Geoffrey Hinton
Geoffrey Hinton (born December 6, 1947) is a British-Canadian computer scientist whose research has focused on neural networks, representation learning, and computational accounts of learning in the brain.
Machine Learning
Explore learning methods, model architectures, datasets, and practical applications.
Explore articles
Browse subtopics (63)
Articles that also belong to these categories. Counts cover all of Machine Learning.
All articles (858)Deep Learning (187)Natural Language Processing (92)Statistics (90)Training & Optimization (70)Data & Datasets (65)Neural Networks (58)Reinforcement Learning (58)Model Evaluation (55)Artificial Intelligence (54)Large Language Models (49)Mathematics (48)Computer Vision (41)AI Ethics (35)AI Infrastructure (29)AI Safety (26)Open Source AI (25)Data Science (24)AI Benchmarks (22)Developer Tools (20)Algorithms (18)AI Hardware (17)AI Tools & Products (17)Google (16)Generative AI (14)Information Retrieval (14)Model Architecture (13)People (13)AI Agents (12)Interpretability (12)Robotics (12)AI Companies (11)Transformer Models (11)AI Inference (10)MLOps (10)AI Models (8)Computer Science (8)AI Code Generation (7)AI Research (7)Multimodal AI (7)Speech & Audio AI (7)Chinese AI (6)AI Policy & Regulation (5)Meta AI (5)Programming Languages (5)AI History (4)AI for Science (4)Prompt Engineering (4)AI Alignment (3)AI Events (3)Reasoning Models (3)Embodied AI (2)Software Development (2)2025 in artificial intelligence (1)Anthropic (1)Diffusion Models (1)Drug Discovery (1)Education AI (1)Enterprise AI (1)Healthcare AI (1)Image Generation (1)NVIDIA (1)Small Language Models (1)World Models (1)
Showing 301-360 of 858 articles
Geometric Deep Learning
Geometric deep learning (GDL) is an area of deep learning that designs neural networks around the structure and symmetries of their data.
Gini Impurity
Gini impurity is the probability that a randomly chosen element from a dataset would be incorrectly classified if it were labeled at random according to the distribution of class labels in that dataset.
GlucoFM
GlucoFM is a self-supervised foundation model for continuous glucose monitoring (CGM) data developed by Google Research.
Google DeepMind
Google DeepMind is an artificial intelligence research and model-development organization at Google.
Grad-CAM
Grad-CAM (Gradient-weighted Class Activation Mapping) is a technique for producing visual explanations from convolutional neural network (CNN) models by using the gradients of a target class flowing into the…
Gradient
In machine learning, the gradient is the vector of partial derivatives of a loss function with respect to every model parameter, and it points in the direction in which the loss increases most steeply.
Gradient Accumulation
Gradient accumulation is a deep learning training technique that simulates a large batch size on limited GPU memory by summing the gradients from several small mini-batches (called micro-batches) and…
Gradient Boosting
Gradient boosting is a supervised machine learning method that constructs an additive prediction function in stages.
Gradient Descent
Gradient descent is an iterative first-order method for minimizing a differentiable scalar objective. Starting from a point, it evaluates the local gradient and moves in the opposite direction.
Gradient boosted (decision) trees (GBT)
Gradient boosted decision trees (GBT, also written GBDT, GBM, or GBRT) is an ensemble method that builds a strong predictor by sequentially fitting many shallow decision trees to the negative gradient of a…
Gradio
Gradio is an open-source Python library that lets developers build interactive web interfaces for machine learning models, APIs, and arbitrary Python functions in a few lines of code.
Graph
In machine learning the word graph has two unrelated meanings. The first is a graph as a data structure: a set of nodes (vertices) connected by edges, written formally as G = (V, E), used to represent…
Graph Execution
Graph execution is a computation paradigm in machine learning frameworks where mathematical operations are organized into a directed acyclic graph (DAG) before being executed, so the runtime sees the whole…
Graph Machine Learning Models
Graph machine learning models are neural networks designed to operate on data structured as graphs, where the input is a set of nodes connected by edges rather than a grid like an image or a sequence like text.
Greedy Policy
In reinforcement learning, a greedy policy is a decision rule that, in every state, selects the action with the highest estimated value, formally the action that maximizes the action-value function $$Q(s, a)$$…
Grokking
Grokking, also called delayed generalization, is a phenomenon in deep learning where a neural network first memorizes its training data (achieving near-perfect training accuracy but random-level test…
Ground Truth
Ground truth is verified, correct information that serves as the authoritative reference for training and evaluating machine learning models.
Group Attribution Bias
Group attribution bias is the tendency to assume that what is true of one member of a group is true of the entire group
Group Sequence Policy Optimization (GSPO)
Group Sequence Policy Optimization (GSPO) is a reinforcement learning algorithm for training large language models, introduced by the Qwen team at Alibaba in July 2025 .
Grouped-Query Attention
Grouped-query attention (GQA) is an attention mechanism for transformer language models that partitions the query heads into a small number of groups, where every query head in a group shares one key…
HNSW
Hierarchical Navigable Small World (HNSW) is an approximate nearest-neighbor search algorithm that indexes high-dimensional vectors in a multi-layer proximity graph and answers similarity queries in expected…
Hallucination
Hallucination in generative AI is the production of content that is unsupported, contradicted by an applicable source, factually wrong, internally inconsistent, or otherwise presented without an adequate basis.
