Return (Reinforcement Learning)
In reinforcement learning, the return…
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Showing 661-720 of 873 articles
Reward
In reinforcement learning (RL), a reward is a scalar feedback signal that an environment sends to an agent after each action, quantifying how desirable that action's outcome was
Reward hacking
Reward hacking (also called specification gaming) is a failure mode in artificial intelligence in which a system maximizes its given objective or reward signal through unintended shortcuts, exploits, or…
Ridge Regression
Ridge regression is a method of estimating the coefficients of a linear regression model by adding a penalty proportional to the sum of squared coefficients (the squared L2 norm) to the ordinary least squares…
Ridge Regularization
Ridge regularization, also known as L2 regularization or Tikhonov regularization, is a technique in statistics and machine learning that adds a squared L2-norm penalty to a model's loss function.
RoBERTa
RoBERTa (Robustly Optimized BERT Pretraining Approach) is an open-source natural language processing model released in July 2019 by researchers at Facebook AI (now Meta AI) and the University of Washington…
Robot learning
Robot learning studies how robots acquire or improve behavior from data and experience.
Root
In machine learning, the root is the starting node of a decision tree: the single topmost node that holds the entire training set and encodes the first condition the tree evaluates, at depth 0.
Root Mean Squared Error (RMSE)
Root Mean Squared Error (RMSE), also known as root mean square deviation (RMSD), is a regression evaluation metric equal to the square root of the average of the squared differences between predicted and…
Root directory
In machine learning, a root directory is the top level folder, on a local disk or in object storage, under which a training run writes the artifacts it produces and a serving run later reads: checkpoints…
Rotary Position Embedding
Rotary Position Embedding (RoPE) is a positional encoding method for transformer models that encodes a token's absolute position by rotating its query and key vectors in two-dimensional subspaces
Rotational invariance
Rotational invariance is the property of a model, function, or feature whose output does not change when the input is rotated.
SARSA (State-Action-Reward-State-Action)
SARSA (State-Action-Reward-State-Action) is an on-policy temporal-difference (TD) control algorithm that learns the action-value function Q^pi(s, a) of the policy an agent is actually following
SGLang
SGLang (short for Structured Generation Language) is a high-performance, open-source serving framework for large language models and multimodal models, originally developed at UC Berkeley's Sky Computing Lab…
SMOTE (Synthetic Minority Over-sampling Technique)
SMOTE (Synthetic Minority Over-sampling Technique) is a data preprocessing algorithm that fixes class imbalance by creating new synthetic minority-class examples through interpolation between existing minority…
STaR (Self-Taught Reasoner)
STaR (Self-Taught Reasoner) is a self-training method that teaches a large language model to reason by having it generate its own chain-of-thought rationales, keeping only the rationales that lead to correct…
SUPERB
SUPERB, which stands for Speech processing Universal PERformance Benchmark, is a comprehensive evaluation framework designed to measure how well self-supervised learning (SSL) models generalize across a…
Safetensors
Safetensors is an open-source tensor serialization format developed by Hugging Face that stores machine learning model weights as raw tensor data plus a small JSON header
Sam McCandlish
Sam McCandlish is an AI researcher and a co-founder of Anthropic, the artificial intelligence company behind the Claude family of language models.
Sampling Bias
Sampling bias is a systematic error in statistics and machine learning that occurs when a sample is collected so that some members of the intended population have a higher or lower probability of being…
Sanjay Ghemawat
Sanjay Ghemawat (born 1966) is an American computer scientist and software engineer, best known for co-creating the core distributed-systems infrastructure that powered Google's rise, including the Google File…
Saver
In machine learning, a Saver is a utility or class that persists and restores the state of a model, its variables, and its optimizer to disk, so training can be paused, resumed, evaluated, or deployed without…
Scalar
A scalar is a single numerical value, a quantity with magnitude but no direction, and the simplest object in linear algebra and machine learning.
Scaling
In machine learning and artificial intelligence, scaling is an umbrella term for several distinct practices: (1) feature scaling, the data-preprocessing step that rescales input features to a common range so…
Scaling Laws
Neural scaling laws are empirical relationships that describe how a measured outcome changes as a neural network, its training data, or the computation used to train it becomes larger.
Scaling Laws for Neural Language Models
Scaling Laws for Neural Language Models is a landmark research paper published by OpenAI on January 23, 2020 (arXiv:2001.08361) that established that the test loss of a neural language model falls as a smooth…
Scikit-learn
Scikit-learn is a free and open-source machine learning library for Python. It provides a common interface for fitting, transforming, predicting, evaluating, and selecting models, with implementations covering…
Score matching
Score matching is a method for fitting a probabilistic model by matching the gradient of its log-density.
