651 Result(s)
within
English
Artificial Intelligence
Computer Appl. in Social and Behavioral Sciences
Information Systems Applications (incl. Internet)
**g WANG
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Chapter and Conference Paper
CO-AutoML: An Optimizable Automated Machine Learning System
In recent years, many automated machine learning (AutoML) techniques are proposed for the automatic selection or design machine learning models. They bring great convenience to the use of machine learning techniq...
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Chapter and Conference Paper
Quantum Entanglement Inspired Correlation Learning for Classification
Correlation is an important information resource, which is often used as a fundamental quantity for modeling tasks in machine learning. Since correlation between quantum entangled systems often surpasses that ...
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Chapter and Conference Paper
ECCKG: An Eventuality-Centric Commonsense Knowledge Graph
Eventuality-centric knowledge graphs are essential resources for many downstream applications. However, current knowledge graphs mainly focus on knowledge about entities while ignoring the real-world eventuali...
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Chapter and Conference Paper
Input Enhanced Logarithmic Factorization Network for CTR Prediction
Factorization-based methods, which can automatically model second-order or higher-order cross features, have been the benchmark models for click-through rate (CTR) prediction. In general, they enumerate all cr...
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Chapter and Conference Paper
Inter-and-Intra Domain Attention Relational Inference for Rack Temperature Prediction in Data Center
In a data center, predicting the rack temperature then generating alarms when an exception is detected can prevent server failure caused by high rack temperature. Each measuring point records the temperature o...
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Chapter and Conference Paper
Finding Hidden Patterns in High Resolution Wind Flow Model Simulations
Wind flow data is critical in terms of investment decisions and policy making. High resolution data from wind flow model simulations serve as a supplement to the limited resource of original wind flow data col...
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Chapter and Conference Paper
Construction Research and Applications of Industry Chain Knowledge Graphs
Research on listed companies is an important part of stock analysis. This study proposes an automatic construction method of the knowledge graph for the financial industry chain, which can better track and stu...
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Chapter and Conference Paper
S \(^2\) QL: Retrieval Augmented Zero-Shot Question Answering over Knowledge Graph
Knowledge Graph Question Answering (KGQA) is a challenging task that aims to obtain the entities from the given Knowledge Graph (KG) to answer the user’s natural language questions. Most existing studies are f...
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Chapter and Conference Paper
CKGAC: A Commonsense Knowledge Graph About Attributes of Concepts
This paper presents a method for building a large commonsense knowledge graph about attributes of concepts, called CKGAC. CKGAC contains triples that connect concepts (nouns) with attribute values (adjectives)...
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Chapter and Conference Paper
Rethinking Adjacent Dependency in Session-Based Recommendations
Session-based recommendations (SBRs) recommend the next item for an anonymous user by modeling the dependencies between items in a session. Benefiting from the superiority of graph neural networks (GNN) in learni...
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Chapter and Conference Paper
Goal Generation and Management in NARS
AGI systems should be able to pursue their many goals autonomously while operating in realistic environments which are complex, dynamic, and often novel. This paper discusses the theory and mechanisms for goal...
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Chapter and Conference Paper
Concurrent Transformer for Spatial-Temporal Graph Modeling
Previous studies have shown that concurrently extracting spatial and temporal information is a better way to model spatial-temporal data. However, in these studies, the receptive field has been fixed to constr...
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Chapter and Conference Paper
IMDb30: A Multi-relational Knowledge Graph Dataset of IMDb Movies
Most knowledge graph embedding (KGE) models are trained and evaluated through common benchmark datasets such as WN18 and FB15k. However, these datasets belong to the general filed and have been utilized as lin...
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Chapter and Conference Paper
Definition-Augmented Jointly Training Framework for Intention Phrase Mining
We propose to mine intention phrases from large numbers of queries, for enabling rich query interpretation that identifies both query intentions and associated intention types. We formalize the notion of inten...
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Chapter and Conference Paper
A Novel Bayesian Deep Learning Approach to the Downscaling of Wind Speed with Uncertainty Quantification
Wind plays a crucial part during adverse events, such as storms and wildfires, and is a widely leveraged source of renewable energy. Predicting long-term daily local wind speed is critical for effective monito...
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Chapter and Conference Paper
RShield: A Refined Shield for Complex Multi-step Attack Detection Based on Temporal Graph Network
Complex multi-step attacks (i.e., CMA) have caused severe damage to core information infrastructures of many organizations. The graph-based methods are well known as the ability for learning complex interactio...
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Chapter and Conference Paper
Effects of Microblog Comments on Chinese User's Sentiment with COVID-19 Epidemic Topics
Social media is one of the most significant sources of information in modern people’s life. Due to the large quantity of user base and public opinions, when people read a blog post, the different tendencies of...
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Chapter and Conference Paper
Multi-Asset Market Making via Multi-Task Deep Reinforcement Learning
Market making (MM) is a trading activity by an individual market participant or a member firm of an exchange that buys and sells same securities with the primary goal of profiting on the bid-ask spread, which ...
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Chapter and Conference Paper
BGC: Multi-agent Group Belief with Graph Clustering
Recent advances have witnessed that value decomposed-based multi-agent reinforcement learning methods make an efficient performance in coordination tasks. Most current methods assume that agents can communicat...
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Chapter and Conference Paper
An Explanation Module for Deep Neural Networks Facing Multivariate Time Series Classification
Deep neural networks currently achieve state-of-the-art performance in many multivariate time series classification (MTSC) tasks, which are crucial for various real-world applications. However, the black-box c...
