DeepMind - AlphaCode
WEBThe vertex 1 is the root. In one⤶. // operation you can choose any bud with all its children (they are leaves) and⤶. // re-hang them to any other vertex of the tree. By doing that you delete the⤶. // edge connecting the bud and its parent and add an edge between the bud and⤶. // the chosen vertex of the tree.
Ithaca | Restoring and attributing ancient texts using deep neural …
WEBWhile Ithaca alone achieves 62% accuracy when restoring damaged texts, as soon as historians use Ithaca their performance leaps from 25% to 72%, confirming this synergistic research aid's impact. Ithaca can attribute inscriptions to their original findspot with 71% accuracy and can date them with a distance of less than 30 years from ground ...
Deepmind
WEBExplore how moves played by AlphaGo compare to those of professional and amateur players. This tool provides analysis of thousands of the most popular opening sequences from the recent history of Go, using data from 231,000 human games and 75 games that DeepMind's AlphaGo played against human players
AlphaGo 教学： 探索围棋创新思路 - Deepmind
WEB主页; 关于. 关于 WGo.js player 2.0; 关于 DeepMind AlphaGo; 感谢. 6,000 种开局变化来自于 超过 230,000 盘人类对局: 来自 Go4Go 的 65,000 局职业比赛 (1980-2017) 来自 GoBase 的 36,000 局职业比赛 (1960-1990) 野狐围棋 的 130,000 盘野狐围棋9段棋手对局; 75 盘 AlphaGo 与人类棋手对局 (前 30 手):
AlphaGo Teach: 바둑에서 새롭고 창의적인 수들을 발견해보세요
WEB색상이 표기된 원 혹은 컨트롤을 이용해 바둑판을 탐색하고, 다양한 포석을 시도해보세요. 각 수에 대한 알파고의 흑이 이길 확률에 대한 예상을 볼 수 있습니다. 각 원의 숫자는 알파고의 예측으로 해당 수에서 흑이 이길 확률을 퍼센티지로 나타냅니다. 흑 ...
AlphaGo教學: 探索圍棋創新思路 - Deepmind
WEB首頁; 關於. 關於WGo.js player 2.0; 關於DeepMind AlphaGo; 感謝. 6,000 種開局變化來自於 超過 230,000 盤人類對局: Go4Go 的 65,000 局職業比賽 (1980-2017) GoBase 的 36,000 局職業比賽 (1960-1990) 野狐圍棋 的 130,000 盤野狐圍棋9段棋手對局; 75 盤 AlphaGo 與人類棋手對局 (前 30 手):
AlphaGo 学習ツール: 新たな囲碁の楽しみを - Deepmind
WEBAlphaGo と棋士たちの手を比較してみましょう。このツールは、よくある序盤パターンを、人同士が打った 231,000 局と、DeepMind 開発の AlphaGo が人と対局した 75 局の棋譜データから分析することができます。
Neural scene representation and rendering - deepmind.com
WEBThere is more than meets the eye when it comes to how we understand a visual scene: our brains draw on prior knowledge to reason and to make inferences that go far beyond the patterns of light that hit our retinas. For example, when entering a room for the first time, you instantly recognise the items it contains and where they are positioned. If you see three legs of a table, you will infer ...
Highlighted research - deepmind.com
WEBOur pioneering research includes Deep Learning, Reinforcement Learning, Theory & Foundations, Neuroscience, Unsupervised Learning & Generative Models, Control & Robotics, and Safety.