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Found 8 papers, 5 papers with code
On Reward Structures of Markov Decision Processes
In our inquiry of various kinds of "costs" associated with reinforcement learning inspired by the demands in robotic applications, rewards are central to understanding the structure of a Markov decision process and reward-centric notions can elucidate important concepts in reinforcement learning.
Word2vec Conjecture and A Limitative Result
Being inspired by the success of \texttt{word2vec} \citep{mikolov2013distributed} in capturing analogies, we study the conjecture that analogical relations can be represented by vector spaces.
Loop Estimator for Discounted Values in Markov Reward Processes
At the working heart of policy iteration algorithms commonly used and studied in the discounted setting of reinforcement learning, the policy evaluation step estimates the value of states with samples from a Markov reward process induced by following a Markov policy in a Markov decision process.
DIODE: A Dense Indoor and Outdoor DEpth Dataset
We introduce DIODE, a dataset that contains thousands of diverse high resolution color images with accurate, dense, long-range depth measurements.
Towards Near-imperceptible Steganographic Text
We show that the imperceptibility of several existing linguistic steganographic systems (Fang et al., 2017; Yang et al., 2018) relies on implicit assumptions on statistical behaviors of fluent text.
Maximum Expected Hitting Cost of a Markov Decision Process and Informativeness of Rewards
We propose a new complexity measure for Markov decision processes (MDPs), the maximum expected hitting cost (MEHC).
End-to-End Content and Plan Selection for Data-to-Text Generation
Learning to generate fluent natural language from structured data with neural networks has become an common approach for NLG.
Glyph-aware Embedding of Chinese Characters
Given the advantage and recent success of English character-level and subword-unit models in several NLP tasks, we consider the equivalent modeling problem for Chinese.
