Neural process - A neural process is a learned probabilistic model that uses a set of observed input-output pairs to predict outputs at other inputs.
Neural radiance field - A neural radiance field, or NeRF, is a learned representation of a scene that predicts volume density and view-dependent color from spatial location and...
Neural scaling law - A neural scaling law is an empirical relationship describing how a neural model's measured performance changes as resources such as model size, training...
Neural style transfer - Neural style transfer is a family of image-transformation methods that uses neural representations to combine aspects of one image's content with aspects of...
Neural tangent kernel - The neural tangent kernel (NTK) measures how similarly a neural network's outputs at two inputs respond to small changes in its trainable parameters.
Neural Turing Machine Network - A Neural Turing Machine, or NTM, is a neural architecture that combines a trainable controller with an external memory matrix.
Neuralese - Neuralese is an informal term for machine-learned communication or internal representations that do not take the form of ordinary human language.
Neuro-fuzzy systems - Neuro-fuzzy systems combine neural or adaptive learning methods with fuzzy inference.
Neuroevolution of augmenting topologies - Neuroevolution of augmenting topologies, usually called NEAT, is an evolutionary method that searches for both neural-network connection weights and network...
Neuron - A neuron in machine learning is a small computing unit that takes several numeric inputs, combines them with learned weights, and passes the result through...
Neurorobotics - Neurorobotics studies neural models and robotic bodies together, using interaction with an environment to investigate brain-inspired control and embodied...
Newline character - A newline character is a control character used to mark a line boundary in text.
Newman-Keuls test - The Newman-Keuls test, also called the Student-Newman-Keuls test, is a stepwise procedure for comparing group means.
Newton-Raphson - Newton-Raphson is an iterative numerical method for finding a root of a function: an input x for which f(x)=0.
Next sentence prediction - Next sentence prediction (NSP) is a self-supervised classification task that asks whether a supplied second text segment follows a first segment in the...
Next word index - A next word index, often written nextword index in information-retrieval research, organizes words by the words that immediately follow them in documents.
Neyman - Pearson lemma - The Neyman - Pearson lemma identifies a most powerful statistical test for a fully specified null distribution against a fully specified alternative...
Nguyen-Wainwright-Jordan lower bound - The Nguyen-Wainwright-Jordan (NWJ) lower bound is a variational expression that bounds Kullback-Leibler divergence from below using a real-valued scoring...
No-good - A no-good is a combination of variable assignments that cannot be extended into a valid solution of a constraint satisfaction problem.
No-information - No-information, in the referenced database literature, describes a missing-value situation in which neither the value nor whether an applicable value exists...
No one right answer - No one right answer, often abbreviated NORA in generative-AI evaluation discussions, describes a prompt that can have multiple acceptable responses.
No-reference metric - A no-reference metric estimates image or video quality without requiring a matching pristine reference at evaluation time.
No-regret learning - No-regret learning is a way of making repeated decisions so that the learner's average performance approaches that of a specified hindsight benchmark.
No-U-turn sampler - The No-U-turn sampler (NUTS) is a Hamiltonian Monte Carlo method that adapts the length of each simulated trajectory instead of requiring a fixed number of...
Node - A node is an individual element of a graph or tree, often representing an entity, state, variable, or operation. Connections between nodes are called edges.
Node Classification - Node classification is the task of labeling the nodes of a network: given a graph of connected items and labels for some of them, predict the labels of the...
Node (decision tree) - A node in a decision tree is one decision point or conclusion: internal nodes test a condition on a feature, and leaf nodes carry the final prediction.
Node degree - Node degree measures the connections incident to a node in a graph. In a simple undirected graph without self-loops, it equals the number of distinct neighbors.
node2vec - node2vec is a graph representation method that learns a numerical vector for each node by predicting neighborhood relationships sampled through biased...
Noise - Noise is the part of data that varies without meaning: measurement error, random fluctuation, typos, one-off circumstances, the static that no model can...
Noise-based logic - Noise-based logic is a research approach in which distinguishable noise signals represent logical values and specially designed operations manipulate those...
Noise-contrastive estimation - Noise-contrastive estimation (NCE) learns a statistical model by training a classifier to distinguish observed data from samples drawn from a known noise...
Noise factor - A noise factor is a process input or operating condition that is difficult, inconvenient or impractical to control in normal use.
Noise reduction - Noise reduction is the process of reducing unwanted variation in a signal while trying to preserve useful information.
Noise-to-signal ratio - Noise-to-signal ratio compares the magnitude of unwanted variation with the magnitude of a signal.
Noisy-channel coding theorem - The noisy-channel coding theorem states that reliable digital communication is possible over a noisy channel at information rates below its capacity, using...
Noisy channel model - A noisy channel model explains an observed signal as the result of an underlying message passing through a process that can alter it.
Noisy-or - Noisy-or is a probabilistic model in which any one of several active causes can produce an effect, but each cause may independently fail to do so.
Noisy text analytics - Noisy text analytics is the analysis of text whose spelling, formatting, segmentation, or other characteristics differ from the conventions expected by a...
Nominal scale - A nominal scale classifies observations into categories without assigning those categories an inherent order or numerical distance.
Nominal significance level - A nominal significance level is the stated significance threshold for a statistical test, commonly written as alpha.
NOMINATE (scaling method) - NOMINATE is a family of statistical scaling methods that estimates low-dimensional positions from recorded choices, especially legislative roll-call votes.
Non-binary condition - A non-binary condition is a test with three or more possible outcomes: not yes-or-no, but a choice among several branches, such as routing by region into...
Non-blind deconvolution - Non-blind deconvolution estimates a sharp signal or image from a blurred observation when the blur model is supplied or treated as known.
Non-capturing group - A non-capturing group is a regular-expression group that organizes part of a pattern without assigning that group a captured substring.
Non-central F distribution - The non-central F distribution is the distribution of a ratio formed by dividing an independent noncentral chi-square variable and a central chi-square...
Non-destructive editing - Non-destructive editing changes the appearance of an image while preserving the source data and keeping editing decisions revisable.
Non-equivalent groups design - A non-equivalent groups design compares intervention and comparison groups that were not formed by random assignment and may differ in important ways before...