Search Results for author:
Found 59 papers, 15 papers with code
融合自编码器和对抗训练的中文新词发现方法(Finding Chinese New Word By Combining Self-encoder and Adversarial Training)
“新词的不断涌现是语言的自然规律, 如在专业领域中新概念和实体名称代表了专业领域中某些共同特征集合的抽象概括, 经常作为关键词在句子中承担一定的角色。新词发现问题直接影响中文分词结果和后继文本语义理解任务的性能, 是自然语言处理研究领域的重要任务。本文提出了融合自编码器和对抗训练的中文新词发现模型, 采用字符级别的自编码器和无监督自学习的方式进行预训练, 可以有效提取语义信息, 不受分词结果影响, 适用于不同领域的文本;同时为了引入通用语言学知识, 添加了先验句法分析结果, 借助领域共享编码器融合语义和语法信息, 以提升划分歧义词的准确性;采用对抗训练机制, 以提取领域无关特征, 减少对于人工标注语料的依赖。实验选择六个不同的专业领域数据集评估新词发现任务, 结果显示本文模型优于其他现有方法;结合模型析构实验, 详细验证了各个模块的有效性。同时通过选择不同类型的源域数据和不同数量的目标域数据进行对比实验, 验证了模型的鲁棒性。最后以可视化的方式对比了自编码器和共享编码器对不同领域数据的编码结果, 显示了对抗训练方法能够有效地提取两者之间的相关性和差异性信息。”
Fault Detection and Tolerant Control for Aero2 2D0F Two-rotor Helicopter
Stability and satisfactory performance are critical control requirements for Unmanned Aerial Vehicle (UAV) applications.
TTA-Nav: Test-time Adaptive Reconstruction for Point-Goal Navigation under Visual Corruptions
Our "plug-and-play" method incorporates a top-down decoder to a pre-trained navigation model.
Impact of Computation in Integral Reinforcement Learning for Continuous-Time Control
We prove that the local convergence rates for IntRL using the trapezoidal rule and Bayesian quadrature with a Mat\'ern kernel to be $O(N^{-2})$ and $O(N^{-b})$, where $N$ is the number of evenly-spaced samples and $b$ is the Mat\'ern kernel's smoothness parameter.
Open Ad Hoc Teamwork with Cooperative Game Theory
Open ad hoc teamwork further complicates this challenge by considering environments with a changing number of teammates, referred to as open teams.
Aligning Individual and Collective Objectives in Multi-Agent Cooperation
The visualization of learning dynamics effectively demonstrates that AgA successfully achieves alignment between individual and collective objectives.
NiSNN-A: Non-iterative Spiking Neural Networks with Attention with Application to Motor Imagery EEG Classification
Motor imagery, an important category in electroencephalogram (EEG) research, often intersects with scenarios demanding low energy consumption, such as portable medical devices and isolated environment operations.
Deep Bayesian Reinforcement Learning for Spacecraft Proximity Maneuvers and Docking
This innovative approach allows the state-value function to be expressed as a Lyapunov function, leveraging the Gaussian process and deep kernel learning.
Causal Discovery with Generalized Linear Models through Peeling Algorithms
This article presents a novel method for causal discovery with generalized structural equation models suited for analyzing diverse types of outcomes, including discrete, continuous, and mixed data.
Cross-Utterance Conditioned VAE for Speech Generation
Experimental results on the LibriTTS datasets demonstrate that our proposed models significantly enhance speech synthesis and editing, producing more natural and expressive speech.
Few shot font generation via transferring similarity guided global style and quantization local style
To better capture the local styles, a cross-attention-based style transfer module is adopted to transfer the styles of reference glyphs to the components, where the components are self-learned discrete latent codes through vector quantization without manual definition.
JiangJun: Mastering Xiangqi by Tackling Non-Transitivity in Two-Player Zero-Sum Games
This paper presents an empirical exploration of non-transitivity in perfect-information games, specifically focusing on Xiangqi, a traditional Chinese board game comparable in game-tree complexity to chess and shogi.
Tackling Cooperative Incompatibility for Zero-Shot Human-AI Coordination
In order to solve cooperative incompatibility in learning and effectively address the problem in the context of ZSC, we introduce the Cooperative Open-ended LEarning (COLE) framework, which formulates open-ended objectives in cooperative games with two players using perspectives of graph theory to evaluate and pinpoint the cooperative capacity of each strategy.
Reinforcement Learning for Safe Robot Control using Control Lyapunov Barrier Functions
Reinforcement learning (RL) exhibits impressive performance when managing complicated control tasks for robots.
Weighted Point Cloud Normal Estimation
Existing normal estimation methods for point clouds are often less robust to severe noise and complex geometric structures.
Sim-and-Real Reinforcement Learning for Manipulation: A Consensus-based Approach
Sim-and-real training is a promising alternative to sim-to-real training for robot manipulations.
