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Towards Cognitive Exploration through Deep Reinforcement Learning for Mobile Robots

Oct 06, 2016

Lei Tai, Ming Liu

Oct 06, 2016

Lei Tai, Ming Liu

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* 9 pages, 7 figures, Github page for code https://github.com/GiggleLiu/QuantumCircuitBornMachine

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Learning a Rotation Invariant Detector with Rotatable Bounding Box

Nov 26, 2017

Lei Liu, Zongxu Pan, Bin Lei

Nov 26, 2017

Lei Liu, Zongxu Pan, Bin Lei

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Face Parsing via a Fully-Convolutional Continuous CRF Neural Network

Aug 12, 2017

Lei Zhou, Zhi Liu, Xiangjian He

Aug 12, 2017

Lei Zhou, Zhi Liu, Xiangjian He

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One key task of fine-grained sentiment analysis on reviews is to extract aspects or features that users have expressed opinions on. This paper focuses on supervised aspect extraction using a modified CNN called controlled CNN (Ctrl). The modified CNN has two types of control modules. Through asynchronous parameter updating, it prevents over-fitting and boosts CNN's performance significantly. This model achieves state-of-the-art results on standard aspect extraction datasets. To the best of our knowledge, this is the first paper to apply control modules to aspect extraction.

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Image Super-Resolution via RL-CSC: When Residual Learning Meets Convolutional Sparse Coding

Dec 31, 2018

Menglei Zhang, Zhou Liu, Lei Yu

Dec 31, 2018

Menglei Zhang, Zhou Liu, Lei Yu

* 10 pages, 8 figures, 4 tables

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* 34 pages, 9 figures, 2 tables

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* accepted at EMNLP 2017

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Towards continuous control of flippers for a multi-terrain robot using deep reinforcement learning

Sep 25, 2017

Giuseppe Paolo, Lei Tai, Ming Liu

Sep 25, 2017

Giuseppe Paolo, Lei Tai, Ming Liu

* 12 pages, single column, submitted to International Journal of Robotics and Automation (IJRA)

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Virtual-to-real Deep Reinforcement Learning: Continuous Control of Mobile Robots for Mapless Navigation

Jul 21, 2017

Lei Tai, Giuseppe Paolo, Ming Liu

Jul 21, 2017

Lei Tai, Giuseppe Paolo, Ming Liu

* video: https://www.youtube.com/watch?v=9AOIwBYIBbs, 6 pages, 9 figures, to appear in he 2017 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS 2017), final submission version

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* Accepted at ACL 2017. arXiv admin note: text overlap with arXiv:1612.07940

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A Generalized Probabilistic Framework for Compact Codebook Creation

Jan 30, 2014

Lingqiao Liu, Lei Wang, Chunhua Shen

Jan 30, 2014

Lingqiao Liu, Lei Wang, Chunhua Shen

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Dictionary LASSO: Guaranteed Sparse Recovery under Linear Transformation

Jul 20, 2013

Ji Liu, Lei Yuan, Jieping Ye

We consider the following signal recovery problem: given a measurement matrix $\Phi\in \mathbb{R}^{n\times p}$ and a noisy observation vector $c\in \mathbb{R}^{n}$ constructed from $c = \Phi\theta^* + \epsilon$ where $\epsilon\in \mathbb{R}^{n}$ is the noise vector whose entries follow i.i.d. centered sub-Gaussian distribution, how to recover the signal $\theta^*$ if $D\theta^*$ is sparse {\rca under a linear transformation} $D\in\mathbb{R}^{m\times p}$? One natural method using convex optimization is to solve the following problem: $$\min_{\theta} {1\over 2}\|\Phi\theta - c\|^2 + \lambda\|D\theta\|_1.$$ This paper provides an upper bound of the estimate error and shows the consistency property of this method by assuming that the design matrix $\Phi$ is a Gaussian random matrix. Specifically, we show 1) in the noiseless case, if the condition number of $D$ is bounded and the measurement number $n\geq \Omega(s\log(p))$ where $s$ is the sparsity number, then the true solution can be recovered with high probability; and 2) in the noisy case, if the condition number of $D$ is bounded and the measurement increases faster than $s\log(p)$, that is, $s\log(p)=o(n)$, the estimate error converges to zero with probability 1 when $p$ and $s$ go to infinity. Our results are consistent with those for the special case $D=\bold{I}_{p\times p}$ (equivalently LASSO) and improve the existing analysis. The condition number of $D$ plays a critical role in our analysis. We consider the condition numbers in two cases including the fused LASSO and the random graph: the condition number in the fused LASSO case is bounded by a constant, while the condition number in the random graph case is bounded with high probability if $m\over p$ (i.e., $#text{edge}\over #text{vertex}$) is larger than a certain constant. Numerical simulations are consistent with our theoretical results.
Jul 20, 2013

Ji Liu, Lei Yuan, Jieping Ye

* 26 pages, 3 figures, ICML2013

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TreeGAN: Syntax-Aware Sequence Generation with Generative Adversarial Networks

Aug 22, 2018

Xinyue Liu, Xiangnan Kong, Lei Liu, Kuorong Chiang

Aug 22, 2018

Xinyue Liu, Xiangnan Kong, Lei Liu, Kuorong Chiang

* IEEE International Conference on Data Mining (ICDM'18)

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A Multi-sentiment-resource Enhanced Attention Network for Sentiment Classification

Jul 13, 2018

Zeyang Lei, Yujiu Yang, Min Yang, Yi Liu

Jul 13, 2018

Zeyang Lei, Yujiu Yang, Min Yang, Yi Liu

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DGCNN: Disordered Graph Convolutional Neural Network Based on the Gaussian Mixture Model

Dec 10, 2017

Bo Wu, Yang Liu, Bo Lang, Lei Huang

Dec 10, 2017

Bo Wu, Yang Liu, Bo Lang, Lei Huang

* 16 pages,8 figures

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Projection Based Weight Normalization for Deep Neural Networks

Oct 06, 2017

Lei Huang, Xianglong Liu, Bo Lang, Bo Li

Oct 06, 2017

Lei Huang, Xianglong Liu, Bo Lang, Bo Li

* 12 pages, 3 figures

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