Probabilistic Graphical Model
A probabilistic graphical model (PGM) is a mathematical representation in which a graph expresses the factorization and conditional-independence structure of a probability distribution.
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Probabilistic Regression Model
A probabilistic regression model (also called distributional regression) is a regression model that outputs a full probability distribution over possible target values rather than a single point estimate.
Process reward model (PRM)
A process reward model (PRM), also called a process-supervised reward model or step-level verifier, is a learned scoring model that evaluates the correctness or quality of each intermediate step in a large…
Program Synthesis
Program synthesis is the task of automatically constructing a program that satisfies a specification expressed at a higher level than the code itself: a logical formula, a set of input-output examples, a…
Prompt Caching
Prompt caching is a large language model (LLM) inference optimization that stores the computed key-value (KV) state of a repeated prompt prefix so it can be reused across API calls, cutting both cost and…
Prompt Engineering
Prompt engineering is the systematic design and testing of the inputs supplied to a generative model so that the model is more likely to produce a useful result.
Prompt lookup decoding
Prompt lookup decoding (PLD), also called n-gram speculative decoding, is an inference acceleration method for large language models that speeds up text generation without changing the model or its outputs.
Protein design
Protein design is the construction of new protein sequences that fold into an intended three-dimensional shape and perform an intended job, such as binding a chosen target, catalyzing a reaction, or assembling…
Protein folding
Protein folding is the physical process by which a polypeptide chain, a linear sequence of amino acids, acquires its functional three-dimensional structure, and the long-standing scientific challenge of…
Protein language model
A protein language model (pLM) is a neural network trained on raw amino acid sequences using the same self-supervised objectives that power text models, with individual amino acids serving as tokens instead of…
Proximal Policy Optimization (PPO)
Proximal Policy Optimization (PPO) is an on-policy policy gradient reinforcement learning algorithm that stabilizes training by clipping the policy update so the new policy stays close ("proximal") to the old…
Proxy labels
A proxy label (also called a surrogate label) is an observable, easy-to-collect stand-in for the true target a model should predict, used to train that model when the real target is too expensive, too slow, or…
Pruning
Pruning is a family of techniques used in machine learning and artificial intelligence to remove parts of a model or search space that are estimated to be unnecessary for accuracy or optimality.
PyTorch
PyTorch is an open-source software library for tensor computation and machine learning.
Q-Function
The Q-function, also called the action-value function or state-action value function and written…
Q-Learning
Q-learning is a model-free, off-policy reinforcement learning algorithm that learns the value of taking a given action in a given state by iteratively updating an action-value function Q(s, a), the expected…
QuIP / QuIP#
QuIP (Quantization with Incoherence Processing) is a family of weight-only post-training quantization methods for large language models developed in the RelaxML group at Cornell University
Quantile
A quantile is a cut point that divides a probability distribution or a sorted dataset into intervals containing equal portions of the probability or the observations.
Quantile bucketing
Quantile bucketing, also called quantile binning, equal-frequency binning, or quantile discretization
Quantization
Quantization is the representation of values from a large or continuous set by values from a smaller, usually finite set.
Quantization-Aware Training (QAT)
Quantization-aware training (QAT) is a model compression technique in which the effects of quantization are simulated during the training or fine-tuning of a neural network, so that the model learns parameter…
Quantum machine learning
Quantum machine learning (QML) is a research field that sits at the intersection of quantum computing and machine learning.
Question answering
Question answering (QA) is the task of producing an answer to a question, usually expressed in natural language.
Queue
A queue in machine learning is a First-In-First-Out (FIFO) data structure that stages and buffers data between an input/output stage and a compute stage so that elements are processed in the order they were…
Quiet-STaR
Quiet-STaR is a self-supervised training method that teaches a large language model to generate short, token-level internal "thoughts," or rationales, that help it predict the text that follows
R (programming language)
R is a free, open-source programming language and software environment for statistical computing and graphics.
RAPTOR
RAPTOR (Recursive Abstractive Processing for Tree-Organized Retrieval) is a retrieval method for retrieval-augmented generation introduced in a 2024 paper by researchers at Stanford University.
RLAIF
Reinforcement Learning from AI Feedback (RLAIF) is a family of alignment techniques for large language models in which the preference labels used to fine-tune a model are produced by another AI system
ROC (Receiver Operating Characteristic) Curve
A Receiver Operating Characteristic (ROC) curve is a graph that measures how well a binary classification system separates two classes by plotting its true positive rate (TPR, also called sensitivity or…
ROUGE
ROUGE (Recall-Oriented Understudy for Gisting Evaluation) is a set of automatic metrics that score the quality of a machine-generated text summary by counting how many overlapping units (n-grams, word…
RWKV
RWKV (pronounced "RwaKuv") is an open-source neural network architecture that combines the parallelizable training of Transformers with the constant-time
Random Forest
A random forest is an ensemble learning method that combines many randomized decision trees. Each tree is built from a randomized view of the training data, and the ensemble aggregates the trees' predictions.
