Search Results for author: Lin Zhao

Found 76 papers, 16 papers with code

Preference-Guided Reinforcement Learning for Efficient Exploration

1 code implementation9 Jul 2024 GuoJian Wang, Faguo Wu, Xiao Zhang, Tianyuan Chen, Xuyang Chen, Lin Zhao

To tackle this issue, we introduce LOPE: Learning Online with trajectory Preference guidancE, an end-to-end preference-guided RL framework that enhances exploration efficiency in hard-exploration tasks.

Efficient Exploration reinforcement-learning +1

Brain Dialogue Interface (BDI): A User-Friendly fMRI Model for Interactive Brain Decoding

no code implementations17 Jun 2024 Heng Huang, Lin Zhao, Zihao Wu, Xiaowei Yu, Jing Zhang, Xintao Hu, Dajiang Zhu, Tianming Liu

To address this issue, this study introduces a user-friendly decoding model that enables dynamic communication with the brain, as opposed to the static decoding approaches utilized by traditional studies.

Brain Decoding

Preferred-Action-Optimized Diffusion Policies for Offline Reinforcement Learning

no code implementations29 May 2024 Tianle Zhang, Jiayi Guan, Lin Zhao, Yihang Li, Dongjiang Li, Zecui Zeng, Lei Sun, Yue Chen, Xuelong Wei, Lusong Li, Xiaodong He

Meanwhile, based on the diffusion model, preferred actions within the same behavior distribution are automatically generated through the critic function.

Offline RL reinforcement-learning +1

Domain adaptive pose estimation via multi-level alignment

1 code implementation23 Apr 2024 Yugan Chen, Lin Zhao, Yalong Xu, Honglei Zu, Xiaoqi An, Guangyu Li

Specifically, we first utilize image style transer to ensure that images from the source and target domains have a similar distribution.

Animal Pose Estimation Domain Adaptation

Clinically Feasible Diffusion Reconstruction for Highly-Accelerated Cardiac Cine MRI

no code implementations13 Mar 2024 Shihan Qiu, Shaoyan Pan, Yikang Liu, Lin Zhao, Jian Xu, Qi Liu, Terrence Chen, Eric Z. Chen, Xiao Chen, Shanhui Sun

The currently limited quality of accelerated cardiac cine reconstruction may potentially be improved by the emerging diffusion models, but the clinically unacceptable long processing time poses a challenge.

Spatiotemporal Diffusion Model with Paired Sampling for Accelerated Cardiac Cine MRI

no code implementations13 Mar 2024 Shihan Qiu, Shaoyan Pan, Yikang Liu, Lin Zhao, Jian Xu, Qi Liu, Terrence Chen, Eric Z. Chen, Xiao Chen, Shanhui Sun

Current deep learning reconstruction for accelerated cardiac cine MRI suffers from spatial and temporal blurring.

Federated Data Model

no code implementations13 Mar 2024 Xiao Chen, Shunan Zhang, Eric Z. Chen, Yikang Liu, Lin Zhao, Terrence Chen, Shanhui Sun

In artificial intelligence (AI), especially deep learning, data diversity and volume play a pivotal role in model development.

Diversity Image Segmentation +2

Safe Reinforcement Learning-Based Eco-Driving Control for Mixed Traffic Flows With Disturbances

no code implementations31 Jan 2024 Ke Lu, Dongjun Li, Qun Wang, Kaidi Yang, Lin Zhao, Ziyou Song

This paper presents a safe learning-based eco-driving framework tailored for mixed traffic flows, which aims to optimize energy efficiency while guaranteeing safety during real-system operations.

Reinforcement Learning (RL) Safe Reinforcement Learning

Revolutionizing Finance with LLMs: An Overview of Applications and Insights

no code implementations22 Jan 2024 Huaqin Zhao, Zhengliang Liu, Zihao Wu, Yiwei Li, Tianze Yang, Peng Shu, Shaochen Xu, Haixing Dai, Lin Zhao, Gengchen Mai, Ninghao Liu, Tianming Liu

Additionally, we conducted holistic tests on multiple financial tasks through the combination of natural language instructions.

