Generalization
Generalization in machine learning is the ability of a trained model to perform accurately on new, unseen data drawn from the same distribution as its training set
Deep Learning
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Generative Model
A generative model is a class of statistical and machine learning model that learns the joint probability distribution P(X) of the observed data, or the joint distribution P(X, Y) of inputs and labels
Generative adversarial network
Generative adversarial networks (GANs) are a family of generative models trained through competition between two learned functions.
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.
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 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 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 checkpointing
Gradient checkpointing, also called activation checkpointing, activation recomputation, or rematerialization, is a memory-saving technique for training deep neural networks that trades extra compute for much…
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 Neural Network
A Graph Neural Network (GNN) is a class of neural_network that operates directly on graph-structured data, meaning data made of nodes (entities) connected by edges (relationships), by repeatedly passing…
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…
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…
H-Net (dynamic chunking)
H-Net, short for Hierarchical Network, is a tokenizer-free neural sequence model that learns to segment raw bytes into content-adaptive "chunks" as part of ordinary end-to-end training, rather than relying on…
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
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.
Hyena
Hyena is a sub-quadratic, attention-free neural sequence operator that replaces the self-attention operator of the Transformer with a recurrence of long, implicitly parameterized convolutions and…
Hymba
Hymba is a hybrid-head neural-network architecture for small language models introduced by NVIDIA researchers in November 2024.
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.
IP-Adapter
IP-Adapter (short for Image Prompt Adapter) is a lightweight neural network module that adds image-prompt conditioning to a pretrained text-to-image diffusion model, allowing a reference image to guide…
Ian Goodfellow
Ian Goodfellow is an American computer scientist and machine learning researcher best known for inventing the generative adversarial network (GAN) in 2014 and for being the lead author of the textbook Deep…
Ilya Sutskever
Ilya Sutskever (born 1986) is a Russian-born computer scientist who was raised in Israel and moved to Canada as a teenager.
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.
ImageNet
ImageNet is a large, hierarchically organized image dataset created to support research in computer vision, especially object recognition.
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)
Inception (deep learning)
Inception is a family of convolutional neural network (CNN) architectures developed by researchers at Google, first introduced in 2014.
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.
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 segmentation
Instance segmentation is the computer vision task of detecting every object instance in an image and producing a pixel-precise mask for each one
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
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
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.
John Hopfield
John Joseph Hopfield (born July 15, 1933) is an American physicist whose work spans condensed-matter physics, molecular biology, and the theory of neural computation.
Jonathan Ho
Jonathan Ho is a machine learning researcher best known as the lead author of "Denoising Diffusion Probabilistic Models" (DDPM), the 2020 paper that made diffusion models practical for high quality image…
Jürgen Schmidhuber
Jürgen Schmidhuber (anglicized Jurgen Schmidhuber, born 17 January 1963) is a German computer scientist best known as a co-inventor of long short-term memory (LSTM), the recurrent neural network architecture…
KV Cache
KV cache, short for key-value cache, is transient model state used during Transformer generation.
Kaiming He
Kaiming He is a Chinese computer scientist known for foundational work in computer vision and deep learning, and is most closely associated with deep residual networks, or ResNet.
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.
Knowledge Distillation
Knowledge distillation is a training method in which a student model learns from signals produced by a teacher model.
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).
Koray Kavukcuoglu
Koray Kavukcuoglu is a Turkish computer scientist who leads Google DeepMind as its Senior Vice President, a role he assumed on 5 August 2026, and serves as Chief AI Architect of Google .
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 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…
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 diffusion model
A latent diffusion model (LDM) is a type of diffusion model that runs the denoising diffusion process in a compressed latent space learned by a pretrained autoencoder, rather than directly in pixel space…
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 normalization
Layer normalization is a technique for normalizing the activations of a neural network across the feature dimension of each individual sample, rather than across a batch of samples.
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…
LeNet
LeNet is the pioneering family of convolutional neural networks developed by Yann LeCun and collaborators at AT&T Bell Labs between roughly 1988 and 1998 to read handwritten characters
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.
Lightning Attention
Lightning Attention is an IO-aware (input/output aware) implementation of linear attention that lets the method reach its theoretical linear-time complexity in practice
LoRA (Low-Rank Adaptation)
Low-Rank Adaptation, usually abbreviated LoRA, is a parameter-efficient fine-tuning method for adapting a pre-trained model.
Logits
In machine learning and statistics, logits refer to the raw, unnormalized scores output by a model before they are converted into probabilities.
Long Short-Term Memory (LSTM)
Long short-term memory (LSTM) is a gated recurrent neural network architecture for processing sequences.
Loss Curve
A loss curve is a plot that shows the value of a loss function over the course of training a machine learning model.
Loss Function
A loss function assigns a numerical cost to a prediction, decision, or action after an outcome is observed.