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Found 63 papers, 9 papers with code
基于迭代信息传递和滑动窗口注意力的问题生成模型研究(Question Generation Model Based on Iterative Message Passing and Sliding Windows Hierarchical Attention)
“知识图谱问题生成任务是从给定的知识图谱中生成与其相关的问题。目前, 知识图谱问题生成模型主要使用基于RNN或Transformer对知识图谱子图进行编码, 但这种方式丢失了显式的图结构化信息, 在解码器中忽视了局部信息对节点的重要性。本文提出迭代信息传递图编码器来编码子图, 获取子图显式的图结构化信息, 此外, 我们还使用滑动窗口注意力机制提高RNN解码器, 提升子图局部信息对节点的重要度。从WQ和PQ数据集上的实验结果看, 我们提出的模型比KTG模型在BLEU4指标上分别高出2. 16和15. 44, 证明了该模型的有效性。”
Training Large Language Models for Reasoning through Reverse Curriculum Reinforcement Learning
In this paper, we propose R$^3$: Learning Reasoning through Reverse Curriculum Reinforcement Learning (RL), a novel method that employs only outcome supervision to achieve the benefits of process supervision for large language models.
Multimodal Urban Areas of Interest Generation via Remote Sensing Imagery and Geographical Prior
Unlike conventional AOI generation methods, such as the Road-cut method that segments road networks at various levels, our approach diverges from semantic segmentation algorithms that depend on pixel-level classification.
Transportation Marketplace Rate Forecast Using Signature Transform
Freight transportation marketplace rates are typically challenging to forecast accurately.
SkySense: A Multi-Modal Remote Sensing Foundation Model Towards Universal Interpretation for Earth Observation Imagery
Prior studies on Remote Sensing Foundation Model (RSFM) reveal immense potential towards a generic model for Earth Observation.
Fast Policy Learning for Linear Quadratic Control with Entropy Regularization
This paper proposes and analyzes two new policy learning methods: regularized policy gradient (RPG) and iterative policy optimization (IPO), for a class of discounted linear-quadratic control (LQC) problems over an infinite time horizon with entropy regularization.
Risk of Transfer Learning and its Applications in Finance
Transfer learning is an emerging and popular paradigm for utilizing existing knowledge from previous learning tasks to improve the performance of new ones.
The Rise and Potential of Large Language Model Based Agents: A Survey
Many efforts have been made to develop intelligent agents, but they mainly focus on advancement in algorithms or training strategies to enhance specific capabilities or performance on particular tasks.
FinEval: A Chinese Financial Domain Knowledge Evaluation Benchmark for Large Language Models
Our work offers a more comprehensive financial knowledge evaluation benchmark, utilizing data of mock exams and covering a wide range of evaluated LLMs.
Transfer Learning for Portfolio Optimization
In particular, 1. a strong correlation between the transfer risk and the overall performance of transfer learning methods is established, underscoring the significance of transfer risk as a viable indicator of "transferability"; 2. transfer risk is shown to provide a computationally efficient way to identify appropriate source tasks in transfer learning, enhancing the efficiency and effectiveness of the transfer learning approach; 3. additionally, the numerical experiments offer valuable new insights for portfolio management across these different settings.
MESOB: Balancing Equilibria & Social Optimality
Motivated by bid recommendation in online ad auctions, this paper considers a general class of multi-level and multi-agent games, with two major characteristics: one is a large number of anonymous agents, and the other is the intricate interplay between competition and cooperation.
Feasibility of Transfer Learning: A Mathematical Framework
Transfer learning is a popular paradigm for utilizing existing knowledge from previous learning tasks to improve the performance of new ones.
Markov $α$-Potential Games
In this new framework, Markov games are shown to be Markov $\alpha$-potential games, and the existence of an associated $\alpha$-potential function is established.
On Consistency of Signatures Using Lasso
Signature transforms are iterated path integrals of continuous and discrete-time time series data, and their universal nonlinearity linearizes the problem of feature selection.
Feasibility and Transferability of Transfer Learning: A Mathematical Framework
In this paper we build for the first time, to the best of our knowledge, a mathematical framework for the general procedure of transfer learning.
Simultaneously Short- and Long-Term Temporal Modeling for Semi-Supervised Video Semantic Segmentation
In this work, we propose a novel feature enhancement network to simultaneously model short- and long-term temporal correlation.
Uncertainty-guided Learning for Improving Image Manipulation Detection
Image manipulation detection (IMD) is of vital importance as faking images and spreading misinformation can be malicious and harm our daily life.
