51 citations · 63 across the 9 of their papers we have counts for
10 papers
Acoustic tactile sensing for mobile robot wheels
Wilfred Mason, David Brenken, Falcon Z. Dai +3
Tactile sensing in mobile robots remains under-explored, mainly due to challenges related to sensor integration and the complexities of distributed sensing. In this work, we presen…
Subwords as Skills: Tokenization for Sparse-Reward Reinforcement Learning
David Yunis, Justin Jung, Falcon Dai +1
Exploration in sparse-reward reinforcement learning is difficult due to the requirement of long, coordinated sequences of actions in order to achieve any reward. Moreover, in conti…
On Reward Structures of Markov Decision Processes
Falcon Z. Dai
A Markov decision process can be parameterized by a transition kernel and a reward function. Both play essential roles in the study of reinforcement learning as evidenced by their…
Word2vec Conjecture and A Limitative Result
Falcon Z. Dai
Being inspired by the success of \texttt{word2vec} \citep{mikolov2013distributed} in capturing analogies, we study the conjecture that analogical relations can be represented by ve…
Loop Estimator for Discounted Values in Markov Reward Processes
Falcon Z. Dai, Matthew R. Walter
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…
DIODE: A Dense Indoor and Outdoor DEpth Dataset
Igor Vasiljevic, Nick Kolkin, Shanyi Zhang +8
We introduce DIODE, a dataset that contains thousands of diverse high resolution color images with accurate, dense, long-range depth measurements. DIODE (Dense Indoor/Outdoor DEpth…
Towards Near-imperceptible Steganographic Text
Falcon Z. Dai, Zheng Cai
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 behavio…
Maximum Expected Hitting Cost of a Markov Decision Process and Informativeness of Rewards
Falcon Z. Dai, Matthew R. Walter
We propose a new complexity measure for Markov decision processes (MDPs), the maximum expected hitting cost (MEHC). This measure tightens the closely related notion of diameter [JO…
End-to-End Content and Plan Selection for Data-to-Text Generation
Sebastian Gehrmann, Falcon Z. Dai, Henry Elder +1
Learning to generate fluent natural language from structured data with neural networks has become an common approach for NLG. This problem can be challenging when the form of the s…
Glyph-aware Embedding of Chinese Characters
Falcon Z. Dai, Zheng Cai
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. Chinese scr…