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共 1949 条记录，以下是81-90 订阅

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Interpretable Reinforcement learning of Behavior Trees 23

Interpretable Reinforcement Learning of Behavior Trees

引用

15th international conference on machine learning and computing, icmlc 2023

作者： Zhao, Chenjing Deng, Chuanshuai Liu, Zhenghui Zhang, Jiexin Wu, Yunlong Wang, Yanzhen Yi, Xiaodong Defense Innovation Institute Beijing China

ISBN: (纸本)9781450398411

The interpretability of reinforcement learning (RL) algorithms has become one of the significant challenges for artificial intelligence (AI) researchers. Behavior Trees (BTs) have enabled developers to design AI policies visually and comprehend the agent's behaviors in robotics and computer games. Combining their strengths, researchers have proposed to utilize the RL algorithm to generate BTs to present learned policies automatically. Existing methods are devoted to the incremental generation or modification of pre-designed BTs. These efforts necessitate specialized knowledge and the manual design of initial BTs. In this paper, we present intelligent generation methods that directly represent the policies generated by Q-learning and its derived algorithms in the form of BTs to enhance the interpretability of RL. We investigate the tradeoff between the size and performance of BTs while attaining interpretability, intending to obtain balanced policies that are easy to comprehend and good in performance. Evaluations in several classic OpenAI Gym environments validate the effectiveness of our methods. © 2023 ACM.

关键词： Reinforcement learning

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Fusing Multi-Granularity Data for Stock Trend Prediction with Contrastive Pre-training 23

Fusing Multi-Granularity Data for Stock Trend Prediction wit...

引用

15th international conference on machine learning and computing, icmlc 2023

作者： Xu, Haonan Li, Jiange Wang, Peng Yin, Xianchen Xue, Wenfang Center for Research on Intelligent Perception and Computing National Laboratory of Pattern Recognition Institute of Automation Chinese Academy of Sciences School of Artificial Intelligence University of Chinese Academy of Sciences Beijing Shi Haidian Qu China Department of Information Technology China Securities Co. Ltd. Beijing Shi China Department of Index and Quantitative Investment China Securities Co. Ltd. Beijing Shi China

ISBN: (纸本)9781450398411

The advances in financial theory and power of computer technology have driven the development of quantitative investing. Stock trend forecasting as an important topic in quantitative investment has attracted more and more researchers. The development of deep learning has also led to the emergence of many interesting models in the field. However, most of the existing models only use daily frequency data to predict the stock price fluctuations, lacking the consideration of high-frequency data and relational data. At the same time, it remains a challenge to incorporate multi-granularity data into model training. Based on this, we propose a contrastive pre-training model fusing high frequency data and daily frequency data(HRC), which construct pre-training tasks with a positive and negative sample design in terms of data granularity and stock relationships. In addition to this, we also constructed a label-based training task using the performance of returns to classify stocks. The experiments on real-world stock dataset show significant improvements of our approach over the baselines. © 2023 Owner/Author.

关键词： Deep learning

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Flu Shot learning: Overview of H1N1 and Seasonal Flu Vaccines

Flu Shot Learning: Overview of H1N1 and Seasonal Flu Vaccine...

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2024 OPJU international Technology conference on Smart computing for Innovation and Advancement in Industry 4.0, OTCON 2024

作者： Kumar, Gulshan Rasane, Atharva Gupta, Sakshi Barker, Divya Kulakarni, Achyut Khot, Radhika Gavade, Priyanka A. Hubballi, Roopa K.L.E Tech. University Department of Cse Karnataka Belagavi India

ISBN: (纸本)9798350373783

This study examines the effectiveness of various machine learning models in predicting individuals' likelihood of receiving both the H1N1 and seasonal flu vaccines. The emergence of the H1N1 flu in 2009 led to a significant global pandemic, prompting widespread vaccination efforts. Utilizing data from a prospective cohort study conducted across emergency departments in the United States and Taiwan from 2015 to 2020, seven machine learning algorithms were developed and compared to detect influenza-positive cases. Linear Discriminant Analysis (LDA) emerged as a promising choice for vaccine prediction tasks, alongside consistent performance demonstrated by Gradient Boosting machines and Neural Networks. Consideration of programmatic needs, such as minimizing false negatives or false positives, is crucial when selecting the appropriate classification model. Models including Random Forest, Logistic Regression, Support Vector machines (SVM), Gradient Boosting machines, and Neural Networks present viable options. These findings contribute to understanding effective machine learning techniques in predicting vaccination outcomes and can inform the development of targeted vaccination campaigns and healthcare strategies to mitigate the impact of future influenza epidemics. Further research and validation of these models in diverse settings are warranted to optimize their applicability and efficacy in real-world scenarios. © 2024 IEEE.