Large Language Models for Robotics: Opportunities, Challenges, and Perspectives

no code implementations9 Jan 2024 Jiaqi Wang, Zihao Wu, Yiwei Li, Hanqi Jiang, Peng Shu, Enze Shi, Huawen Hu, Chong Ma, Yiheng Liu, Xuhui Wang, Yincheng Yao, Xuan Liu, Huaqin Zhao, Zhengliang Liu, Haixing Dai, Lin Zhao, Bao Ge, Xiang Li, Tianming Liu, Shu Zhang

Notably, in the realm of robot task planning, LLMs harness their advanced reasoning and language comprehension capabilities to formulate precise and efficient action plans based on natural language instructions.

Robot Task Planning

An Asymmetric Augmented Self-Supervised Learning Method for Unsupervised Fine-Grained Image Hashing

no code implementations CVPR 2024 Feiran Hu, Chenlin Zhang, Jiangliang Guo, Xiu-Shen Wei, Lin Zhao, Anqi Xu, Lingyan Gao

In this paper we first identify a granularity gap between generic and fine-grained datasets for unsupervised hashing methods highlighting the inadequacy of conventional self-supervised learning for fine-grained visual objects.

Contrastive Learning Self-Supervised Learning

A comprehensive framework for occluded human pose estimation

no code implementations30 Dec 2023 Linhao Xu, Lin Zhao, Xinxin Sun, Di Wang, Guangyu Li, Kedong Yan

The challenges posed by occlusion can be attributed to the following factors: 1) Data: The collection and annotation of occluded human pose samples are relatively challenging.

Data Augmentation Pose Estimation

SHaRPose: Sparse High-Resolution Representation for Human Pose Estimation

1 code implementation17 Dec 2023 Xiaoqi An, Lin Zhao, Chen Gong, Nannan Wang, Di Wang, Jian Yang

In this paper, we address the following question: "Only sparse human keypoint locations are detected for human pose estimation, is it really necessary to describe the whole image in a dense, high-resolution manner?"

Pose Estimation

THInImg: Cross-modal Steganography for Presenting Talking Heads in Images

no code implementations28 Nov 2023 Lin Zhao, Hongxuan Li, Xuefei Ning, Xinru Jiang

Cross-modal Steganography is the practice of concealing secret signals in publicly available cover signals (distinct from the modality of the secret signals) unobtrusively.

Decoder

Optimization Landscape of Policy Gradient Methods for Discrete-time Static Output Feedback

no code implementations29 Oct 2023 Jingliang Duan, Jie Li, Xuyang Chen, Kai Zhao, Shengbo Eben Li, Lin Zhao

Despite the absence of convexity, we leverage these properties to derive novel findings regarding convergence (and nearly dimension-free rate) to stationary points for three policy gradient methods, including the vanilla policy gradient method, the natural policy gradient method, and the Gauss-Newton method.

Policy Gradient Methods

Chat2Brain: A Method for Mapping Open-Ended Semantic Queries to Brain Activation Maps

no code implementations10 Sep 2023 Yaonai Wei, Tuo Zhang, Han Zhang, Tianyang Zhong, Lin Zhao, Zhengliang Liu, Chong Ma, Songyao Zhang, Muheng Shang, Lei Du, Xiao Li, Tianming Liu, Junwei Han

In this study, we propose a method called Chat2Brain that combines LLMs to basic text-2-image model, known as Text2Brain, to map open-ended semantic queries to brain activation maps in data-scarce and complex query environments.

PharmacyGPT: The AI Pharmacist

no code implementations19 Jul 2023 Zhengliang Liu, Zihao Wu, Mengxuan Hu, Bokai Zhao, Lin Zhao, Tianyi Zhang, Haixing Dai, Xianyan Chen, Ye Shen, Sheng Li, Brian Murray, Tianming Liu, Andrea Sikora

In this study, we introduce PharmacyGPT, a novel framework to assess the capabilities of large language models (LLMs) such as ChatGPT and GPT-4 in emulating the role of clinical pharmacists.

