Episode (Reinforcement Learning)
An episode in reinforcement learning is one complete sequence of interaction between an agent and its environment, starting from an initial state and ending when a terminal state is reached.
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Showing 241-300 of 873 articles
Epoch
An epoch in machine learning is one complete pass through the entire training dataset, during which every example is presented to the model exactly once to compute gradients and update parameters before the…
Epsilon Greedy Policy
The epsilon-greedy policy (also written as ε-greedy) is a simple action-selection rule for reinforcement learning and multi-armed bandit problems: with probability 1 - ε the agent picks the action with the…
Equality of Opportunity
Equality of opportunity is a group-fairness criterion in machine learning that requires a classifier's true positive rate (TPR) to be equal across all groups defined by a sensitive attribute: qualified…
Equalized Odds
Equalized odds is a group fairness criterion in machine learning that requires a classifier's true positive rate (TPR) and false positive rate (FPR) to be equal across all groups defined by a protected…
Estimator
An estimator is a rule, function, or algorithm that takes observed data and produces a value intended to approximate some unknown quantity, typically a parameter of a probability distribution or a function…
Estimator (tf.estimator)
tf.estimator is a high-level TensorFlow API that encapsulates the complete lifecycle of a machine learning model, including training, evaluation, prediction, and export for serving.
Evol-Instruct
Evol-Instruct is a method for automatically generating large instruction tuning datasets by prompting a large language model to rewrite
Example
In machine learning, an example is a single data point that a model trains on or makes a prediction about: in supervised settings it is a pair $(x, y)$ where $x$ is a vector of features and $y$ is a label, and…
Expectation-Maximization (EM) Algorithm
The Expectation-Maximization (EM) algorithm is an iterative method for finding maximum likelihood or maximum a posteriori (MAP) estimates of the parameters of statistical models that involve latent…
Expected calibration error
Expected calibration error (ECE) is a metric that measures how well a classifier's predicted confidence matches its observed accuracy.
Experience Replay
Experience replay is a reinforcement learning technique in which an agent stores its past transitions in a memory called a replay buffer and samples random mini-batches of those stored transitions to train on
Experimenter's Bias
Experimenter's bias (also called the observer-expectancy effect, experimenter expectancy effect, or experimenter effect) is a type of cognitive bias in which a researcher's expectations or beliefs about the…
Exploding Gradient Problem
The exploding gradient problem is a training failure in deep neural networks where the gradients of the loss function grow exponentially large as they propagate backward through layers during backpropagation…
F1 score
The F1 score (also written as F1-score, F-score, or F-measure) is the harmonic mean of precision and recall.
FLOPS
FLOPS (floating-point operations per second, also written FLOP/s) is a measure of computing speed: the number of arithmetic operations on floating-point numbers that a processor, accelerator or supercomputer…
FOMO-Dex
FOMO-Dex (short for Force-Modulated Dexterity) is a robot control method for contact-rich dexterous manipulation.
Fairness Constraint
A fairness constraint is an explicit mathematical condition imposed on a machine learning model during training, evaluation, or post-processing that forces its predictions to satisfy a specified group-fairness…
Fairness Metric
A fairness metric is a quantitative, mathematical measure used to evaluate whether a machine learning model's predictions or decisions treat different demographic groups equitably.
False Negative Rate
The false negative rate (FNR), also known as the miss rate, is the proportion of actual positive instances that a model or test incorrectly classifies as negative, computed as FNR = FN / (FN + TP).
False Positive Rate (FPR)
The false positive rate (FPR) is the proportion of actual negative cases that a test, model, or decision process incorrectly classifies as positive, defined as FPR = FP / (FP + TN) where FP is the number of…
False negative
A false negative (FN), also called a Type II error or a miss, is an instance whose true label is positive but that a classification model or test predicts as negative: a real positive case that the model fails…
False positive
A false positive (FP), also called a Type I error or a false alarm, is an instance whose true label is negative but whose predicted label is positive: the classifier raises an alarm where none was warranted.
Feature
In machine learning and statistics, a feature is an individual measurable property or characteristic of a phenomenon being observed, used as an input variable from which a model learns patterns and makes…
Feature Cross
A feature cross (also called a crossed feature or feature interaction) is a synthetic feature created by combining two or more existing features so that a model can learn their joint effect on a prediction.
Feature Engineering
Feature engineering is the process of using domain knowledge to create, transform, and select features from raw data so that machine learning models can learn more effectively.
Feature Extraction
Feature extraction is the process of transforming raw data into a smaller set of derived, informative numerical variables called features that capture the essential structure of the input while discarding…
Feature Importances
Feature importances are numeric scores that quantify how much each input feature contributes to the predictions of a machine learning model.
Feature Selection
Feature selection is the process of choosing a subset of the most relevant input variables (features) from a larger candidate pool for use in a machine learning model, with the goal of finding the smallest set…
Feature Set
A feature set is the complete collection of input variables (features, attributes, or predictors) that a machine learning model uses to learn patterns and make predictions.