关键词： Support vector machines

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A Review of Face Attribute Editing Based on Deep learning 23

A Review of Face Attribute Editing Based on Deep Learning

引用

15th international conference on machine learning and computing, icmlc 2023

作者： Yun, Pang Zhenping, Qiang Shiqi, Su Qingyang, Chen Hong, Lin Southwest Forestry University Yunnan Kunming China

ISBN: (纸本)9781450398411

Face attribute editing is an important research area in computer vision, which refers to the modification of single or multiple attributes of human faces, such as age, expression, makeup, hairstyle, etc. The application of face attribute editing is popular in social networks because of its diversity, and it has important application value in a huge market. This paper briefly expounds the face attribute editing technology, and reviews the existing face attribute editing methods. We try to collect some methods of face attribute editing based on deep learning and classify them into DFI-based, 3DMM-based, GAN-based, and hybrid-based editing methods, summarize the previously proposed datasets and loss functions commonly used in the network. Finally, we summarize the current situation and future development trend of face attribute editing. © 2023 ACM.

关键词： Deep learning

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Data Assimilation with Ocean Models: A Case Study of Reduced Precision and machine learning in the Gulf of Mexico

Data Assimilation with Ocean Models: A Case Study of Reduced...

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29th international conference on Parallel and Distributed computing (Euro-Par)

作者： Voss, Daniel Tyson, Gary Zavala-Romero, Olmo Bozec, Alexandra Srinivasan, Ashwanth Florida State Univ Tallahassee FL 32304 USA Tendral LLC Key Biscayne FL 33149 USA

ISBN: (纸本)9783031488023;9783031488030

The deployment of increasingly higher resolution environmental observation systems along with higher resolution geophysical models has caused operational data assimilation systems to explore techniques to increase computational performance while maintaining numerical accuracy. Recent research efforts have explored implementing reduced or mixed-precision in geophysical circulation models and data assimilation schemes to validate their numerical accuracy versus full-precision models, or using machine learning techniques to enhance and speed up model simulations and data assimilation. In this paper, we combine the two techniques by examining the effects of coupling a modified, reduced-precision data assimilation system, the Tendral Statistical Interpolation System (T-SIS) version 1.0, with a machine learning model using the HYbrid Coordinate Ocean Model (HYCOM) outputs for a Gulf of Mexico experiment. A Unet type Convolutional Neural Network (CNN) was trained with two years, 2009-2010, of T-SIS reduced-precision assimilation runs. It was tested on two different years, 2002 and 2006, with unique ocean circulation properties. For sea-surface height, the ocean modeling variable, an optimal, reduced-precision trained CNN was capable of predicting the full-precision 2002 and 2006 data assimilation increment with an analysis root mean square error (RMSE) reduction of the same order versus the full-precision trained CNN version.

关键词： reduced precision machine learning ocean modeling high-performance computing data assimilation environmental big data

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Injecting Commonsense Knowledge into Prompt learning for Zero-Shot Text Classification 23

Injecting Commonsense Knowledge into Prompt Learning for Zer...

引用

15th international conference on machine learning and computing, icmlc 2023

作者： Qian, Jing Chen, Qi Yue, Yong Atkinson, Katie Li, Gangmin School of Advanced Technology Xi'An Jiaotong-Liverpool University Suzhou China School of AI and Advanced Computing Xi'An Jiaotong-Liverpool University Suzhou China Department of Computer Science University of Liverpool Liverpool United Kingdom School of Computer Science & Technology University of Bedfordshire Luton United Kingdom

ISBN: (纸本)9781450398411

The combination of pre-training and fine-tuning has become a default solution to Natural Language Processing (NLP) tasks. The emergence of prompt learning breaks such routine, especially in the scenarios of low data resources. Insufficient labelled data or even unseen classes are frequent problems in text classification, equipping Pre-trained Language Models (PLMs) with task-specific prompts helps get rid of the dilemma. However, general PLMs are barely provided with commonsense knowledge. In this work, we propose a KG-driven verbalizer that leverages commonsense Knowledge Graph (KG) to map label words with predefined classes. Specifically, we transform the mapping relationships into semantic relevance in the commonsense-injected embedding space. For zero-shot text classification task, experimental results exhibit the effectiveness of our KG-driven verbalizer on a Twitter dataset for natural disasters (i.e. HumAID) compared with other baselines. © 2023 ACM.

关键词： Knowledge graph

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Design of a Dining Coupon Usage Prediction and Distribution System Based on machine learning 24

Design of a Dining Coupon Usage Prediction and Distribution ...

