• Publications
  • Influence
PACHI: State of the Art Open Source Go Program
TLDR
We present a state of the art implementation of the Monte Carlo Tree Search algorithm for the game of Go. Expand
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Modeling of the Question Answering Task in the YodaQA System
TLDR
We briefly survey the current state of art in the field of Question Answering and present the YodaQA system, an open source framework for this task and a baseline pipeline with reasonable performance. Expand
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Balancing MCTS by Dynamically Adjusting the Komi Value
  • P. Baudis
  • Computer Science
  • J. Int. Comput. Games Assoc.
  • 2011
TLDR
Monte-Carlo Tree Search tends to produce unstable and unreasonable results in the game of Go when used in positions with an extreme advantage or disadvantage. Expand
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Joint Learning of Sentence Embeddings for Relevance and Entailment
TLDR
We consider the problem of Recognizing Textual Entailment within an Information Retrieval context, where we must simultaneously determine the relevancy as and degree of entailment for individual pieces of evidence to determine a yes/no answer to a binary natural language question. Expand
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MCTS with Information Sharing
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Dimension Selection in Axis-Parallel Brent-STEP Method for Black-Box Optimization of Separable Continuous Functions
TLDR
This article explores the possibility to choose the dimension for the next step in a more "intelligent way", i.e. to optimize first along dimensions which are believed to bring the highest profit. Expand
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Online Black-Box Algorithm Portfolios for Continuous Optimization
TLDR
We investigate algorithm selection through the Multi-Armed Bandit scenario for continuous black-box optimization. Expand
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On Move Pattern Trends in a Large Go Games Corpus
TLDR
We process a large corpus of game records of the board game of Go and propose a way of extracting summary information on played moves. Expand
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Evaluating Go game records for prediction of player attributes
TLDR
We propose a way of extracting and aggregating per-move evaluations from sets of Go game records. Expand
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Table Understanding in Structured Documents
TLDR
Table detection and extraction has been studied in the context of documents like reports, where tables are clearly outlined and stand out from the document structure visually. Expand
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