Feature Vector
A feature vector is an n-dimensional, ordered list of numerical values that represents the measurable properties of an object, data point, or observation in a format suitable for processing by machine learning…
Feature spec
A feature spec (short for feature specification) is a declarative description of the input features used by a machine learning model: the name of each feature, its data type, its shape, whether it is required…
Federated Learning
Federated learning is a machine learning technique that trains a shared model across many decentralized devices or servers without moving their raw data to a central location.
Feedback Loop
A feedback loop in machine learning is a cycle in which a deployed model's predictions influence the real world, and the resulting data is then collected and used to retrain the same model
Feedforward Neural Network (FFN)
A feedforward neural network (FFN), also called a multilayer perceptron (MLP) when it has multiple layers, is a type of artificial neural network in which information flows in one direction only, from the…
Few-Shot Learning
Few-shot learning is a branch of machine learning in which a model learns to recognize new classes or perform new tasks from only a very small number of labeled examples, typically one to five per class.
Fine-tuning
Fine-tuning is the process of adapting a pretrained machine-learning model by continuing to optimize some or all of its parameters on data selected for a target task, domain, behavior, or population.
FineWeb
FineWeb is a large-scale, open pretraining dataset for large language models (LLMs) created by Hugging Face.
FineWeb-2
FineWeb-2 (also written FineWeb2) is a massively multilingual web pretraining dataset released by Hugging Face in December 2024.
FineWeb-Edu
FineWeb-Edu is an open, English-language pretraining dataset of roughly 1.3 trillion tokens
Flash Attention
FlashAttention is a family of exact, input/output-aware algorithms and GPU kernels for scaled dot-product attention.
Focal loss
Focal loss is a loss function that reshapes standard cross-entropy loss by adding a (1 - p_t)^gamma modulating factor, which down-weights well-classified (easy) examples so that training concentrates on hard
Forget Gate
The forget gate is a sigmoid layer inside a Long Short-Term Memory (LSTM) recurrent neural network that decides, element by element, how much of the previous cell state to keep and how much to discard at each…
Frontis-MA1
Frontis-MA1 is a family of open-weight large language models post-trained to act as agents for machine learning engineering (MLE), released in late July 2026 by FrontisAI
Full Softmax
Full softmax (also called the standard softmax or exact softmax) is the softmax computation that calculates a probability for every possible output class in a classification model
Fully Connected Layer
A fully connected layer (also called a dense layer or linear layer) is a layer in an artificial neural network in which every input value connects to every output value through a learned weight.
Fundamental (company)
Fundamental (legal name Fundamental Technologies Inc.) is an American artificial intelligence company, headquartered in San Francisco, that builds what it calls large tabular models (LTMs): foundation models…
GEM (Meta)
GEM (Generative Ads Recommendation Model) is a proprietary foundation model for advertising recommendation developed by Meta Platforms.
GGML
GGML is an open-source tensor library written in pure C that runs machine learning inference efficiently on consumer hardware
GGUF
GGUF (GPT-Generated Unified Format) is the standard binary file format for storing large language models for local inference, bundling a model's weights, tokenizer, and metadata into a single self-contained…
GLUE benchmark
The General Language Understanding Evaluation (GLUE) benchmark is a collection of nine natural language understanding (NLU) tasks designed to evaluate and compare the performance of language models across a…
GPU computing
GPU computing is the use of a graphics processing unit (GPU) to perform general-purpose computation that was traditionally handled by the central processing unit (CPU).
GSM8K
GSM8K (Grade School Math 8K) is an English-language benchmark of grade-school arithmetic word problems released by OpenAI researchers in 2021.
GSQ-RCO
GSQ-RCO is a model-compression workflow that combines Gumbel-Softmax Quantization (GSQ) with Riemannian Constrained Optimization (RCO).
GaLore (Gradient Low-Rank Projection)
GaLore (Gradient Low-Rank Projection) is a memory-efficient training strategy for large neural networks that projects each weight matrix's gradient into a low-rank subspace, computes the Adam-style optimizer…
Gated SAE
A Gated sparse autoencoder (Gated SAE) is a sparse-autoencoder architecture for mechanistic interpretability that splits the encoder into a gating path, which decides which features are active, and a magnitude…
Gaussian Mixture Model
A Gaussian Mixture Model (GMM) is a probabilistic model that represents a dataset as a weighted mixture of a finite number of Gaussian distributions with unknown parameters
Gaussian Process
A Gaussian process (GP) is a probabilistic machine learning model defined as a collection of random variables, any finite number of which have a joint Gaussian distribution.
Gemma Scope
Gemma Scope is an open, comprehensive suite of sparse autoencoders (SAEs) released by Google DeepMind in 2024 to support mechanistic interpretability research on its open-weight Gemma 2 language models.
GenRec (Netflix)
GenRec is an experimental large language model (LLM) backed recommendation ranker built at Netflix.