Cooperative Open-ended Learning Framework for Zero-shot Coordination
However, these approaches can result in a loss of learning and an inability to cooperate with certain strategies within the population, known as cooperative incompatibility.
Data-Adaptive Discriminative Feature Localization with Statistically Guaranteed Interpretation
We apply the proposed method to the MNIST dataset and the MIT-BIH dataset with a convolutional auto-encoder.
Millimeter-level Resolution Photonic Multiband Radar Using a Single MZM and Sub-GHz-Bandwidth Electronics
We here propose a novel cost-effective millimeter-level resolution photonic multiband radar system using a single MZM driven by a 1-GHz-bandwidth LFM signal.
Deep Koopman Learning of Nonlinear Time-Varying Systems
This paper presents a data-driven approach to approximate the dynamics of a nonlinear time-varying system (NTVS) by a linear time-varying system (LTVS), which is resulted from the Koopman operator and deep neural networks.
Cost-effective photonic super-resolution millimeter-wave joint radar-communication system using self-coherent detection
Then the self-coherent detection, as a simple and low-cost means, is accordingly facilitated for both de-chirping of MMW radar and frequency down-conversion reception of MMW communication, which circumvents the costly high-speed mixers along with MMW local oscillators and more significantly achieves the real-time decomposition of radar and communication information.
Robust Bayesian Inference for Moving Horizon Estimation
The accuracy of moving horizon estimation (MHE) suffers significantly in the presence of measurement outliers.
Bayesian Learning to Discover Mathematical Operations in Governing Equations of Dynamic Systems
Discovering governing equations from data is critical for diverse scientific disciplines as they can provide insights into the underlying phenomenon of dynamic systems.
AI-enabled Automatic Multimodal Fusion of Cone-Beam CT and Intraoral Scans for Intelligent 3D Tooth-Bone Reconstruction and Clinical Applications
Here, we present a Deep Dental Multimodal Analysis (DDMA) framework consisting of a CBCT segmentation model, an intraoral scan (IOS) segmentation model (the most accurate digital dental model), and a fusion model to generate 3D fused crown-root-bone structures with high fidelity and accurate occlusal and dentition information.
Towards Uniform Point Distribution in Feature-preserving Point Cloud Filtering
As a popular representation of 3D data, point cloud may contain noise and need to be filtered before use.
Model-Based Safe Reinforcement Learning with Time-Varying State and Control Constraints: An Application to Intelligent Vehicles
Also, few works have addressed the safe RL algorithm design under time-varying safety constraints.
Two-level monotonic multistage recommender systems
In this article, we develop a multistage recommender system utilizing a two-level monotonic property characterizing a monotonic chain of events for personalized prediction.
Robust Tube-based Model Predictive Control with Koopman Operators--Extended Version
The finite data-driven approximation of Koopman operators results in a class of linear predictors, useful for formulating linear model predictive control (MPC) of nonlinear dynamical systems with reduced computational complexity.
Sparse Bayesian Deep Learning for Dynamic System Identification
Furthermore, a practical calculation approach based on the Monte-Carlo integration method is derived to quantify the uncertainty of the parameters and predictions.
Probabilistic Mixture-of-Experts for Efficient Deep Reinforcement Learning
However, grasping distinguishable skills for some tasks with non-unique optima can be essential for further improving its learning efficiency and performance, which may lead to a multimodal policy represented as a mixture-of-experts (MOE).
Reinforcement Learning for Orientation Estimation Using Inertial Sensors with Performance Guarantee
This paper presents a deep reinforcement learning (DRL) algorithm for orientation estimation using inertial sensors combined with magnetometer.
Significance tests of feature relevance for a black-box learner
In this article, we derive one-split and two-split tests relaxing the assumptions and computational complexity of existing black-box tests and extending to examine the significance of a collection of features of interest in a dataset of possibly a complex type such as an image.
Reinforcement Learning for Control with Probabilistic Stability Guarantee
Reinforcement learning is promising to control dynamical systems for which the traditional control methods are hardly applicable.
Adaptive Deep Learning for Entity Resolution by Risk Analysis
Built on the recent advances on risk analysis for ER, the proposed approach first trains a deep model on labeled training data, and then fine-tunes it by minimizing its estimated misprediction risk on unlabeled target data.
Reinforcement Learning Control of Constrained Dynamic Systems with Uniformly Ultimate Boundedness Stability Guarantee
In comparison with the existing RL algorithms, the proposed method can achieve superior performance in terms of maintaining safety.
Lyapunov-Based Reinforcement Learning State Estimator
An actor-critic reinforcement learning algorithm is proposed to learn the state estimator approximated by a deep neural network.
Penalized model-based clustering of fMRI data
To this end, we propose a random covariance clustering model (RCCM) to concurrently cluster subjects based on their FC networks, estimate the unique FC networks of each subject, and to infer shared network features.