Review of Large Vision Models and Visual Prompt Engineering

no code implementations3 Jul 2023 Jiaqi Wang, Zhengliang Liu, Lin Zhao, Zihao Wu, Chong Ma, Sigang Yu, Haixing Dai, Qiushi Yang, Yiheng Liu, Songyao Zhang, Enze Shi, Yi Pan, Tuo Zhang, Dajiang Zhu, Xiang Li, Xi Jiang, Bao Ge, Yixuan Yuan, Dinggang Shen, Tianming Liu, Shu Zhang

This review aims to summarize the methods employed in the computer vision domain for large vision models and visual prompt engineering, exploring the latest advancements in visual prompt engineering.

Prompt Engineering

AD-AutoGPT: An Autonomous GPT for Alzheimer's Disease Infodemiology

no code implementations16 Jun 2023 Haixing Dai, Yiwei Li, Zhengliang Liu, Lin Zhao, Zihao Wu, Suhang Song, Ye Shen, Dajiang Zhu, Xiang Li, Sheng Li, Xiaobai Yao, Lu Shi, Quanzheng Li, Zhuo Chen, Donglan Zhang, Gengchen Mai, Tianming Liu

In this pioneering study, inspired by AutoGPT, the state-of-the-art open-source application based on the GPT-4 large language model, we develop a novel tool called AD-AutoGPT which can conduct data collection, processing, and analysis about complex health narratives of Alzheimer's Disease in an autonomous manner via users' textual prompts.

Language Modelling Large Language Model

Artificial General Intelligence for Medical Imaging

no code implementations8 Jun 2023 Xiang Li, Lu Zhang, Zihao Wu, Zhengliang Liu, Lin Zhao, Yixuan Yuan, Jun Liu, Gang Li, Dajiang Zhu, Pingkun Yan, Quanzheng Li, Wei Liu, Tianming Liu, Dinggang Shen

In this review, we explore the potential applications of Artificial General Intelligence (AGI) models in healthcare, focusing on foundational Large Language Models (LLMs), Large Vision Models, and Large Multimodal Models.

Instruction-ViT: Multi-Modal Prompts for Instruction Learning in ViT

no code implementations29 Apr 2023 Zhenxiang Xiao, Yuzhong Chen, Lu Zhang, Junjie Yao, Zihao Wu, Xiaowei Yu, Yi Pan, Lin Zhao, Chong Ma, Xinyu Liu, Wei Liu, Xiang Li, Yixuan Yuan, Dinggang Shen, Dajiang Zhu, Tianming Liu, Xi Jiang

Prompts have been proven to play a crucial role in large language models, and in recent years, vision models have also been using prompts to improve scalability for multiple downstream tasks.

Image Classification

Exploring the Trade-Offs: Unified Large Language Models vs Local Fine-Tuned Models for Highly-Specific Radiology NLI Task

no code implementations18 Apr 2023 Zihao Wu, Lu Zhang, Chao Cao, Xiaowei Yu, Haixing Dai, Chong Ma, Zhengliang Liu, Lin Zhao, Gang Li, Wei Liu, Quanzheng Li, Dinggang Shen, Xiang Li, Dajiang Zhu, Tianming Liu

To this end, in this study, we evaluate the performance of ChatGPT/GPT-4 on a radiology NLI task and compare it to other models fine-tuned specifically on task-related data samples.

Specificity Task 2

Summary of ChatGPT-Related Research and Perspective Towards the Future of Large Language Models

no code implementations4 Apr 2023 Yiheng Liu, Tianle Han, Siyuan Ma, Jiayue Zhang, Yuanyuan Yang, Jiaming Tian, Hao He, Antong Li, Mengshen He, Zhengliang Liu, Zihao Wu, Lin Zhao, Dajiang Zhu, Xiang Li, Ning Qiang, Dingang Shen, Tianming Liu, Bao Ge

This paper presents a comprehensive survey of ChatGPT-related (GPT-3. 5 and GPT-4) research, state-of-the-art large language models (LLM) from the GPT series, and their prospective applications across diverse domains.

