This paper introduces a principled approach for the design of a scalable general reinforcement learning agent. This approach is based on a direct approximation of AIXI, a Bayesian...
Joel Veness, Kee Siong Ng, Marcus Hutter, David Si...
How humans infer probable information from the limited observed data? How they are able to build on little knowledge about the context in hand? Is the human memory repeatedly const...
Abstract. We present a new learning to rank framework for estimating context-sensitive term weights without use of feedback. Specifically, knowledge of effective term weights on ...
Abstract. One of issues in the bootstrapping for named entity recognition is how to control annotation errors introduced at every iteration. In this paper, we present several heuri...
Paraphrases have proved to be useful in many applications, including Machine Translation, Question Answering, Summarization, and Information Retrieval. Paraphrase acquisition meth...