Search Results for author: Bo Cheng

Found 18 papers, 5 papers with code

Exploring Entity Interactions for Few-Shot Relation Learning (Student Abstract)

no code implementations4 May 2022 Yi Liang, Shuai Zhao, Bo Cheng, Yuwei Yin, Hao Yang

Few-shot relation learning refers to infer facts for relations with a limited number of observed triples.

Metric Learning

FCM: A Fine-grained Comparison Model for Multi-turn Dialogue Reasoning

no code implementations Findings (EMNLP) 2021 Xu Wang, Hainan Zhang, Shuai Zhao, Yanyan Zou, Hongshen Chen, Zhuoye Ding, Bo Cheng, Yanyan Lan

Furthermore, the consistency signals between each candidate and the speaker's own history are considered to drive a model to prefer a candidate that is logically consistent with the speaker's history logic.

Reading Comprehension

Exploring Task Difficulty for Few-Shot Relation Extraction

1 code implementation EMNLP 2021 Jiale Han, Bo Cheng, Wei Lu

Few-shot relation extraction (FSRE) focuses on recognizing novel relations by learning with merely a handful of annotated instances.

Contrastive Learning Meta-Learning +1

DPN-SENet:A self-attention mechanism neural network for detection and diagnosis of COVID-19 from chest x-ray images

1 code implementation20 May 2021 Bo Cheng, Ruhui Xue, Hang Yang, Laili Zhu, Wei Xiang

We propose a deep learning model that can help radiologists and clinicians use chest X-rays to diagnose COVID-19 cases and show the diagnostic features of pneumonia.

Data Augmentation

Integrating Subgraph-aware Relation and DirectionReasoning for Question Answering

no code implementations1 Apr 2021 Xu Wang, Shuai Zhao, Bo Cheng, Jiale Han, Yingting Li, Hao Yang, Ivan Sekulic, Guoshun Nan

Question Answering (QA) models over Knowledge Bases (KBs) are capable of providing more precise answers by utilizing relation information among entities.

Question Answering

Integrated Decision and Control: Towards Interpretable and Computationally Efficient Driving Intelligence

2 code implementations18 Mar 2021 Yang Guan, Yangang Ren, Qi Sun, Shengbo Eben Li, Haitong Ma, Jingliang Duan, Yifan Dai, Bo Cheng

In this paper, we present an interpretable and computationally efficient framework called integrated decision and control (IDC) for automated vehicles, which decomposes the driving task into static path planning and dynamic optimal tracking that are structured hierarchically.

Autonomous Driving Model-based Reinforcement Learning +1

Recurrent Model Predictive Control

no code implementations23 Feb 2021 Zhengyu Liu, Jingliang Duan, Wenxuan Wang, Shengbo Eben Li, Yuming Yin, Ziyu Lin, Qi Sun, Bo Cheng

This paper proposes an off-line algorithm, called Recurrent Model Predictive Control (RMPC), to solve general nonlinear finite-horizon optimal control problems.

Mixed Policy Gradient

2 code implementations23 Feb 2021 Yang Guan, Jingliang Duan, Shengbo Eben Li, Jie Li, Jianyu Chen, Bo Cheng

MPG contains two types of PG: 1) data-driven PG, which is obtained by directly calculating the derivative of the learned Q-value function with respect to actions, and 2) model-driven PG, which is calculated using BPTT based on the model-predictive return.

Decision Making

Recurrent Model Predictive Control: Learning an Explicit Recurrent Controller for Nonlinear Systems

no code implementations20 Feb 2021 Zhengyu Liu, Jingliang Duan, Wenxuan Wang, Shengbo Eben Li, Yuming Yin, Ziyu Lin, Bo Cheng

This paper proposes an offline control algorithm, called Recurrent Model Predictive Control (RMPC), to solve large-scale nonlinear finite-horizon optimal control problems.

Mixed Reinforcement Learning with Additive Stochastic Uncertainty

no code implementations28 Feb 2020 Yao Mu, Shengbo Eben Li, Chang Liu, Qi Sun, Bingbing Nie, Bo Cheng, Baiyu Peng

This paper presents a mixed reinforcement learning (mixed RL) algorithm by simultaneously using dual representations of environmental dynamics to search the optimal policy with the purpose of improving both learning accuracy and training speed.


Distributional Soft Actor-Critic: Off-Policy Reinforcement Learning for Addressing Value Estimation Errors

2 code implementations9 Jan 2020 Jingliang Duan, Yang Guan, Shengbo Eben Li, Yangang Ren, Bo Cheng

In reinforcement learning (RL), function approximation errors are known to easily lead to the Q-value overestimations, thus greatly reducing policy performance.

Continuous Control reinforcement-learning

Direct and indirect reinforcement learning

no code implementations23 Dec 2019 Yang Guan, Shengbo Eben Li, Jingliang Duan, Jie Li, Yangang Ren, Qi Sun, Bo Cheng

Reinforcement learning (RL) algorithms have been successfully applied to a range of challenging sequential decision making and control tasks.

Decision Making reinforcement-learning

Adaptive dynamic programming for nonaffine nonlinear optimal control problem with state constraints

no code implementations26 Nov 2019 Jingliang Duan, Zhengyu Liu, Shengbo Eben Li, Qi Sun, Zhenzhong Jia, Bo Cheng

CADP linearizes the constrained optimization problem locally into a quadratically constrained linear programming problem, and then obtains the optimal update of the policy network by solving its dual problem.

Generalized Policy Iteration for Optimal Control in Continuous Time

no code implementations11 Sep 2019 Jingliang Duan, Shengbo Eben Li, Zhengyu Liu, Monimoy Bujarbaruah, Bo Cheng

This paper proposes the Deep Generalized Policy Iteration (DGPI) algorithm to find the infinite horizon optimal control policy for general nonlinear continuous-time systems with known dynamics.

Intention-aware Long Horizon Trajectory Prediction of Surrounding Vehicles using Dual LSTM Networks

no code implementations6 Jun 2019 Long Xin, Pin Wang, Ching-Yao Chan, Jianyu Chen, Shengbo Eben Li, Bo Cheng

As autonomous vehicles (AVs) need to interact with other road users, it is of importance to comprehensively understand the dynamic traffic environment, especially the future possible trajectories of surrounding vehicles.

Autonomous Vehicles Intent Detection +1

An End-to-End Multi-task Learning Model for Fact Checking

no code implementations WS 2018 Sizhen Li, Shuai Zhao, Bo Cheng, Hao Yang

With huge amount of information generated every day on the web, fact checking is an important and challenging task which can help people identify the authenticity of most claims as well as providing evidences selected from knowledge source like Wikipedia.

Common Sense Reasoning Entity Linking +4

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