Influence functions (machine learning)
An influence function is a tool for estimating how a machine learning model's predictions would change if a single training example were removed or perturbed, without retraining the model.
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Information Gain
Information Gain (IG) is a measure from information theory that quantifies the reduction in entropy (uncertainty) achieved by partitioning a dataset on a particular feature: it equals the entropy of the parent…
Information Retrieval
Information retrieval (IR) is the study and engineering of systems that identify material likely to satisfy an information need.
Input Layer
The input layer is the first layer of a neural network: it receives the raw feature vector for each data sample and passes those values forward to the next layer, performing no learned computation of its own.
Instance
In machine learning and statistics, an instance is a single data point in a dataset: the values of one row of features and, optionally, a label .
Instruction Tuning
Instruction tuning is the post-pretraining training stage in which a large language model (LLM) is fine-tuned on a curated collection of (instruction
Instruction backtranslation (Humpback)
Instruction backtranslation is a self-alignment method for generating instruction tuning data, introduced by researchers at Meta AI in the paper "Self-Alignment with Instruction Backtranslation," first posted…
Integrated Gradients
Integrated Gradients (IG) is a feature-attribution method for explainable AI that explains a neural network prediction by assigning each input feature an importance score
Interpretability
Interpretability in artificial intelligence concerns what people can learn about a system's behavior, predictions, or internal computations, and whether that understanding is reliable enough for a stated…
Iris dataset
The Iris dataset, sometimes referred to as Fisher's Iris dataset or the Iris flower dataset, is a multivariate dataset introduced by the British statistician and biologist Ronald Fisher in his 1936 paper "The…
Item matrix
In collaborative filtering and matrix factorization recommender systems, the item matrix (commonly written V, sometimes Q or H) is the matrix of latent-factor vectors for items: each row holds one item's…
Items
In recommendation systems, items are the entities that the system recommends to users. Google's recommendation systems course defines an item, also called a document, as "the entities a system recommends.
Iteration
An iteration in machine learning is a single update of a model's parameters during training, performed by processing one batch of data: the model makes predictions on the batch (a forward pass), computes the…
JAX
JAX is an open-source Python library for accelerator-oriented array computation and program transformation.
Jev (AI model)
Jev is an AI model released in early access on 15 September 2026 by TypeSafe AI, a San Francisco startup founded in 2024 by former OpenAI researcher Diogo Almeida with Erik Gafni and Sasha Sheng.
John Giannandrea
John Giannandrea is a Scottish-born software engineer and technology executive who ran artificial intelligence organizations at two of the largest consumer technology companies in the world.
Joint Embedding Predictive Architecture
Joint Embedding Predictive Architecture (JEPA) is a family of self-supervised, non-generative neural network architectures proposed by Yann LeCun in his June 2022 position paper A Path Towards Autonomous…
Julia (programming language)
Julia is a high-level, high-performance, dynamically typed programming language built for technical computing, scientific computing, machine learning, and numerical analysis.
JumpReLU SAE
A JumpReLU sparse autoencoder (JumpReLU SAE) is a variant of the sparse autoencoder used in mechanistic interpretability whose encoder applies a learnable per-feature threshold that forces a feature to exactly…
K-Means
K-means is an unsupervised machine learning clustering algorithm that partitions a dataset into k distinct, non-overlapping clusters by repeatedly assigning each data point to its nearest cluster center and…
K-Median
K-median clustering (also written k-medians) is a partitioning-based clustering algorithm that divides a dataset of $$n$$ objects into $$k$$ groups by minimizing the sum of distances between each data point…
KL Divergence (Kullback-Leibler Divergence)
Kullback-Leibler divergence, often abbreviated KL divergence and written $$D_{\mathrm{KL}}(P \parallel Q)$$
KV Cache
KV cache, short for key-value cache, is transient model state used during Transformer generation.
KV-cache quantization
KV cache quantization is a family of large language model inference optimizations that store the attention key and value (KV) cache in low-bit numeric formats, typically 2 to 4 bits per value
Kalman Filter
The Kalman filter is a recursive algorithm that estimates the hidden state of a dynamic system from a sequence of noisy measurements.
Keras
Keras is an open-source, high-level neural network API written in Python that lets developers build, train, and deploy deep learning models with minimal code.
Kernel Support Vector Machines (KSVMs)
A kernel support vector machine (kernel SVM, or KSVM) is a supervised learning algorithm that finds the maximum-margin boundary between two classes after implicitly mapping the data into a high-dimensional…
Knowledge Distillation
Knowledge distillation is a training method in which a student model learns from signals produced by a teacher model.
Knowledge Editing
Knowledge editing (also called model editing) is a family of techniques for updating or correcting specific factual associations stored in the weights of a trained large language model without full retraining…
Kolmogorov-Arnold Network
A Kolmogorov-Arnold Network (KAN) is a type of neural network architecture proposed as an alternative to the traditional Multi-Layer Perceptron (MLP).