Scoring
Scoring is the process of applying a trained machine learning model to data so it produces an output number, either a prediction (probability, class score, or ranking value) emitted at inference time, or a…
Selection Bias
Selection bias is a systematic error that occurs when the data used for analysis, training, or evaluation does not accurately represent the population or domain it is intended to describe
Selective Language Modeling (Rho-1)
Selective Language Modeling (SLM) is a pretraining objective for language models that applies the training loss to only a chosen subset of tokens rather than to every token in the corpus.
Self-RAG
Self-RAG (Self-Reflective Retrieval-Augmented Generation) is a framework that trains a single large language model to adaptively decide when to retrieve external passages, to generate text grounded in those…
Self-Supervised Learning
Self-supervised learning (SSL) is a machine learning approach in which a model learns representations from unlabeled data by generating its own supervisory signal from the structure of the data itself
Self-attention
Self-attention is a neural network operation in which each position forms a data-dependent mixture of information from positions in the same input sequence or set.
Self-speculative decoding (LayerSkip)
Self-speculative decoding is a family of speculative decoding methods that accelerate large language model (LLM) inference by using the target model itself, run in a cheaper reduced-depth mode
Self-training
Self-training is a semi-supervised learning procedure in which a model trained on a small labeled set is used to generate predictions on unlabeled data
Semantic search
Semantic search is an information retrieval approach that finds results based on the meaning and intent behind a query rather than relying solely on exact keyword matches.
Semi-Supervised Learning
Semi-supervised learning is a machine learning approach that trains a model on a small amount of labeled data together with a large amount of unlabeled data
Sensitive Attribute
A sensitive attribute (also called a protected attribute or protected characteristic) is any feature in a dataset that corresponds to a legally or ethically protected personal trait, such as race, sex or…
Sensor fusion
Sensor fusion is the process of combining data from multiple sensors, often of different types, to produce information that is more accurate, complete, or reliable than any single sensor could provide on its…
SentencePiece
SentencePiece is an open-source, language-agnostic subword tokenization library and detokenizer developed at Google and introduced by Taku Kudo and John Richardson in their 2018 EMNLP system demonstration…
Sentiment Analysis
Sentiment analysis, also called opinion mining or emotion AI, is the use of natural language processing to automatically determine the sentiment, emotion, or opinion expressed in a piece of text, typically…
Sequence Model
A sequence model is a class of machine learning models that processes, generates, or predicts ordered data where the position and surrounding context of each element carry meaning.
Sequence-to-Sequence Task
A sequence-to-sequence (seq2seq) task is any machine learning problem in which a model receives a variable-length input sequence and produces a variable-length output sequence, where the input and output may…
Serving
In machine learning, serving (also called model serving or inference serving) is the process of deploying a trained model into a production environment so it can answer prediction requests, usually exposed as…
Shape (Tensor)
In machine learning, the shape of a tensor is the tuple of integers giving the size of the tensor along each of its axes.
Shapley value
The Shapley value is a rule from cooperative game theory for dividing the payoff of a group among its members.
Shrinkage
Shrinkage in machine learning and statistics is a regularization technique that deliberately pulls model coefficient estimates toward zero, or toward some other fixed shrinkage target
Sigmoid Function
The sigmoid function is a mathematical function that maps any real number to a value between 0 and 1 using the formula…
SimCLR
SimCLR (Simple Framework for Contrastive Learning of Visual Representations) is a self-supervised learning method for computer vision in which a network is trained to recognise that two differently augmented…
Similarity Measure
A similarity measure (also called a similarity function or similarity metric) is a real-valued function that quantifies the degree of resemblance between two objects, returning a large value when the objects…
Simulated annealing
Simulated annealing is a probabilistic method for finding a good approximate solution to a global optimization problem, especially one with many local optima that trap ordinary hill-climbing methods.
Singular value decomposition
Singular value decomposition (SVD) is a matrix factorization that writes any real or complex m x n matrix…
Sleep-time compute
Sleep-time compute is a technique for large language model inference in which a model uses idle periods, before any user query has arrived, to "think" about a known context offline and pre-compute a richer…
Small language model
A small language model (SLM) is a compact large language model, typically under about 10 billion parameters, built to run efficiently on a single consumer GPU, a laptop, or a smartphone rather than in a data…
Snorkel
Snorkel is an open-source software framework and methodology for programmatic data labeling that started at Stanford University in 2015 and is licensed under Apache 2.0.
Social Media
Social media comprises internet-based services through which people and organizations create, distribute, discover, and respond to content in networked publics.
Softmax
Softmax is a function that converts a finite vector of real-valued scores into a vector of positive numbers that sum to one.
Sparse Coding
Sparse coding is a representation learning principle in which a signal is encoded as a linear combination of a small number of elements drawn from a (usually overcomplete) dictionary of atoms.
Sparse Feature
A sparse feature is a feature in machine learning whose values are predominantly zero or empty across a dataset.