Random Policy
A random policy is a reinforcement learning policy that chooses actions from a fixed probability distribution, most commonly the uniform distribution, independent of the current state and of any learned value…
Rank (Tensor)
In machine learning and deep learning frameworks, the rank of a tensor is the number of dimensions (axes) it has: the count of indices you must supply to pick out a single scalar element.
Ranking
Ranking in machine learning, often called learning to rank (LTR), is the supervised task of ordering a set of items by relevance to a query
Ray (framework)
Ray is an open-source distributed computing framework, developed at the University of California, Berkeley's RISELab and commercialized by Anyscale, that lets developers scale Python and artificial…
ReLU
ReLU, short for rectified linear unit, is an activation function that maps a real input to its positive part:
ReST / ReST-EM (Reinforced Self-Training)
ReST (Reinforced Self-Training) is a family of self-training algorithms for large language models that improve a model by fine-tuning it on its own filtered outputs instead of on additional human-written data.
Reasoning (artificial intelligence)
Reasoning in artificial intelligence is the ability of an AI system to draw inferences, solve problems, and make decisions through structured, multi-step thought rather than a single immediate response.
Reasoning models
Reasoning models are large language models that are post-trained or configured to spend additional inference-time computation on intermediate steps before returning an answer.
Recall (metric)
Recall is a classification and retrieval metric that measures the proportion of actual positive instances a model correctly identifies, defined as TP / (TP + FN)
Recommendation System
A recommendation system (also called a recommender system) is an information filtering system that predicts a user's preferences and ranks the items most likely to interest that user, such as the movies…
Recommender System
A recommender system (also called a recommendation system or recommendation engine) is an information filtering system that predicts the preference a user would give to an item and uses those predictions to…
Rectified Linear Unit (ReLU)
The Rectified Linear Unit (ReLU) is the most widely used activation function in deep learning.
Recurrent Neural Network
A recurrent neural network (RNN) is a neural network whose computation includes a state that is passed from one step to the next.
Recursive self-improvement
Recursive self-improvement (RSI) is a process in which an artificial intelligence system improves its own intelligence or its ability to improve itself, so that each enhancement increases its capacity for…
RedPajama
RedPajama is a family of large-scale, openly available datasets for training large language models (LLMs), created by Together AI with academic and open-source partners to reproduce, in fully open form
Reflexion
Reflexion is a 2023 framework for reinforcing language agents through verbal self-reflection rather than weight updates: the agent reflects in natural language on feedback from failed attempts, stores those…
Regression (statistics and machine learning)
Regression is a family of statistical and machine learning methods for modelling the relationship between a numeric outcome variable and one or more explanatory variables (often called predictors, features, or…
Regularization
Regularization is any deliberate change to a learning problem or training algorithm intended to improve performance on data that were not used to fit the model.
Regularization Rate
"Regularization rate" is the terminology used in Google's Machine Learning Crash Course and Machine Learning Glossary
Reinforcement Learning from Human Feedback (RLHF)
Reinforcement learning from human feedback (RLHF) is a family of methods in which judgments supplied by people are converted into a reward signal and a policy is optimized with reinforcement learning against…
Reinforcement learning
Reinforcement learning (RL) is a branch of machine learning in which an agent learns to make decisions by taking actions in an environment to maximize a cumulative reward signal, discovering good behavior…
Rejection sampling
Rejection sampling, also called the accept-reject method or the acceptance-rejection method, is a Monte Carlo technique that draws independent samples from a hard-to-sample target distribution p(x) by…
Replay Buffer
A replay buffer (also called an experience replay buffer or replay memory) is a fixed-size memory that stores an off-policy reinforcement learning agent's past transitions, each a tuple of state, action…
Reporting Bias
Reporting bias is a type of data bias in machine learning that occurs when the frequency of events, properties, or outcomes captured in a dataset does not reflect their real-world frequency, because people…
Representation
A representation in machine learning is the format in which a data example is encoded before a model processes it: the same image can be represented as raw pixels, a list of hand-designed measurements, or a…
Representation learning
Representation learning is the area of machine learning concerned with getting a system to discover the features it needs for a task, instead of having people specify those features by hand.
Residual connection
A residual connection (also called a skip connection or shortcut connection) is a structural element in neural networks that adds the input of a layer or block directly to its output.
Retrieval-Augmented Generation
Retrieval-augmented generation (RAG) is a family of methods that retrieves information from an external collection and conditions a generative model on that information when producing an output.