Tightly-coupled Visual-DVL-Inertial Odometry for Robot-based Ice-water Boundary Exploration

1 code implementation29 Mar 2023 Lin Zhao, Mingxi Zhou, Brice Loose

The proposed method is validated with a data set collected in the field under frozen ice, and the result is compared with 6 other different sensor fusion setups.

Sensor Fusion

When Brain-inspired AI Meets AGI

no code implementations28 Mar 2023 Lin Zhao, Lu Zhang, Zihao Wu, Yuzhong Chen, Haixing Dai, Xiaowei Yu, Zhengliang Liu, Tuo Zhang, Xintao Hu, Xi Jiang, Xiang Li, Dajiang Zhu, Dinggang Shen, Tianming Liu

Artificial General Intelligence (AGI) has been a long-standing goal of humanity, with the aim of creating machines capable of performing any intellectual task that humans can do.

In-Context Learning

CP-CNN: Core-Periphery Principle Guided Convolutional Neural Network

no code implementations27 Mar 2023 Lin Zhao, Haixing Dai, Zihao Wu, Dajiang Zhu, Tianming Liu

In this study, We explore a novel brain-inspired design principle based on the core-periphery property of the human brain network to guide the design of CNNs.

Neural Architecture Search

Coupling Artificial Neurons in BERT and Biological Neurons in the Human Brain

no code implementations27 Mar 2023 Xu Liu, Mengyue Zhou, Gaosheng Shi, Yu Du, Lin Zhao, Zihao Wu, David Liu, Tianming Liu, Xintao Hu

Linking computational natural language processing (NLP) models and neural responses to language in the human brain on the one hand facilitates the effort towards disentangling the neural representations underpinning language perception, on the other hand provides neurolinguistics evidence to evaluate and improve NLP models.

Core-Periphery Principle Guided Redesign of Self-Attention in Transformers

no code implementations27 Mar 2023 Xiaowei Yu, Lu Zhang, Haixing Dai, Yanjun Lyu, Lin Zhao, Zihao Wu, David Liu, Tianming Liu, Dajiang Zhu

Designing more efficient, reliable, and explainable neural network architectures is critical to studies that are based on artificial intelligence (AI) techniques.

DeID-GPT: Zero-shot Medical Text De-Identification by GPT-4

1 code implementation20 Mar 2023 Zhengliang Liu, Yue Huang, Xiaowei Yu, Lu Zhang, Zihao Wu, Chao Cao, Haixing Dai, Lin Zhao, Yiwei Li, Peng Shu, Fang Zeng, Lichao Sun, Wei Liu, Dinggang Shen, Quanzheng Li, Tianming Liu, Dajiang Zhu, Xiang Li

The digitization of healthcare has facilitated the sharing and re-using of medical data but has also raised concerns about confidentiality and privacy.

Benchmarking De-identification +4

Gyri vs. Sulci: Disentangling Brain Core-Periphery Functional Networks via Twin-Transformer

no code implementations31 Jan 2023 Xiaowei Yu, Lu Zhang, Haixing Dai, Lin Zhao, Yanjun Lyu, Zihao Wu, Tianming Liu, Dajiang Zhu

To solve this fundamental problem, we design a novel Twin-Transformer framework to unveil the unique functional roles of gyri and sulci as well as their relationship in the whole brain function.

Anatomy

Towards High Performance One-Stage Human Pose Estimation

no code implementations12 Jan 2023 Ling Li, Lin Zhao, Linhao Xu, Jie Xu

Making top-down human pose estimation method present both good performance and high efficiency is appealing.

Human Detection Keypoint Detection +1

BI AVAN: Brain inspired Adversarial Visual Attention Network

no code implementations27 Oct 2022 Heng Huang, Lin Zhao, Xintao Hu, Haixing Dai, Lu Zhang, Dajiang Zhu, Tianming Liu

Visual attention is a fundamental mechanism in the human brain, and it inspires the design of attention mechanisms in deep neural networks.