Kubeflow
Kubeflow is an open-source MLOps platform that runs the entire machine learning lifecycle on Kubernetes, described by its creators as a project "dedicated to making using ML stacks on Kubernetes easy, fast and…
L0 Regularization
L0 regularization is a regularization technique in machine learning and statistics that penalizes the number of nonzero parameters in a model, a quantity written $$\lVert \theta \rVert_0$$.
L1 Loss
L1 loss is a regression loss function equal to the average of the absolute differences between predicted values and target values, written as $$\frac{1}{n} \sum \lvert y_i - \hat{y}_i \rvert$$.
L1 Regularization
L1 regularization is a regularization technique in machine learning and statistics that prevents overfitting by adding the sum of the absolute values of a model's parameters as a penalty term to the loss…
L2 Loss
L2 loss is the squared-error loss function: for a true value $$y$$ and a predicted value $$\hat{y}$$, it is the squared difference $$(y - \hat{y})^2$$, and averaging it across a dataset gives the mean squared…
L2 Regularization
L2 regularization is a technique in machine learning and statistics that penalizes large weight values by adding the sum of squared parameters, scaled by a strength factor $$\lambda$$, to the loss function.
LAION
LAION (Large-scale Artificial Intelligence Open Network) is a German non-profit organization
Label
In machine learning, a label is the target output value associated with a single training example: the correct answer that a supervised learning model is trained to predict.
Labeled example
A labeled example is a single data point used to train a machine learning model that consists of one or more input features paired with the correct answer, called the label.
Lambda
Lambda (the Greek letter λ) is a symbol used across machine learning, statistics, and computer science to denote several distinct quantities
Landmarks
Landmarks are reference points used as anchors in two largely separate areas of machine learning.
Language Model
A language model is a model of patterns in language that assigns probabilities or comparable scores to linguistic sequences. Its units may be characters, words, subwords, bytes, or other tokens.
Large Behavior Model
A Large Behavior Model (LBM) is a single neural network for robotics that is pretrained on large, diverse datasets of robot demonstrations and outputs robot actions
Large Language Model
A large language model (LLM) is an artificial intelligence system built on a transformer neural network with billions to trillions of parameters, trained on massive text corpora to predict the next token in a…
Lasso Regression
Lasso regression (an acronym for Least Absolute Shrinkage and Selection Operator) is a linear regression method, introduced by Robert Tibshirani in 1996, that adds an L1 penalty to the ordinary least squares…
Latent Dirichlet allocation
Latent Dirichlet allocation (LDA) is a generative probabilistic model that discovers the hidden thematic structure in a collection of documents by treating each document as a mixture of a small number of…
Latent Space
A latent space is the vector space a machine learning model maps its inputs into, where each input becomes a point (a latent vector or latent code) and the geometry of the space carries information the raw…
Latent reasoning via recurrent depth (Huginn)
Latent reasoning via recurrent depth is an approach to scaling a language model's test-time computation by iterating a recurrent transformer block in latent (hidden) space
Layer
A layer is the fundamental building block of a neural network: an organized group of neurons (also called nodes or units) that together apply one mathematical transformation to their input and pass the result…
Layer-wise Relevance Propagation (LRP)
Layer-wise Relevance Propagation (LRP) is an explainable AI method that explains the prediction of a deep neural network by propagating the model's output backward through the network, layer by layer, and…
LeVJEPA
LeVJEPA is a self-supervised learning method for training video encoders. It applies the LeJEPA objective to video
Leaf
A leaf (also called a terminal node) is a node in a decision tree that has no children and holds the model's prediction.
Learning Rate
The learning rate is a hyperparameter that scales an update made by an iterative optimization algorithm. In machine learning, it is commonly written as η or α.
Least Squares Regression
Least squares regression is a statistical method that fits a model to data by choosing the parameters that minimize the sum of the squared residuals, the squared differences between the observed values and the…
Leo Breiman
Leo Breiman (January 27, 1928 to July 5, 2005) was an American statistician at UC Berkeley whose work on tree-based prediction supplied machine learning with several of its most durable tools.
LightGBM
LightGBM (short for Light Gradient-Boosting Machine) is a free and open-source gradient boosting framework that trains ensembles of decision trees on tabular data, originally developed at Microsoft Research by…
Lilian Weng
Lilian Weng is an artificial intelligence researcher known for her work at OpenAI, where she spent about seven years and led the Safety Systems team as Vice President of Research and Safety, and for her…
Linear
In machine learning and mathematics, linear describes a function or relationship in which the output is built from the inputs only through addition and multiplication by constants.
Linear Discriminant Analysis
Linear Discriminant Analysis (LDA) is a classical statistical method for classification and dimensionality reduction that finds the linear combination of features which best separates two or more classes by…
Linear Regression
Linear regression is a statistical method that models the relationship between one or more independent variables (the predictors or features) and a continuous dependent variable (the response) by fitting a…