Bayesian network
A Bayesian network (also called a belief network, Bayes net, directed graphical model, or probabilistic causal network) is a probabilistic graphical model that represents a set of random variables and their…
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Showing 61-120 of 858 articles
Behavioral Cloning
Behavioral cloning is the approach to imitation learning that reduces control to a supervised learning problem.
Bellman Equation
The Bellman equation is a recursive formula stating that the value of a state equals the immediate reward received there plus the discounted value of the next state
Best-of-N sampling
Best-of-N sampling (BoN) is an inference-time method that improves a large language model output by drawing N independent candidate responses to the same prompt, scoring each with a reward model, verifier, or…
Bias
Bias in artificial intelligence carries three distinct technical meanings: a learnable scalar parameter added inside a neuron, the systematic error component of an estimator (the "bias" in the bias-variance…
Bias (Math) or Bias Term
The bias term is a learnable additive constant b added to the weighted sum of a neuron's inputs before an activation function is applied, computed as $$z = w \cdot x + b$$.
Bias-variance tradeoff
The bias-variance tradeoff is a foundational concept in machine learning and statistics that describes the tension between two competing sources of error in predictive models: bias (error from overly…
Bigram
A bigram (also written 2-gram) is a contiguous sequence of two adjacent elements, typically two words or two characters, taken from a body of text or speech.
Binary Classification
Binary classification is a supervised learning task in which a model assigns each input to exactly one of two mutually exclusive classes, conventionally labeled the positive class (1) and the negative class…
Binary condition
A binary condition is a test at a node of a decision tree that has exactly two possible outcomes, typically yes or no (equivalently true or false), routing an example down one of two branches.
Bioinformatics
Bioinformatics is the branch of science that builds computational methods, databases, and software for storing, searching, and interpreting biological data, above all the sequences of DNA, RNA, and proteins…
Bittensor
Bittensor is a decentralized machine learning network that uses blockchain-based incentives to pay independent contributors for producing digital commodities such as model inference, training, data, and raw…
Boosting
Boosting is an ensemble learning method in machine learning that trains a sequence of weak learners, each one correcting the errors of its predecessors, and combines them into a single strong learner with high…
Bounding Box
A bounding box is a rectangular region defined by a set of coordinates that encloses an object of interest within an image, video frame, or three-dimensional space.
Broadcasting
Broadcasting is the set of rules that lets element-wise operations (addition, subtraction, multiplication, division) act on arrays or tensors of different but compatible shapes by virtually stretching the…
Bucketing
Bucketing, also called binning or discretization, is a feature engineering technique in machine learning that converts a continuous feature into a small number of discrete categories by dividing its range of…
Byte-Pair Encoding
Byte-pair encoding (BPE) is a subword tokenization algorithm that splits text into tokens by starting from individual characters or bytes and iteratively merging the most frequent adjacent pair into a new token
CART algorithm
The CART algorithm (Classification And Regression Trees) is a non-parametric supervised learning method that builds a binary decision tree from labelled training data, using Gini impurity to split…
CIDEr
CIDEr (Consensus-based Image Description Evaluation) is an automatic evaluation metric for image captioning that scores a machine-generated caption by how closely it matches the consensus of several human…
CLIP (Contrastive Language-Image Pre-training)
CLIP, short for Contrastive Language-Image Pre-training, is a family of neural networks developed by OpenAI researchers to learn a shared representation of images and natural-language descriptions.
COCO dataset
COCO (Common Objects in Context) is a large-scale dataset for object detection, image segmentation, keypoint detection, and image captioning.
CRUXEval
CRUXEval (Code Reasoning, Understanding, and eXecution Evaluation) is a benchmark designed to measure how well large language models can reason about, understand, and mentally execute short Python programs.
Calibration (machine learning)
Calibration in machine learning is the property that the probability scores produced by a probabilistic classifier match the empirical frequency of the predicted event: a model that assigns a confidence of 0.8…
Calibration Layer
A calibration layer is a post-prediction adjustment appended to a trained machine learning model that rescales its raw output scores or predicted probabilities so they better reflect the true likelihood of…
Candidate Generation
Candidate generation is the first stage in a multi-stage recommendation system or information retrieval pipeline.
Candidate Sampling
Candidate sampling is a family of training-time optimization techniques used in machine learning to reduce the computational cost of models that must choose among a very large number of output classes.
Candle (HuggingFace Rust ML)
Candle is a minimalist machine learning framework written in pure Rust and published by Hugging Face under the huggingface/candle GitHub repository.
CatBoost
CatBoost is an open-source gradient boosted decision trees library developed by Yandex and released to the public on July 18, 2017 .
Categorical Data
Categorical data, also called qualitative data, is data whose values are discrete labels or groups (such as colors, country names, or blood types) rather than measurable quantities, so they cannot be…
Causal Language Model
A causal language model (CLM), also called an autoregressive language model or a decoder-only language model, is a language model that predicts the next token in a sequence using only the tokens that precede…
Causal inference
Causal inference is the field of study concerned with drawing conclusions about cause-and-effect relationships from data, answering questions of the form "what would happen to outcome Y if we intervened on…
Causal scrubbing
Causal scrubbing is a methodology in mechanistic interpretability for rigorously and quantitatively testing hypotheses about the internal computational structure of a neural network.