JoJoNet: Joint-contrast and Joint-sampling-and-reconstruction Network for Multi-contrast MRI

no code implementations22 Oct 2022 Lin Zhao, Xiao Chen, Eric Z. Chen, Yikang Liu, Dinggang Shen, Terrence Chen, Shanhui Sun

The proposed framework consists of a sampling mask generator for each image contrast and a reconstructor exploiting the inter-contrast correlations with a recurrent structure which enables the information sharing in a holistic way.

Finite-time analysis of single-timescale actor-critic

no code implementations NeurIPS 2023 Xuyang Chen, Lin Zhao

We investigate the more practical online single-timescale actor-critic algorithm on continuous state space, where the critic assumes linear function approximation and updates with a single Markovian sample per actor step.

On the Optimization Landscape of Dynamic Output Feedback: A Case Study for Linear Quadratic Regulator

no code implementations12 Sep 2022 Jingliang Duan, Wenhan Cao, Yang Zheng, Lin Zhao

At the core of our results is the uniqueness of the stationary point of dLQR when it is observable, which is in a concise form of an observer-based controller with the optimal similarity transformation.

Decision Making Policy Gradient Methods

Forensicability Assessment of Questioned Images in Recapturing Detection

no code implementations5 Sep 2022 Changsheng chen, Lin Zhao, Rizhao Cai, Zitong Yu, Jiwu Huang, Alex C. Kot

We integrate the trained FANet with practical recapturing detection schemes in face anti-spoofing and recaptured document detection tasks.

Face Anti-Spoofing Image Quality Assessment

Global Convergence of Two-timescale Actor-Critic for Solving Linear Quadratic Regulator

no code implementations18 Aug 2022 Xuyang Chen, Jingliang Duan, Yingbin Liang, Lin Zhao

To our knowledge, this is the first finite-time convergence analysis for the single sample two-timescale AC for solving LQR with global optimality.

Coupling Visual Semantics of Artificial Neural Networks and Human Brain Function via Synchronized Activations

no code implementations22 Jun 2022 Lin Zhao, Haixing Dai, Zihao Wu, Zhenxiang Xiao, Lu Zhang, David Weizhong Liu, Xintao Hu, Xi Jiang, Sheng Li, Dajiang Zhu, Tianming Liu

However, whether there exists semantic correlations/connections between the visual representations in ANNs and those in BNNs remains largely unexplored due to both the lack of an effective tool to link and couple two different domains, and the lack of a general and effective framework of representing the visual semantics in BNNs such as human functional brain networks (FBNs).

Image Classification Representation Learning

Neural Moving Horizon Estimation for Robust Flight Control

1 code implementation21 Jun 2022 Bingheng Wang, Zhengtian Ma, Shupeng Lai, Lin Zhao

Estimating and reacting to external disturbances is crucial for robust flight control of quadrotors.

Rectify ViT Shortcut Learning by Visual Saliency

no code implementations17 Jun 2022 Chong Ma, Lin Zhao, Yuzhong Chen, David Weizhong Liu, Xi Jiang, Tuo Zhang, Xintao Hu, Dinggang Shen, Dajiang Zhu, Tianming Liu

In this work, we propose a novel and effective saliency-guided vision transformer (SGT) model to rectify shortcut learning in ViT with the absence of eye-gaze data.

Invertible Sharpening Network for MRI Reconstruction Enhancement

no code implementations6 Jun 2022 Siyuan Dong, Eric Z. Chen, Lin Zhao, Xiao Chen, Yikang Liu, Terrence Chen, Shanhui Sun

During inference, the learned blurring transform can be inverted to a sharpening transform leveraging the network's invertibility.

MRI Reconstruction SSIM

Representing Brain Anatomical Regularity and Variability by Few-Shot Embedding

no code implementations26 May 2022 Lu Zhang, Xiaowei Yu, Yanjun Lyu, Zhengwang Wu, Haixing Dai, Lin Zhao, Li Wang, Gang Li, Tianming Liu, Dajiang Zhu

Our experimental results show that: 1) the learned embedding vectors can quantitatively encode the commonality and individuality of cortical folding patterns; 2) with the embeddings we can robustly infer the complicated many-to-many anatomical correspondences among different brains and 3) our model can be successfully transferred to new populations with very limited training samples.

