A5100373007- Advanced Graph Neural Networks
- Recommender Systems and Techniques
- Topic Modeling
- Natural Language Processing Techniques
- Web Data Mining and Analysis
- Advanced Chemical Sensor Technologies
- Air Quality Monitoring and Forecasting
- Industrial Technology and Control Systems
- Anomaly Detection Techniques and Applications
- Information Retrieval and Search Behavior
- Data Mining Algorithms and Applications
- Slime Mold and Myxomycetes Research
- Real-time simulation and control systems
- Soil Mechanics and Vehicle Dynamics
- Microtubule and mitosis dynamics
- Intraperitoneal and Appendiceal Malignancies
- Osteoarthritis Treatment and Mechanisms
- Genetic Associations and Epidemiology
- Structural Engineering and Vibration Analysis
- Advanced Clustering Algorithms Research
- Topological and Geometric Data Analysis
- Energy Load and Power Forecasting
- Optical Imaging and Spectroscopy Techniques
- Learning Styles and Cognitive Differences
- Geomechanics and Mining Engineering
Suzhou Research Institute
2023
Shanghai University
2023
Binzhou University
2022
Harvard University Press
2022
Beijing Institute of Technology
2022
Zhuhai Institute of Advanced Technology
2022
Binzhou Medical University
2022
Wuhan University of Technology
2018
Nanjing University of Aeronautics and Astronautics
2007-2014
Google (United States)
2013
Various approaches have been proposed for out-of-distribution (OOD) detection by augmenting models, input examples, training sets, and optimization objectives. Deviating from existing work, we a simple hypothesis that standard off-the-shelf models may already contain sufficient information about the set distribution which can be leveraged reliable OOD detection. Our empirical study on validating this hypothesis, measures model activation's mean in-distribution (ID) minibatches, surprisingly...
Deep-web crawl is concerned with the problem of surfacing hidden content behind search interfaces on Web. While many deep-web sites maintain document-oriented textual (e.g., Wikipedia, PubMed, Twitter, etc.), which has traditionally been focus literature, we observe that a significant portion sites, including almost all online shopping curate structured entities as opposed to text documents. Although crawling such entity-oriented clearly useful for variety purposes, existing techniques...
Patient-derived tumor xenograft (PDX)/organoid (PDO), driven by cancer stem cells (CSC), are considered the most predictive models for translational oncology. Large PDX collections reflective of patient populations have been created and used extensively to test various investigational therapies, including population-trials as surrogate subjects in vivo. PDOs recognized vitro surrogates patients amenable high-throughput screening (HTS). We built a biobank carcinoma PDX-derived organoids...
Generative retrieval constitutes an innovative approach in in- formation retrieval, leveraging generative language models (LM) to generate a ranked list of document identifiers (do- cid) for given query. It simplifies the pipeline by replacing large external index with model parameters. However, existing works merely learned relationship be- tween queries and identifiers, which is unable directly represent relevance between docu- ments. To address above problem, we propose novel general...
Generative retrieval constitutes an innovative approach in information retrieval, leveraging generative language models(LM) to generate a ranked list of document identifiers (docid) for given query. It simplifies the pipeline by replacing large external index with model parameters. However, existing works merely learned relationship between queries and identifiers, which is unable directly represent relevance documents. To address above problem, we propose novel general framework, namely...
The purpose of this study was to examine the role personality traits on academic performance. Furthermore, also aims at exploring effects virtual experience (mediator) and emotional intelligence (moderator) between performance students. findings imply that are strong predictors better However, several do not have a positive impact further suggests students who abilities more likely perform well in their academics. population research consists various colleges universities developing regions....
Knowledge graphs (KGs) have emerged as a compelling abstraction for organizing the world's structured knowledge and integrating information extracted from multiple data sources. They are also beginning to play central role in representing by AI systems, improving predictions of systems giving them expressed KGs input. The goals this article (a) introduce discuss important areas application that gained recent prominence; (b) situate context prior work AI; (c) present few contrasting...
A session-based recommendation system (SRS) tries to predict the next possible choice of anonymous users. In recent years, graph neural network (GNN) models have been successfully applied SRSs and achieved great success. Using GNN in SRSs, each session is processed successively obtain embedding node (i.e, action on an item), which then imported into prediction module generate results. However, solely depending embeddings not sufficient because only involves a few items. Therefore, neighbor...