Centroid
A centroid is the geometric center of a set of points, computed as the arithmetic mean of their coordinates: each component of the centroid is the average of that coordinate across all the points.
Centroid-based clustering
Centroid-based clustering is a family of machine learning algorithms that group data by representing each cluster with a single prototype point, called a centroid, and assigning every point to whichever…
Chain of Thought Monitorability
Chain of thought monitorability is the property that lets safety researchers read a reasoning model's chain-of-thought (CoT), the step-by-step working it writes out in human language before answering, and…
Chain-of-Thought
Chain-of-thought (CoT) is a sequence of intermediate steps that a language model generates before its final answer.
Checkpoint
In machine learning, a checkpoint is a saved snapshot of a model's state captured at a specific point during the training process, used to resume training, recover from failures, or deploy and fine-tune the…
Chelsea Finn
Chelsea Finn (born October 8, 1992) is an American computer scientist, an assistant professor of computer science and electrical engineering at Stanford University, and a co-founder of the robotics company…
Chinchilla scaling laws
The Chinchilla scaling laws are a set of empirical findings published by DeepMind researchers in 2022 showing that, for a fixed compute budget, a large language model trains most efficiently when its number of…
Chunked prefill
Chunked prefill is a scheduling technique for large language model serving that splits the processing of a long input prompt (the prefill) into smaller, fixed-size token chunks and combines each chunk with the…
Circuit Breakers (Representation Rerouting)
Circuit Breakers are an AI safety method, introduced in 2024, that aims to make a large language model (LLM) or multimodal model robust to harmful generations by intervening directly on the model's internal…
Class
In machine learning, a class is one of the discrete categories that a classification model can assign to an input.
Class-Imbalanced Dataset
A class-imbalanced dataset is a dataset in which the distribution of examples across the target classes is significantly unequal, so that one class (the majority class) contains far more samples than one or…
Classification (machine learning)
Classification is a supervised learning task in which a system uses labeled examples to assign one or more discrete labels to an input.
Classification Threshold
A classification threshold (also called a decision threshold or cut-off point) is a numeric value used to convert the continuous probability output of a classification model into a discrete class label.
Claude Sonnet 4.5
Claude Sonnet 4.5 is a multimodal large language model (LLM) developed by Anthropic and released on September 29, 2025, which Anthropic described at launch as "the best coding model in the world." It is a…
Cleanlab
Cleanlab is an open source Python library for automatically finding and fixing label errors and other data quality problems in machine learning datasets, and the data-centric AI startup, incorporated in 2021
Clipping
Clipping is a family of techniques in machine learning that constrain numerical values to lie within a specified range or below a specified magnitude.
Cloud TPU
Cloud TPU is Google Cloud's offering of Tensor Processing Units (TPUs), the family of custom application-specific integrated circuits (ASICs) that Google builds to accelerate machine learning training and…
Clustering
Clustering is an unsupervised learning technique that groups a set of data points into clusters so that points in the same cluster are more similar to each other than to points in other clusters
Co-Adaptation
Co-adaptation in neural networks refers to a phenomenon in which different hidden units develop highly correlated behavior, becoming excessively dependent on one another rather than learning independent
Co-Training
Co-training is a semi-supervised learning algorithm that leverages both labeled and unlabeled data by training two classifiers on two distinct "views" of the data, allowing them to teach each other iteratively.
Coconut (Chain of Continuous Thought)
Coconut (Chain of Continuous Thought) is a reasoning paradigm for large language models introduced by researchers at FAIR at Meta, Meta's Fundamental AI Research lab, and the University of California
CodeContests
CodeContests is a competitive programming dataset created by Google DeepMind for training and evaluating machine learning models on algorithmic problem-solving tasks.
Collaborative filtering
Collaborative filtering (CF) is a family of techniques used in recommendation systems that predicts a user's preferences by collecting and analyzing preference signals from many other users.
Common Crawl
Common Crawl is a nonprofit 501(c)(3) organization that maintains a free, open repository of web crawl data, and it is the single largest publicly available source of text used to train large language models.
Compound AI System
A compound AI system is an AI system that achieves its objectives by combining multiple interacting components, such as large language models, retrieval mechanisms, external tools, guardrails, and…
Computer Vision
Computer vision is the study of computational methods that extract, estimate, or generate useful representations from visual measurements.
Computer-use agent
A computer-use agent (CUA) is a category of AI agent in artificial intelligence that performs tasks by directly operating a general-purpose computer's graphical user interface (GUI) the way a human does, by…
Concept drift
Concept drift is the change over time in the statistical relationship between a model's inputs and its target, formally when the joint distribution P(X, Y) (and in the most damaging case the conditional P(Y |…