewards that an agent experiences while interacting with an environment.
Transfer Learning
Transfer learning is a family of machine learning methods in which information learned from one source domain or task is used while learning a target domain or task.
Trigram
A trigram is a contiguous sequence of three items (most often three words) drawn from a sample of text or speech
True negative
A true negative (TN) is a case that a binary classification model correctly predicts as belonging to the negative class: the true label is negative and the predicted label is also negative.
True positive
A true positive (TP) is a prediction that is correctly positive: the model predicts the positive class and the true label is also positive.
True positive rate (TPR)
The true positive rate (TPR) is the proportion of actual positive cases that a classifier correctly identifies as positive, computed as TPR = TP / (TP + FN), where TP is the number of true positives and FN the…
TruthfulQA
TruthfulQA is a benchmark designed to measure whether large language models (LLMs) generate truthful answers to questions.
Two-Tower Model
The two-tower model, also known as the dual encoder, bi-encoder, or Siamese network for retrieval, is a neural network architecture that encodes a query and a candidate item with two separate sub-networks…
U-Net
U-Net is a convolutional neural network architecture designed for biomedical image segmentation.
UMAP (Uniform Manifold Approximation and Projection)
UMAP (Uniform Manifold Approximation and Projection) is a nonlinear dimensionality reduction technique that compresses high-dimensional data into a low-dimensional map (typically 2 or 3 dimensions) while…
Unawareness (Fairness Through Unawareness)
Unawareness to a sensitive attribute, more commonly called fairness through unawareness (FTU), is a machine learning fairness approach that tries to make a model fair by simply not giving it the sensitive or…
Underfitting
Underfitting occurs when a machine learning model is too simple to capture the underlying patterns in the data.
Undersampling
Undersampling is a class imbalance handling technique in machine learning that removes examples from the majority class of a training set so the minority class is no longer drowned out.
Universal Approximation Theorem
The universal approximation theorem states that a feedforward neural network with a single hidden layer of finite width and a suitable nonlinear activation function can approximate any continuous function on a…
Universal Manipulation Interface
Universal Manipulation Interface (UMI) is an open-source system for collecting robot manipulation training data with a handheld, camera-equipped gripper instead of an actual robot.
Unlabeled example
An unlabeled example is a data instance that has one or more features but no label, meaning it carries the inputs a model reads but not the target answer the model is meant to produce.
Unsupervised Machine Learning
Unsupervised machine learning is a type of machine learning that finds patterns, structures, and relationships in data that has no labels, with no human-provided answer key to learn from.
Unsupervised learning
Unsupervised learning is a branch of machine learning in which algorithms identify patterns, structures, and relationships in data without relying on labeled examples or explicit human guidance.
Uplift Modeling
Uplift modeling (also called incremental modeling, true lift modeling, or net modeling) is a set of machine learning and statistical techniques that predict the incremental impact of a treatment or action on…
Upweighting
Upweighting is the practice of assigning a larger weight to certain training examples (or groups of examples) so they contribute more to the loss function and gradient updates than the rest of the data.
User matrix
In collaborative filtering and matrix factorization recommender systems, the user matrix (commonly written U or P) is the matrix of latent-factor vectors for users: each row is one user's embedding in a…
V-JEPA
V-JEPA (Video Joint Embedding Predictive Architecture) is a self-supervised video model from Meta AI that learns by predicting masked regions of a video in an abstract latent representation space rather than…
VAPO (Value-based Augmented PPO)
VAPO (Value-based Augmented Proximal Policy Optimization) is a reinforcement learning framework for training large language models on long chain-of-thought reasoning tasks.
VC dimension
The Vapnik-Chervonenkis dimension, almost always shortened to VC dimension, measures the capacity of a class of binary classifiers by asking how many points the class can label in every possible way.
Validation
Validation in machine learning is the process of checking how well a trained model performs on data it did not see during training, using a held-out validation set to tune hyperparameters, choose between…
Validation Set
A validation set (also called a development set or dev set) is a subset of labeled data that is held out from the training set and used to evaluate a model's performance during development
Validation loss
Validation loss is the value of a model's loss function measured on a held-out validation set, data the model never sees during weight updates, and it is the primary signal practitioners use to judge how well…
Vanishing Gradient Problem
The vanishing gradient problem is a difficulty in training deep neural networks where the gradients used to update the network shrink exponentially as they are propagated backward through the layers, leaving…
Variable importances
Variable importances, also called feature importances, are scores assigned to each input variable of a predictive model that measure how much that variable contributes to the model's output.
Variational Inference
Variational inference (VI), also called variational Bayes (VB), is a method in machine learning and statistics that approximates an intractable posterior distribution by turning Bayesian inference into an…
Vector database
A vector database is a database that stores data as high-dimensional vectors (numerical embeddings produced by a machine learning model) and retrieves records by similarity rather than exact match
Wasserstein Loss
Wasserstein loss is a loss function for training generative models that measures the distance between two probability distributions as the Wasserstein-1 distance
Wav2Vec 2.0
Wav2Vec 2.0 is a self-supervised learning framework for speech representation, developed by the Facebook AI Research (FAIR) group at Meta and introduced in 2020.
Weak supervision
Weak supervision is a machine learning paradigm in which models are trained from noisy, limited, imprecise, or programmatically generated labels rather than from large, expensively hand annotated datasets.
Weight
In machine learning and neural networks, a weight is a learnable numerical parameter that determines the strength of the connection between two neurons.
Weight Decay
Weight decay is a regularization technique used in training neural networks that shrinks every parameter toward zero by a small fraction on each update step
Weighted Alternating Least Squares (WALS)
Weighted Alternating Least Squares (WALS), also called implicit Alternating Least Squares (iALS) or weighted regularized matrix factorization (WRMF), is a matrix factorization algorithm for collaborative…
Weighted Sum
A weighted sum is a mathematical operation that combines multiple input values by multiplying each value by a corresponding weight (coefficient) and then summing the results.
Weights & Biases
Weights & Biases (commonly abbreviated as W&B or wandb) is a machine learning experiment tracking and model management platform that acts as a system of record for AI model training and fine-tuning.
Wide Model
A wide model is a type of machine learning model that uses a large number of input features, often with sparse, high-dimensional representations such as one-hot encoding and cross-product feature…
Width
Width refers to the number of neurons in a specific layer of a neural network. In modern transformer language models, the dominant width parameter is usually called hidden_size or d_model, and it sets the…
WildChat
WildChat is a large public corpus of real conversations between human users and ChatGPT, released by researchers at the Allen Institute for AI (AI2) and Cornell University.
Wisdom of the Crowd
Wisdom of the crowd is the observation that the aggregate judgment of a large group of individuals often produces more accurate estimates or decisions than any single member of that group
Wojciech Zaremba
Wojciech Zaremba is a Polish computer scientist and a co-founder of OpenAI, the artificial intelligence research company started in December 2015, where he has worked since the company's founding.
Word Embedding
A word embedding is a learned representation of text in which words are mapped to dense vectors of real numbers in a continuous vector space, so that words with similar meanings are positioned close together.
Word error rate
Word error rate (WER) is the standard metric for measuring the accuracy of an automatic speech recognition (ASR) system