The data cube and iceberg computation problem has been studied by many researchers. There are three major approaches developed in this direction: (1) top-down computation, represented MultiWay array aggregation (Zhao et. al., 1997) which utilizes shared performs well on dense sets; (2) bottom-up BUC (Beyer Ramakrishnan, 1999), takes advantage of Apriori Pruning sparse (3) integrated Star-Cubing (Xin, 2003), advantages both high performance most cases. However; the degrades very sets due to...
The correlation analysis of telemetry data plays a significant role in satellite performance analysis. However, the existing methods cannot be well applied, because is large and high-dimensional. In this paper, an efficient algorithm named QARC Apriori proposed. First, to reduce redundant attributes lower problem complexity, grey relational method applied. Second, each filtered attribute partitioned into several subintervals, combining with K-Means clustering algorithm. During clustering,...
Mobile edge devices see increased demands in deep neural networks (DNNs) inference while suffering from stringent constraints computing resources. Split (SC) emerges as a popular approach to the issue by executing only initial layers on and offloading remaining cloud. Prior works usually assume that SC offers privacy benefits intermediate features, instead of private data, are shared In this work, we debunk SC-induced protection (i) presenting novel data-free model inversion method (ii)...
Large Language Models (LLMs) have demonstrated impressive in-context learning (ICL) capabilities from few-shot demonstration exemplars. While recent learning-based selection methods proven beneficial to ICL by choosing more useful exemplars, their underlying mechanisms are opaque, hindering efforts address limitations such as high training costs and poor generalization across tasks. These generally assume the process captures similarities between exemplar target instance, however, it remains...
In this paper, based on the characteristic analyzing of mechanical fuel injection system for marine medium-speed diesel engine, a sectional high-pressure common rail is designed, rated condition pressure which 160MPa. The simulation model built and performance high analyzed, research results provide technical foundation engineering development.
Building a universal conversational agent has been long-standing goal of the dialogue research community. Most previous works only focus on small set tasks. In this work, we aim to build unified foundation model (DFM) which can be used solve massive diverse To achieve goal, large-scale well-annotated dataset with rich task diversity (DialogZoo) is collected. We introduce framework unify all tasks and propose novel auxiliary self-supervised stable training DFM highly large scale DialogZoo...
Single-mode recognition method remains a difficulty problem in target detection and of road vehicle targets complex urban situations. Hence, using the advantages obtaining different feature information from infrared visible images situations is considered. We propose level image fusion based on deep learning. This first obtains registered image, extracts features respectively through two main extraction networks, passes layer, into pyramid network to obtain effective then carries out...
Various approaches have been proposed for out-of-distribution (OOD) detection by augmenting models, input examples, training sets, and optimization objectives. Deviating from existing work, we a simple hypothesis that standard off-the-shelf models may already contain sufficient information about the set distribution which can be leveraged reliable OOD detection. Our empirical study on validating this hypothesis, measures model activation's mean in-distribution (ID) mini-batches, surprisingly...
Prediction models for click-through rate (CTR) learn feature interactions underlying user behaviors, which are crucial in recommendation systems. Due to their size and complexity, existing approaches have a limited range of applications. In order decrease inference delay, knowledge distillation techniques been used the student model's lower capacity, process is less effective when there significant difference complexity network architecture between teacher model model.
Numerical features are an important type of input for CTR prediction models. Recently, several discretization and numerical transformation methods have been proposed to deal with features. However, existing approaches do not fully consider compatibility different distributions. Here, we propose a novel feature embedding framework, called Distribution-Aware Embedding (DAE), which is applicable various First, DAE efficiently approximates the cumulative distribution function by estimating...
Conversion rate (CVR) prediction models play a vital role in recommendation systems. Recent research shows that learning unified model to serve multiple scenarios is effective for improving overall performance. However, it remains challenging improve performance across at low parameter cost, and current solutions are hard robustly multi-scenario diversity. In this paper, we propose MI-DPG the CVR prediction, which learns scenario-conditioned dynamic parameters each scenario more efficient...