Hashing
Hashing in machine learning is the use of hash functions to map data of arbitrary size (a word, a URL, a document, a vector) into a fixed-size integer range, trading a small and controllable amount of…
Heuristic
A heuristic is a practical problem-solving approach that trades optimality, completeness, accuracy, or precision for speed, producing a good-enough answer when an exact method would be too slow, too expensive…
Hidden Layer
A hidden layer is a layer of artificial neurons in a neural network that sits between the input layer and the output layer
Hidden Markov Model
A Hidden Markov Model (HMM) is a statistical model of sequential data in which an unobserved (hidden) sequence of discrete states follows a Markov process
Hierarchical Clustering
Hierarchical clustering is an unsupervised learning method that groups data into a tree of nested clusters, building the hierarchy by repeatedly merging the most similar groups (agglomerative, bottom-up) or…
Hinge Loss
Hinge loss is the margin-based loss function defined as max(0, 1 - y * f(x)), used to train support vector machines (SVMs) and other maximum-margin classifiers, where y in {+1, -1} is the true label and f(x)…
History of Artificial Intelligence
The history of artificial intelligence is the story of a scientific field that has cycled between extravagant promises, painful funding collapses, and genuine breakthroughs since the mid-twentieth century.
Holdout data
Holdout data is a portion of a labeled dataset that is deliberately kept out of training so it can be used later to evaluate how well a model performs on examples it has never seen.
Homomorphic encryption for machine learning
Homomorphic encryption for machine learning is the application of fully, somewhat, or leveled homomorphic encryption (FHE, SHE, LHE) so that a server can run machine learning computations, almost always…
Hopfield Network
A Hopfield network is a form of recurrent neural network that stores information as stable patterns in a system of interconnected binary units and retrieves it by content rather than by address.
HuBERT
HuBERT (Hidden-Unit BERT) is a self-supervised learning model for speech representation, introduced by researchers at Meta AI (then Facebook AI Research) in 2021 .
Hugging Face
Hugging Face is an artificial intelligence company and collaboration platform for machine learning.
Human-in-the-loop
Human-in-the-loop (HITL) describes any arrangement in which a person is a required participant in an automated system's operating cycle rather than a bystander to it.
Hyperparameter
A hyperparameter is an input that configures a machine learning algorithm or modeling pipeline, rather than an output fitted by that algorithm in one training run.
Hyperparameter Tuning
Hyperparameter tuning (also called hyperparameter optimization or hyperparameter search) is the process of finding the configuration parameters that make a machine learning model perform best, by searching…
Hyperplane
A hyperplane is a flat, affine subspace of dimension n-1 embedded in an n-dimensional space, defined by the linear equation $$w \cdot x + b = 0$$, where w is a normal vector and b is a scalar offset .
I-JEPA
I-JEPA (Image-based Joint-Embedding Predictive Architecture) is a self-supervised learning method for computer vision developed by Meta AI.
ICML
ICML, the International Conference on Machine Learning, is one of the three top-tier academic conferences in machine learning, alongside NeurIPS and ICLR, and is organized annually by the International Machine…
Image Recognition
Image recognition is the field of artificial intelligence and computer vision that enables machines to identify, classify, and interpret the objects, patterns, and features contained in a digital image or…
Image classification
Image classification is the task of assigning a whole image to one category drawn from a fixed set of labels.
Image segmentation
Image segmentation is the computer vision task of partitioning a digital image into multiple regions by assigning every pixel a label, producing a pixel-level map of what each part of the image contains.
Image-to-Text Models
Image-to-text models are machine learning systems that take an image as input and produce natural language text as output.
ImageNet
ImageNet is a large, hierarchically organized image dataset created to support research in computer vision, especially object recognition.
Imbalanced Dataset
An imbalanced dataset is a dataset used in machine learning where the classification categories are not approximately equally represented, so that one class (the majority class) contains far more samples than…
Imitation Learning
Imitation learning is a family of methods for learning sequential behavior from demonstrations.
Implicit Bias
Implicit bias is an umbrella term that, in artificial intelligence and machine learning, refers to systematic tendencies operating below the surface of explicit design choices.
In-Context Learning
In-context learning (ICL) is the ability of a large language model to learn a new task at inference time by conditioning on a prompt that contains a few input-output examples (demonstrations)
In-Group Bias
In-group bias (also called in-group favoritism or in-group preference) is the systematic tendency to favor members of one's own social group over members of other groups
In-set condition
An in-set condition is a split condition used inside a decision tree node that tests whether the value of a single categorical feature belongs to a specified subset of that feature's possible values.
InclusionAI
inclusionAI is an open-source artificial general intelligence (AGI) research initiative established by Ant Group, the financial-technology affiliate of the Alibaba ecosystem.
Incompatibility of Fairness Metrics
The incompatibility of fairness metrics (also called the impossibility theorem of fairness or fairness trade-offs) is the proven mathematical result that several widely used definitions of algorithmic fairness…
Independently and Identically Distributed (i.i.d.)
Independently and identically distributed (abbreviated i.i.d., iid, or IID) data is a collection of random variables in which each variable has the same probability distribution as the others and all variables…
Individual Fairness
Individual fairness is the principle in machine learning that any two individuals who are similar with respect to a task should receive similar algorithmic outcomes.
Inductive bias
Inductive bias (also called learning bias) is the set of assumptions that a learning algorithm uses to predict outputs for previously unseen inputs.
Inference
In machine learning, inference is the execution of a trained model to produce an output from an input.
Inference optimization
Inference optimization is the set of techniques that make running a trained artificial intelligence model, especially a large language model, faster, more memory-efficient, and cheaper to serve in production.
Inference path
The inference path is the sequence of nodes that a single example visits as it travels from the root node of a decision tree down to a leaf node during prediction.