Lyapunov-Based Reinforcement Learning for Decentralized Multi-Agent Control
More importantly, the existing multi-agent reinforcement learning (MARL) algorithms cannot ensure the closed-loop stability of a multi-agent system from a control-theoretic perspective, so the learned control polices are highly possible to generate abnormal or dangerous behaviors in real applications.
Multi-agent Reinforcement Learning
reinforcement-learning
+1
Model-Reference Reinforcement Learning for Collision-Free Tracking Control of Autonomous Surface Vehicles
This paper presents a novel model-reference reinforcement learning algorithm for the intelligent tracking control of uncertain autonomous surface vehicles with collision avoidance.
Towards Lossless Binary Convolutional Neural Networks Using Piecewise Approximation
Binary Convolutional Neural Networks (CNNs) can significantly reduce the number of arithmetic operations and the size of memory storage, which makes the deployment of CNNs on mobile or embedded systems more promising.
Stochastic mesoscale circulation dynamics in the thermal ocean
Asymptotic expansion of the TRSW model equations in these three small parameters leads to the deterministic thermal versions of the Salmon's L1 (TL1) model and the thermal quasi-geostrophic (TQG) model, upon expanding in the neighbourhood of thermal quasi-geostrophic balance among the flow velocity and the gradients of free surface elevation and buoyancy.
Fluid Dynamics Geophysics
Actor-Critic Reinforcement Learning for Control with Stability Guarantee
Reinforcement Learning (RL) and its integration with deep learning have achieved impressive performance in various robotic control tasks, ranging from motion planning and navigation to end-to-end visual manipulation.
Model-Reference Reinforcement Learning Control of Autonomous Surface Vehicles with Uncertainties
With the conventional control, we can ensure the learning-based control law provides closed-loop stability for the overall system, and potentially increase the sample efficiency of the deep reinforcement learning.
A Sparse Bayesian Deep Learning Approach for Identification of Cascaded Tanks Benchmark
This raises the challenge that how to train a neural network for system identification with a small dataset.
$H_\infty$ Model-free Reinforcement Learning with Robust Stability Guarantee
In this paper, we introduce and extend the idea of robust stability and $H_\infty$ control to design policies with both stability and robustness guarantee.
Model-free Learning Control of Nonlinear Stochastic Systems with Stability Guarantee
Reinforcement learning (RL) offers a principled way to achieve the optimal cumulative performance index in discrete-time nonlinear stochastic systems, which are modeled as Markov decision processes.
Variational Constrained Reinforcement Learning with Application to Planning at Roundabout
In this paper, we combine variational learning and constrained reinforcement learning to simultaneously learn a Conditional Representation Model (CRM) to encode the states into safe and unsafe distributions respectively as well as to learn the corresponding safe policy.
BayesNAS: A Bayesian Approach for Neural Architecture Search
One-Shot Neural Architecture Search (NAS) is a promising method to significantly reduce search time without any separate training.
HLO: Half-kernel Laplacian Operator for Surface Smoothing
This paper presents a simple yet effective method for feature-preserving surface smoothing.
Computational Geometry Graphics
Probabilistic Recursive Reasoning for Multi-Agent Reinforcement Learning
Our methods are tested on both the matrix game and the differential game, which have a non-trivial equilibrium where common gradient-based methods fail to converge.
Multi-agent Reinforcement Learning
reinforcement-learning
+1
Data-driven Discovery of Cyber-Physical Systems
Cyber-physical systems (CPSs) embed software into the physical world.
LiDAR and Camera Detection Fusion in a Real Time Industrial Multi-Sensor Collision Avoidance System
Collision avoidance is a critical task in many applications, such as ADAS (advanced driver-assistance systems), industrial automation and robotics.
State-of-the-art and gaps for deep learning on limited training data in remote sensing
Deep learning usually requires big data, with respect to both volume and variety.
Deep Learning Hyperspectral Image Classification Using Multiple Class-based Denoising Autoencoders, Mixed Pixel Training Augmentation, and Morphological Operations
Herein, we present a system for hyperspectral image segmentation that utilizes multiple class--based denoising autoencoders which are efficiently trained.
Towards Accurate Binary Convolutional Neural Network
We introduce a novel scheme to train binary convolutional neural networks (CNNs) -- CNNs with weights and activations constrained to {-1,+1} at run-time.
Mixed Neural Network Approach for Temporal Sleep Stage Classification
Use of this recording configuration with neural network deconvolution promises to make clinically indicated home sleep studies practical.
Human collective intelligence as distributed Bayesian inference
To address this gap, we introduce a new analytical framework: We propose that groups arrive at accurate shared beliefs via distributed Bayesian inference.
DropNeuron: Simplifying the Structure of Deep Neural Networks
We proposed regularisers which support a simple mechanism of dropping neurons during a network training process.
Distributed Reconstruction of Nonlinear Networks: An ADMM Approach
In this paper, we present a distributed algorithm for the reconstruction of large-scale nonlinear networks.
