Titlebook: Advanced Intelligent Computing Technology and Applications; 20th International C De-Shuang Huang,Xiankun Zhang,Qinhu Zhang Conference proce

萬靈丹

Graph Causal Contrastive for Partial Label Learning powerful for this, they often require substantial annotated data, which can be challenging due to graph sample complexity. Partial Label Learning (PLL) addresses this by allowing imprecise annotations, where each sample has multiple potential labels, with only one being true. Nevertheless, effectiv

crutch

A Stacking Ensemble Deep Learning Model for Stock Price Forecastingchallenging problem. Existing research primarily focuses on constructing complex deep learning models to capture the intrinsic autocorrelation within stocks and to learn intricate nonlinear relationships from stock features. Despite the significant efficacy of deep learning in this domain, two limit

Deference

Smart Trading: A Novel Reinforcement Learning Framework for Quantitative Trading in Noisy Marketsovides a state-of-the-art reinforcement learning framework for training agents to trade in markets, it lacks the necessary approaches to counteract market noise and boost the agent’s learning process in the complex environment. This paper proposes a novel reinforcement learning framework for quantit

Indicative

circuit

Ontology-Aware Overlapping Event Extractionations from text. This task has become challenging when dealing with complex sentences that encompass overlapping sub-events. To address this issue, we propose a novel ontology-aware neural approach for extracting overlapping events. Our approach consists of an Ontology-Aware Semantic Encoder (OASE)

發(fā)電機(jī)

TVD-BERT: A Domain-Adaptation Pre-trained Model for Textural Vulnerability Descriptionslucidating key facets of software vulnerabilities including impacted products, causation, and impacts. Collecting and analyzing TVD holds significant importance in the field of cybersecurity. In order to better analyze TVD, identify potential trends in software vulnerabilities, and reduce the occurr

Neonatal

Obstreperous

織布機(jī)

https://doi.org/10.1007/3-7985-1555-7 paper is close to 500 us. The accuracy of the attack detection constructed on the I5-7200U platform finally reaches 97.39%, while on the I7-7700 platform, it reaches 99.31%. The CPU utilization from Sampling of hardware performance events falls below the 1%. The performance loss of the final build

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