Few-Shot Learning

Eye-gaze-guided Vision Transformer for Rectifying Shortcut Learning

no code implementations25 May 2022 Chong Ma, Lin Zhao, Yuzhong Chen, Lu Zhang, Zhenxiang Xiao, Haixing Dai, David Liu, Zihao Wu, Zhengliang Liu, Sheng Wang, Jiaxing Gao, Changhe Li, Xi Jiang, Tuo Zhang, Qian Wang, Dinggang Shen, Dajiang Zhu, Tianming Liu

To address this problem, we propose to infuse human experts' intelligence and domain knowledge into the training of deep neural networks.

A Unified and Biologically-Plausible Relational Graph Representation of Vision Transformers

no code implementations20 May 2022 Yuzhong Chen, Yu Du, Zhenxiang Xiao, Lin Zhao, Lu Zhang, David Weizhong Liu, Dajiang Zhu, Tuo Zhang, Xintao Hu, Tianming Liu, Xi Jiang

The key characteristic of these ViT models is to adopt different aggregation strategies of spatial patch information within the artificial neural networks (ANNs).

Mask-guided Vision Transformer (MG-ViT) for Few-Shot Learning

no code implementations20 May 2022 Yuzhong Chen, Zhenxiang Xiao, Lin Zhao, Lu Zhang, Haixing Dai, David Weizhong Liu, Zihao Wu, Changhe Li, Tuo Zhang, Changying Li, Dajiang Zhu, Tianming Liu, Xi Jiang

However, for data-intensive models such as vision transformer (ViT), current fine-tuning based FSL approaches are inefficient in knowledge generalization and thus degenerate the downstream task performances.

Active Learning Few-Shot Learning

Disentangling Spatial-Temporal Functional Brain Networks via Twin-Transformers

no code implementations20 Apr 2022 Xiaowei Yu, Lu Zhang, Lin Zhao, Yanjun Lyu, Tianming Liu, Dajiang Zhu

In this work, we propose a novel Twin-Transformers framework to simultaneously infer common and individual functional networks in both spatial and temporal space, in a self-supervised manner.

Faster Non-asymptotic Convergence for Double Q-learning

no code implementations NeurIPS 2021 Lin Zhao, Huaqing Xiong, Yingbin Liang

This paper tackles the more challenging case of a constant learning rate, and develops new analytical tools that improve the existing convergence rate by orders of magnitude.

Q-Learning

MedMNIST v2 -- A large-scale lightweight benchmark for 2D and 3D biomedical image classification

3 code implementations27 Oct 2021 Jiancheng Yang, Rui Shi, Donglai Wei, Zequan Liu, Lin Zhao, Bilian Ke, Hanspeter Pfister, Bingbing Ni

We introduce MedMNIST v2, a large-scale MNIST-like dataset collection of standardized biomedical images, including 12 datasets for 2D and 6 datasets for 3D.

AutoML Image Classification

LIF-Seg: LiDAR and Camera Image Fusion for 3D LiDAR Semantic Segmentation

no code implementations17 Aug 2021 Lin Zhao, Hui Zhou, Xinge Zhu, Xiao Song, Hongsheng Li, Wenbing Tao

However, two major issues of the fusion between camera and LiDAR hinder its performance, \ie, how to effectively fuse these two modalities and how to precisely align them (suffering from the weak spatiotemporal synchronization problem).

Autonomous Driving LIDAR Semantic Segmentation +1

Double Q-learning: New Analysis and Sharper Finite-time Bound

no code implementations1 Jan 2021 Lin Zhao, Huaqing Xiong, Yingbin Liang, Wei zhang

Double Q-learning (Hasselt 2010) has gained significant success in practice due to its effectiveness in overcoming the overestimation issue of Q-learning.

