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Sense Networks : Sense Networks website CabSense website CitySense website Archived 2010-08-20 at the Wayback Machine |
Simultaneous localization and mapping : Simultaneous localization and mapping (SLAM) is the computational problem of constructing or updating a map of an unknown environment while simultaneously keeping track of an agent's location within it. While this initially appears to be a chicken or the egg problem, there are se... |
Simultaneous localization and mapping : Given a series of controls u t and sensor observations o t over discrete time steps t , the SLAM problem is to compute an estimate of the agent's state x t and a map of the environment m t . All quantities are usually probabilistic, so the objective is to compute P ( m t + 1... |
Simultaneous localization and mapping : Statistical techniques used to approximate the above equations include Kalman filters and particle filters (the algorithm behind Monte Carlo Localization). They provide an estimation of the posterior probability distribution for the pose of the robot and for the parameters of the... |
Simultaneous localization and mapping : Various SLAM algorithms are implemented in the open-source software Robot Operating System (ROS) libraries, often used together with the Point Cloud Library for 3D maps or visual features from OpenCV. |
Simultaneous localization and mapping : A seminal work in SLAM is the research of Smith and Cheeseman on the representation and estimation of spatial uncertainty in 1986. Other pioneering work in this field was conducted by the research group of Hugh F. Durrant-Whyte in the early 1990s. which showed that solutions to S... |
Simultaneous localization and mapping : Probabilistic Robotics by Sebastian Thrun, Wolfram Burgard and Dieter Fox with a clear overview of SLAM. SLAM For Dummies (A Tutorial Approach to Simultaneous Localization and Mapping). Andrew Davison research page at the Department of Computing, Imperial College London about SLA... |
Stockfish (chess) : Stockfish is a free and open-source chess engine, available for various desktop and mobile platforms. It can be used in chess software through the Universal Chess Interface. Stockfish has been one of the strongest chess engines in the world for several years; it has won all main events of the Top Ch... |
Stockfish (chess) : Stockfish uses a tree-search algorithm based on alpha–beta search with several hand-designed heuristics, and since Stockfish 12 (2020) uses an efficiently updatable neural network as its evaluation function. It represents positions using bitboards. Stockfish supports Chess960, a feature it inherited... |
Stockfish (chess) : The program originated from Glaurung, an open-source chess engine created by Tord Romstad and first released in 2004. Four years later, Marco Costalba forked the project, naming it Stockfish because it was "produced in Norway and cooked in Italy" (Romstad is Norwegian and Costalba is Italian). The f... |
Stockfish (chess) : YaneuraOu, a strong shogi engine and the origin of NNUE. Speaks USI, a variant of UCI for shogi. Fairy Stockfish, a version modified to play fairy chess. Runs with regional variants (chess, shogi, makruk, etc.) as well as other variants like antichess. Lichess Stockfish, a version for playing varian... |
Stockfish (chess) : Interview with Tord Romstad (Norway), Joona Kiiski (Finland) and Marco Costalba (Italy), programmers of Stockfish |
Stockfish (chess) : Official website Official code repository on GitHub WebAssembly port of Stockfish Development versions built for Linux and Windows Developers forum Stockfish Testing Framework |
TasteDive : TasteDive (formerly named TasteKid) is an entertainment recommendation engine for films, TV shows, music, video games, books, people, places, and brands. It also has elements of a social media site; it allows users to connect with "tastebuds", people with like minded interests. |
TasteDive : TasteDive was founded in 2008 as TasteKid by brothers Andrei Oghina and Felix Oghina. In 2019, it was acquired by Qloo headquartered in NYC. "Qloo has built for developers and enterprises what TasteDive has built for individuals". |
TasteDive : When a user types in the title of a film or TV show, the site's algorithm provides a list of similar content. It provides recommendations for TV shows to watch based on films liked by the user, and vice versa. It also provides recommendations for music, video games, and books, and includes film and TV trail... |
TasteDive : Rating site Recommender system == References == |
Tractable (company) : Tractable is a technology company specializing in the development of Artificial Intelligence (AI) to assess damage to property and vehicles. The AI allows users to appraise damage digitally. |
Tractable (company) : Tractable's technology uses computer vision and deep learning to automate the appraisal of visual damage in accident and disaster recovery, for example to a vehicle. Drivers can be directed to use the application by their insurer after an accident, with the aim of settling their claim more quickly... |
Tractable (company) : Alexandre Dalyac and Razvan Ranca founded Tractable in 2014, and Adrien Cohen joined as co-founder in 2015. The company employs more than 300 staff members, largely in the United Kingdom. Tractable was named one of the 100 leading AI companies in the world in 2020 and 2021 by CB Insights. It won t... |
Cristóbal Valenzuela : Cristóbal Valenzuela is a Chilean-born technologist, software developer, and CEO of Runway. In 2018, Valenzuela co-founded the AI research company Runway in New York City with Anastasis Germanidis and Alejandro Matamala. |
Cristóbal Valenzuela : Valenzuela graduated from Adolfo Ibáñez University (AIU), a research private university in Chile. From there, Valenzuela obtained a bachelor's degree in economics and business management, along with a master's degree in arts in design in 2012. In 2018, he graduated with a media arts degree from I... |
Cristóbal Valenzuela : One of Valenzuela's first jobs was as a teaching and research assistant at the Adolfo Ibáñez University School of Design, and later an adjunct professor in the same department. In 2018, he became a researcher at NYU's Tisch School of the Arts ITP program, where he worked with Daniel Shiffman. He ... |
Vicarious (company) : Vicarious was an artificial intelligence company based in the San Francisco Bay Area, California. They use the theorized computational principles of the brain to attempt to build software that can think and learn like a human. Vicarious describes its technology as "a turnkey robotics solution inte... |
Vicarious (company) : The company was founded in 2010 by D. Scott Phoenix and Dileep George. Before co-founding Vicarious, Phoenix was Entrepreneur in Residence at Founders Fund and CEO of Frogmetrics, a touchscreen analytics company he co-founded through the Y Combinator incubator program. Previously, George was Chief... |
Vicarious (company) : The company launched in February 2011 with funding from Founders Fund, Dustin Moskovitz, Adam D’Angelo (former Facebook CTO and co-founder of Quora), Felicis Ventures, and Palantir co-founder Joe Lonsdale. In August 2012, in its Series A round of funding, it raised an additional $15 million. The r... |
Vicarious (company) : Vicarious is developing machine learning software based on the computational principles of the human brain. One such software is a vision system known as the Recursive Cortical Network (RCN), it is a generative graphical visual perception system that interprets the contents of photographs and vide... |
Vicarious (company) : Artificial intelligence Glossary of artificial intelligence |
Visual temporal attention : Visual temporal attention is a special case of visual attention that involves directing attention to specific instant of time. Similar to its spatial counterpart visual spatial attention, these attention modules have been widely implemented in video analytics in computer vision to provide en... |
Visual temporal attention : Recent video segmentation algorithms often exploits both spatial and temporal attention mechanisms. Research in human action recognition has accelerated significantly since the introduction of powerful tools such as Convolutional Neural Networks (CNNs). However, effective methods for incorpo... |
Visual temporal attention : Seibold VC, Balke J and Rolke B (2023): Temporal attention. Front. Cognit. 2:1168320. doi: 10.3389/fcogn.2023.1168320. |
Visual temporal attention : Attention Visual spatial attention Action Recognition Video content analysis Convolutional neural network Computer vision == References == |
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