A*
A* (pronounced "A-star") is a best-first graph search algorithm that finds a least-cost path from a start node to a goal node.
Algorithms
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Showing 1-37 of 37 articles
Actor-critic methods
Actor-critic methods are a family of reinforcement learning algorithms that learn two things at once: a parameterized policy, called the actor, which chooses actions, and a value function, called the critic
AlphaDev
AlphaDev is an artificial intelligence system built by Google DeepMind that used deep reinforcement learning to discover faster algorithms for common computing tasks, most notably small-scale sorting and…
BM25 (Okapi BM25)
BM25, also called Okapi BM25 or Best Match 25, is a probabilistic ranking function used by search engines and information retrieval systems to estimate how relevant a document is to a given query.
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…
CatBoost
CatBoost is an open-source gradient boosted decision trees library developed by Yandex and released to the public on July 18, 2017 .
Conditional Random Field
A conditional random field (CRF) is a discriminative probabilistic model for structured prediction, used most often to assign a label to every position in an input sequence.
Dynamic Programming
Dynamic programming (DP) is an algorithmic technique that solves a complex problem by breaking it into simpler overlapping subproblems, solving each subproblem once, and storing the result so it is never…
Feature Selection
Feature selection is the process of choosing a subset of the most relevant input variables (features) from a larger candidate pool for use in a machine learning model, with the goal of finding the smallest set…
Flash Attention 3
Flash Attention 3 (FA3, styled FlashAttention-3) is the third generation of the FlashAttention algorithm
Flash-Decoding
Flash-Decoding is an inference-time variant of the FlashAttention algorithm that targets the decoding (autoregressive generation) phase of large language model inference
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…
Kalman Filter
The Kalman filter is a recursive algorithm that estimates the hidden state of a dynamic system from a sequence of noisy measurements.
LightGBM
LightGBM (short for Light Gradient-Boosting Machine) is a free and open-source gradient boosting framework that trains ensembles of decision trees on tabular data, originally developed at Microsoft Research by…
Lion (optimizer)
Lion (EvoLved Sign Momentum) is a stochastic optimizer for training deep neural networks, introduced by researchers at Google in the February 2023 paper "Symbolic Discovery of Optimization Algorithms" by…
Lookahead Decoding
Lookahead Decoding is a parallel decoding algorithm for accelerating inference in large language models, introduced in November 2023 by Yichao Fu, Peter Bailis, Ion Stoica, and Hao Zhang from the Hao AI Lab at…
Markov Chain Monte Carlo
Markov Chain Monte Carlo (MCMC) is a class of algorithms for drawing samples from a probability distribution by constructing a Markov chain whose stationary distribution equals the target distribution .
Matrix multiplication
Matrix multiplication combines an M by K matrix A with a K by N matrix B to produce an M by N matrix C
Monte Carlo Tree Search
Monte Carlo Tree Search (MCTS) is a heuristic search algorithm for sequential decision-making that finds strong actions by running many simulated playthroughs of a problem and aggregating their outcomes into a…
Motion planning
Motion planning is the computational problem of finding a sequence of valid configurations or controls that moves a robot (or another articulated or dynamical system) from a start state to a goal state while…
Multi-armed bandit
A multi-armed bandit is a sequential decision problem in which a learner repeatedly picks one action from a fixed set, receives a random reward for that action only, and tries to accumulate as much reward as…
Nash equilibrium
A Nash equilibrium is a combination of strategies, one for each player in a game, such that no player can raise their own payoff by changing strategy alone while everyone else keeps theirs unchanged.
Particle Filter
A particle filter is a simulation-based method for estimating the changing, unobserved state of a system from a sequence of noisy observations.
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.
Quantum computing
Quantum computing is a model of computation that stores and manipulates information in quantum mechanical systems, using superposition, entanglement, and interference to solve certain problems with far fewer…
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…
Shapley value
The Shapley value is a rule from cooperative game theory for dividing the payoff of a group among its members.
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.
Simultaneous Localization and Mapping (SLAM)
Simultaneous Localization and Mapping (SLAM) is the computational problem of building a map of an unknown environment while at the same time estimating the position of a sensor, vehicle, or agent moving…
Sketching
Sketching is a family of techniques in computer science, machine learning, and database systems that use small probabilistic data structures, called sketches
Soft Actor-Critic
Soft Actor-Critic (SAC) is an off-policy, maximum-entropy deep reinforcement learning algorithm that trains a stochastic actor-critic to maximize expected reward plus the entropy of its own policy, so the…
TF-IDF (Term Frequency-Inverse Document Frequency)
TF-IDF (term frequency-inverse document frequency) is a numerical statistic that measures how important a word is to a single document within a larger collection or corpus, computed as the product of two…
Temporal-difference learning
Temporal-difference (TD) learning is a class of model-free reinforcement learning methods that learn value-function estimates by bootstrapping: updating each estimate of how good a state is toward a target…
Top-k sampling
Top-k sampling is a decoding strategy for autoregressive language models that restricts each generation step to the k most probable next tokens.
Twin Delayed DDPG
Twin Delayed Deep Deterministic Policy Gradient (TD3) is an off-policy actor-critic reinforcement learning algorithm for continuous action spaces, introduced by Scott Fujimoto, Herke van Hoof, and David Meger…
XGBoost
XGBoost (short for eXtreme Gradient Boosting) is an open-source software library that implements an optimized, distributed version of gradient boosted decision trees, and it is one of the most widely used…
k-Nearest Neighbors
k-Nearest Neighbors (often abbreviated k-NN or KNN) is a non-parametric, instance-based supervised learning algorithm that classifies a new data point by finding the k most similar examples in the training set…