Learning Weather-General and Weather-Specific Features for Image Restoration Under Multiple Adverse Weather Conditions
Inspired by this observation, we design an efficient unified framework with a two-stage training strategy to explore the weather-general and weather-specific features.
Capacity dependent analysis for functional online learning algorithms
This article provides convergence analysis of online stochastic gradient descent algorithms for functional linear models.
Meta-learning with GANs for anomaly detection, with deployment in high-speed rail inspection system
Anomaly detection has been an active research area with a wide range of potential applications.
Transfer Learning for Retinal Vascular Disease Detection: A Pilot Study with Diabetic Retinopathy and Retinopathy of Prematurity
Recently, deep learning techniques have been successfully applied for detection of diabetic retinopathy (DR).
SiamTrans: Zero-Shot Multi-Frame Image Restoration with Pre-Trained Siamese Transformers
For zero-shot image restoration, we design a novel model, termed SiamTrans, which is constructed by Siamese transformers, encoders, and decoders.
Extracting and Measuring Uncertain Biomedical Knowledge from Scientific Statements
Design/methodology/approach: Taking cardiovascular research publications in China as a sample, we extracted the SPO triples as knowledge unit and the hedging/conflicting uncertainties as the knowledge context.
Convergence of GANs Training: A Game and Stochastic Control Methodology
Training generative adversarial networks (GANs) is known to be difficult, especially for financial time series.
Theoretical Guarantees of Fictitious Discount Algorithms for Episodic Reinforcement Learning and Global Convergence of Policy Gradient Methods
To our best knowledge, this is the first theoretical guarantee on fictitious discount algorithms for the episodic reinforcement learning of finite-time-horizon MDPs, which also leads to the (first) global convergence of policy gradient methods for finite-time-horizon episodic reinforcement learning.
FedSkel: Efficient Federated Learning on Heterogeneous Systems with Skeleton Gradients Update
Federated learning aims to protect users' privacy while performing data analysis from different participants.
Mean-Field Multi-Agent Reinforcement Learning: A Decentralized Network Approach
This paper proposes a framework of localized training and decentralized execution to study MARL with network of states.
Multi-agent Reinforcement Learning
reinforcement-learning
+1
RoSearch: Search for Robust Student Architectures When Distilling Pre-trained Language Models
In order to overcome these security problems, RoSearch is proposed as a comprehensive framework to search the student models with better adversarial robustness when performing knowledge distillation.
Adversarial Training for Gradient Descent: Analysis Through its Continuous-time Approximation
Adversarial training has gained great popularity as one of the most effective defenses for deep neural network and more generally for gradient-based machine learning models against adversarial perturbations on data points.
Discriminative-Generative Dual Memory Video Anomaly Detection
In this paper, we propose a DiscRiminative-gEnerative duAl Memory (DREAM) anomaly detection model to take advantage of a few anomalies and solve data imbalance.
Generative Adversarial Network: Some Analytical Perspectives
Ever since its debut, generative adversarial networks (GANs) have attracted tremendous amount of attention.
Reinforcement learning for linear-convex models with jumps via stability analysis of feedback controls
We study finite-time horizon continuous-time linear-convex reinforcement learning problems in an episodic setting.
Delay Analysis of Wireless Federated Learning Based on Saddle Point Approximation and Large Deviation Theory
Federated learning (FL) is a collaborative machine learning paradigm, which enables deep learning model training over a large volume of decentralized data residing in mobile devices without accessing clients' private data.
Edge-Labeling based Directed Gated Graph Network for Few-shot Learning
In this paper, we proposed an edge-labeling-based directed gated graph network (DGGN) for few-shot learning, which utilizes gated recurrent units to implicitly update the similarity between nodes.
Universal Silicon Microwave Photonic Spectral Shaper
Optical modulation plays arguably the utmost important role in microwave photonic (MWP) systems.
Continual Learning Long Short Term Memory
Catastrophic forgetting in neural networks indicates the performance decreasing of deep learning models on previous tasks while learning new tasks.
Entropy Regularization for Mean Field Games with Learning
In addition, this study leads to a policy-gradient algorithm for exploration in MFG.
DanceIt: Music-inspired Dancing Video Synthesis
To explore the relationship between music and dance movements, we propose a cross-modal alignment module that focuses on dancing video clips, accompanied on pre-designed music, to learn a system that can judge the consistency between the visual features of pose sequences and the acoustic features of music.