Q-Learning

Origin of the Electronic Structure in Single-Layer FeSe/SrTiO3 Films

no code implementations16 Dec 2020 Defa Liu, Xianxin Wu, Fangsen Li, Yong Hu, Jianwei Huang, Yu Xu, Cong Li, Yunyi Zang, Junfeng He, Lin Zhao, Shaolong He, Chenjia Tang, Zhi Li, Lili Wang, Qingyan Wang, Guodong Liu, Zuyan Xu, Xu-Cun Ma, Qi-Kun Xue, Jiangping Hu, X. J. Zhou

These observations not only show the first direct evidence that the electronic structure of single-layer FeSe/SrTiO3 films originates from bulk FeSe through a combined effect of an electronic phase transition and an interfacial charge transfer, but also provide a quantitative basis for theoretical models in describing the electronic structure and understanding the superconducting mechanism in single-layer FeSe/SrTiO3 films.

Band Gap Superconductivity Strongly Correlated Electrons

Finite-Time Analysis for Double Q-learning

no code implementations NeurIPS 2020 Huaqing Xiong, Lin Zhao, Yingbin Liang, Wei zhang

Although Q-learning is one of the most successful algorithms for finding the best action-value function (and thus the optimal policy) in reinforcement learning, its implementation often suffers from large overestimation of Q-function values incurred by random sampling.

Q-Learning

Momentum Q-learning with Finite-Sample Convergence Guarantee

no code implementations30 Jul 2020 Bowen Weng, Huaqing Xiong, Lin Zhao, Yingbin Liang, Wei zhang

For the infinite state-action space case, we establish the convergence guarantee for MomentumQ with linear function approximations and Markovian sampling.

Q-Learning

JSNet: Joint Instance and Semantic Segmentation of 3D Point Clouds

2 code implementations20 Dec 2019 Lin Zhao, Wenbing Tao

In this paper, we propose a novel joint instance and semantic segmentation approach, which is called JSNet, in order to address the instance and semantic segmentation of 3D point clouds simultaneously.

3D Instance Segmentation Clustering +2

Localization-aware Channel Pruning for Object Detection

no code implementations6 Nov 2019 Zihao Xie, Wenbing Tao, Li Zhu, Lin Zhao

In this paper, based on discrimination-aware channel pruning (DCP) which is state-of-the-art pruning method for classification, we propose a localization-aware auxiliary network to find out the channels with key information for classification and regression so that we can conduct channel pruning directly for object detection, which saves lots of time and computing resources.

Classification General Classification +5

Data-based wind disaster climate identification algorithm and extreme wind speed prediction

no code implementations29 Aug 2019 Wei Cui, Teng Ma, Lin Zhao, Yaojun Ge

Based on classification results, the extreme wind speeds calculated based on mixed wind hazard types is compared with those obtained from conventional methods, and the effects on structural design for different return periods are discussed.

Improving Slot Filling in Spoken Language Understanding with Joint Pointer and Attention

1 code implementation ACL 2018 Lin Zhao, Zhe Feng

We present a generative neural network model for slot filling based on a sequence-to-sequence (Seq2Seq) model together with a pointer network, in the situation where only sentence-level slot annotations are available in the spoken dialogue data.

Sentence slot-filling +3

Structure-Infused Copy Mechanisms for Abstractive Summarization

1 code implementation COLING 2018 Kaiqiang Song, Lin Zhao, Fei Liu

In this paper, we present structure-infused copy mechanisms to facilitate copying important words and relations from the source sentence to summary sentence.

Abstractive Text Summarization Sentence

A unified integral equation scheme for doubly-periodic Laplace and Stokes boundary value problems in two dimensions

2 code implementations24 Nov 2016 Alex H. Barnett, Gary Marple, Shravan Veerapaneni, Lin Zhao

We present a spectrally-accurate scheme to turn a boundary integral formulation for an elliptic PDE on a single unit cell geometry into one for the fully periodic problem.

Numerical Analysis 65N38, 65N80, 76D07, 76M50

Question Generation from a Knowledge Base with Web Exploration

no code implementations12 Oct 2016 Linfeng Song, Lin Zhao

Question generation from a knowledge base (KB) is the task of generating questions related to the domain of the input KB.

Natural Questions Question Generation +1

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