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Found 18 papers, 4 papers with code
期货领域知识图谱构建(Construction of Knowledge Graph in Futures Field)
“期货领域是数据最丰富的领域之一, 本文以商品期货的研究报告为数据来源构建了期货领域知识图谱(Commodity Futures Knowledge Graph, CFKG)。以期货产品为核心, 确立了概念分类体系及关系描述体系, 形成图谱的概念层;在MHS-BIA与GPN模型的基础上, 通过领域专家指导对242万字的研报文本进行标注与校对, 形成了CFKG数据层, 并设计了可视化查询系统。所构建的CFKG包含17, 003个农产品期货关系三元组、13, 703种非农产品期货关系三元组, 为期货领域文本分析、舆情监控和推理决策等应用提供知识支持。”
Constructing Non-Markovian Decision Process via History Aggregator
In the domain of algorithmic decision-making, non-Markovian dynamics manifest as a significant impediment, especially for paradigms such as Reinforcement Learning (RL), thereby exerting far-reaching consequences on the advancement and effectiveness of the associated systems.
Quantum-Classical Hybrid Quantized Neural Network
Additionally, we provide an upper bound on the Time-To-Solution for the QCBO solving process.
Mxplainer: Explain and Learn Insights by Imitating Mahjong Agents
People need to internalize the skills of AI agents to improve their own capabilities.
InterMT: Multi-Turn Interleaved Preference Alignment with Human Feedback
To compensate for the lack of capability for multi-modal understanding and generation, we introduce an agentic workflow that leverages tool-augmented MLLMs to construct multi-turn QA instances.
A Note On Deterministic Submodular Maximization With Bounded Curvature
We show that the recent breakthrough result of [Buchbinder and Feldman, FOCS'24] could further lead to a deterministic $(1-\kappa_{f}/e-\varepsilon)$-approximate algorithm for maximizing a submodular function with curvature $\kappa_{f}$ under matroid constraint.
Business Process Text Sketch Automation Generation Using Large Language Model
Business process document generation is a crucial stage in BPM.
Evolutionary Game-Theoretical Analysis for General Multiplayer Asymmetric Games
First, there is inaccuracy when analysing the simplified payoff table.
A Note on Optimizing the Ratio of Monotone Supermodular Functions
We show that for the problem of minimizing (or maximizing) the ratio of two supermodular functions, no bounded approximation ratio can be achieved via polynomial number of queries, if the two supermodular functions are both monotone non-decreasing or non-increasing.
Submodular Maximization in Clean Linear Time
In this paper, we provide the first deterministic algorithm that achieves the tight $1-1/e$ approximation guarantee for submodular maximization under a cardinality (size) constraint while making a number of queries that scales only linearly with the size of the ground set $n$.
Viral Pneumonia Screening on Chest X-ray Images Using Confidence-Aware Anomaly Detection
In this paper, we formulate the task of differentiating viral pneumonia from non-viral pneumonia and healthy controls into an one-class classification-based anomaly detection problem, and thus propose the confidence-aware anomaly detection (CAAD) model, which consists of a shared feature extractor, an anomaly detection module, and a confidence prediction module.
Artificial Intelligence for Prosthetics - challenge solutions
In the NeurIPS 2018 Artificial Intelligence for Prosthetics challenge, participants were tasked with building a controller for a musculoskeletal model with a goal of matching a given time-varying velocity vector.
Layout Design for Intelligent Warehouse by Evolution with Fitness Approximation
With the rapid growth of the express industry, intelligent warehouses that employ autonomous robots for carrying parcels have been widely used to handle the vast express volume.
N2RPP: An Adversarial Network to Rebuild Plantar Pressure for ACLD Patients
Plantar pressure is one of which contains this information and it describes human walking features.
A Model for Medical Diagnosis Based on Plantar Pressure
Here, we propose a model using convolutional neural network based on plantar pressure for medical diagnosis.
ICFVR 2017: 3rd International Competition on Finger Vein Recognition
The development of finger vein recognition algorithms heavily depends on large-scale real-world data sets.
Learning to Design Games: Strategic Environments in Reinforcement Learning
In typical reinforcement learning (RL), the environment is assumed given and the goal of the learning is to identify an optimal policy for the agent taking actions through its interactions with the environment.
A Fusion Method Based on Decision Reliability Ratio for Finger Vein Verification
Our work is first to define decision reliability ratio to quantify this confidence, and then propose the Maximum Decision Reliability Ratio (MDRR) fusion method incorporating Weighted Voting.