Kernel-based L_2-Boosting with Structure Constraints
Numerically, we carry out a series of simulations to show the promising performance of KReBooT in terms of its good generalization, near over-fitting resistance and structure constraints.
Out-of-distribution Generalization via Partial Feature Decorrelation
Most deep-learning-based image classification methods assume that all samples are generated under an independent and identically distributed (IID) setting.
Logarithmic regret for episodic continuous-time linear-quadratic reinforcement learning over a finite-time horizon
We study finite-time horizon continuous-time linear-quadratic reinforcement learning problems in an episodic setting, where both the state and control coefficients are unknown to the controller.
PAC-Bayesian Generalization Bounds for MultiLayer Perceptrons
We study PAC-Bayesian generalization bounds for Multilayer Perceptrons (MLPs) with the cross entropy loss.
Early Detection of Retinopathy of Prematurity (ROP) in Retinal Fundus Images Via Convolutional Neural Networks
Retinopathy of prematurity (ROP) is an abnormal blood vessel development in the retina of a prematurely-born infant or an infant with low birth weight.
SDE approximations of GANs training and its long-run behavior
This paper analyzes the training process of GANs via stochastic differential equations (SDEs).
Escaping Saddle Points Efficiently with Occupation-Time-Adapted Perturbations
Motivated by the super-diffusivity of self-repelling random walk, which has roots in statistical physics, this paper develops a new perturbation mechanism for optimization algorithms.
A General Framework for Learning Mean-Field Games
This paper presents a general mean-field game (GMFG) framework for simultaneous learning and decision-making in stochastic games with a large population.
Prediction and analysis of Coronavirus Disease 2019
According to the current trend, based on the three models, the total number of people expected to be infected is 49852-57447 in Wuhan, 12972-13405 in non-Hubei areas and 80261-85140 in China respectively.
Populations and Evolution
Audio-video Emotion Recognition in the Wild using Deep Hybrid Networks
This paper presents an audiovisual-based emotion recognition hybrid network.
Mean-Field Controls with Q-learning for Cooperative MARL: Convergence and Complexity Analysis
Multi-agent reinforcement learning (MARL), despite its popularity and empirical success, suffers from the curse of dimensionality.
Connecting GANs, MFGs, and OT
Generative adversarial networks (GANs) have enjoyed tremendous success in image generation and processing, and have recently attracted growing interests in financial modelings.
Automatic Group Cohesiveness Detection With Multi-modal Features
The task is to predict the cohesive level for a group of people in images.
Graph Neural Networks for Image Understanding Based on Multiple Cues: Group Emotion Recognition and Event Recognition as Use Cases
A graph neural network (GNN) for image understanding based on multiple cues is proposed in this paper.
Accelerating CNN Training by Pruning Activation Gradients
Sparsification is an efficient approach to accelerate CNN inference, but it is challenging to take advantage of sparsity in training procedure because the involved gradients are dynamically changed.
POG: Personalized Outfit Generation for Fashion Recommendation at Alibaba iFashion
In particular, there exist two requirements for fashion outfit recommendation: the Compatibility of the generated fashion outfits, and the Personalization in the recommendation process.
Transaction Cost Analytics for Corporate Bonds
This part is on the time scale of each transaction of liquid corporate bonds, and is by applying a transient impact model to estimate the price impact kernel using a non-parametric method.
Sparsemax and Relaxed Wasserstein for Topic Sparsity
As topic sparsity of individual documents in online social media increases, so does the difficulty of analyzing the online text sources using traditional methods.
A class of stochastic games and moving free boundary problems
In this paper we propose and analyze a class of $N$-player stochastic games that include finite fuel stochastic games as a special case.
Smile detection in the wild based on transfer learning
Compared to the size of databases for face recognition, far less labeled data is available for training smile detection systems.
Ambiguity set and learning via Bregman and Wasserstein
Construction of ambiguity set in robust optimization relies on the choice of divergences between probability distributions.
Relaxed Wasserstein with Applications to GANs
Wasserstein Generative Adversarial Networks (WGANs) provide a versatile class of models, which have attracted great attention in various applications.
Distributed learning with regularized least squares
We study distributed learning with the least squares regularization scheme in a reproducing kernel Hilbert space (RKHS).
Robust Object Co-detection
Object co-detection aims at simultaneous detection of objects of the same category from a pool of related images by exploiting consistent visual patterns present in candidate objects in the images.
Towards a Mathematical Foundation of Immunology and Amino Acid Chains
We attempt to set a mathematical foundation of immunology and amino acid chains.