引用

4th international conference on Artificial Intelligence, Automation and High Performance computing, AIAHPC 2024

作者： Qin, Jilin Aviles, Joey S. Graduate School Angeles University Foundation Angeles City2009 Philippines

ISBN: (纸本)9798400710049

The aim of this study is to design a system for predicting and distributing dining coupons based on machine learning methods for the dining population in urban areas of China. Firstly, by analyzing the consumption habits and behavioral data of diners, we have established a predictive model to forecast users’ dining intentions at specific times and locations. Secondly, combining users’ historical consumption records and personal preferences, we have designed a strategy for distributing dining coupons, and developed and tested the application system using the WeChat platform. Finally, through the validation and analysis of actual data, we have demonstrated the effectiveness and feasibility of this system. This study provides a new marketing method and management approach for the catering industry in urban areas of China, with practical significance and application value for improving the operational efficiency and user experience of the catering industry. © 2024 Copyright held by the owner/author(s).

关键词： Adversarial machine learning

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Surrogate Deep learning to Estimate Uncertainties for Driver Intention Recognition 23

Surrogate Deep Learning to Estimate Uncertainties for Driver...

引用

15th international conference on machine learning and computing, icmlc 2023

作者： Vellenga, Koen Karlsson, Alexander Steinhauer, H. Joe Falkman, Göran Sjogren, Anders University of Skövde Sweden Volvo Car Corporation Sweden

ISBN: (纸本)9781450398411

Real-world applications of artificial intelligence that can potentially harm human beings should be able to express uncertainty about the made predictions. Probabilistic deep learning (DL) methods (e.g., variational inference [VI], VI last layer [VI-LL], Monte-Carlo [MC] dropout, stochastic weight averaging - Gaussian [SWA-G], and deep ensembles) can produce a predictive uncertainty but require expensive MC sampling techniques. Therefore, we evaluated if the probabilistic DL methods are uncertain when making incorrect predictions for an open-source driver intention recognition dataset and if a surrogate DL model can reproduce the uncertainty estimates. We found that all probabilistic DL methods are significantly more uncertain when making incorrect predictions at test time, but there are still instances where the models are very certain but completely incorrect. The surrogate DL models trained on the MC dropout and VI uncertainty estimates were capable of reproducing a significantly higher uncertainty estimate when making incorrect predictions. © 2023 Owner/Author.

关键词： Forecasting

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Optimizing Zero-Knowledge Proofs for Secure Educational Data Storage on Blockchain

Optimizing Zero-Knowledge Proofs for Secure Educational Data...

引用

international conference on machine learning and Cybernetics (icmlc)

作者： DYLAN WARMAN DAVID TIEN MUHAMMAD ASHAD KABIR School of Computing Mathematics and Engineering Charles Sturt University Bathurst New South Wales Australia

ISBN: (数字)9798331528041

ISBN: (纸本)9798331528058

The security of academic credentials is increasingly at risk due to cyberattacks and credential fraud. Traditional verification systems rely on centralised databases, creating single points of failure and privacy concerns. This paper explores Zero-Knowledge Proofs (ZKPs) with blockchain to enhance educational data security. We propose an optimised ZKP protocol for education, improving lightweight infrastructure, scalability, credential revocation, and ease of use. By refining existing techniques, our approach enhances secure, privacy-preserving credential verification, creating a resilient educational data system.

关键词： Data privacy Protocols Scalability Refining Education Memory machine learning Blockchains Fraud Usability

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A Hybrid Search Method for Accelerating Convolutional Neural Architecture Search 23

A Hybrid Search Method for Accelerating Convolutional Neural...

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15th international conference on machine learning and computing, icmlc 2023

作者： Xun, Zhou Songbai, Liu Ka-Chun, Wong Qiuzhen, Lin Kaychen, Tan The Department of Computer Science City University of Hong Kong Hong Kong College of Computer Science and Software Engineering Shenzhen University KA-CHun Wong China College of Computer Science and Software Engineering Shenzhen University China The Department of Computing The Hong Kong Polytechnic University Hong Kong

ISBN: (纸本)9781450398411

The performance evaluation of candidate architectures is a key step in evolution-based neural architecture search (ENAS). Generally, high-fidelity evaluation is desired for finding the optimal architecture but suffers from expensive computational cost while low-fidelity evaluation can reduce search cost but may influence the search performance. In this work, a hybrid search method, HybridNAS, is proposed by synergizing these two evaluation strategies to improve the search effectiveness of ENAS. This method consists of two stages: global exploration and local exploitation. The former applies low-fidelity evaluation to explore the promising architectures efficiently. As for the latter, it exploits the most promising ones identified by the former to generate the better-performancing architectures. Those two stages are iteratively repeated toward the optimal architecture. Extensive experiments on NAS-Bench-101 and NAS-Bench-201 reflect that HybridNAS can achieve comparable accuracy compared to existing popular NAS but at the expense of only 60%-80% computational cost. © 2023 ACM.

关键词： Iterative methods

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