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Found 24 papers, 6 papers with code
Time-aware Heterogeneous Graph Transformer with Adaptive Attention Merging for Health Event Prediction
The widespread application of Electronic Health Records (EHR) data in the medical field has led to early successes in disease risk prediction using deep learning methods.
An Enhanced Grey Wolf Optimizer with Elite Inheritance and Balance Search Mechanisms
To tackle these problems, an enhanced Grey Wolf Optimizer with Elite Inheritance Mechanism and Balance Search Mechanism, named as EBGWO, is proposed to improve the effectiveness of the position updating and the quality of the convergence solutions.
Balancing Information Perception with Yin-Yang: Agent-Based Information Neutrality Model for Recommendation Systems
While preference-based recommendation algorithms effectively enhance user engagement by recommending personalized content, they often result in the creation of ``filter bubbles''.
Detecting misinformation through Framing Theory: the Frame Element-based Model
In this paper, we delve into the rapidly evolving challenge of misinformation detection, with a specific focus on the nuanced manipulation of narrative frames - an under-explored area within the AI community.
Ultra Dual-Path Compression For Joint Echo Cancellation And Noise Suppression
Echo cancellation and noise reduction are essential for full-duplex communication, yet most existing neural networks have high computational costs and are inflexible in tuning model complexity.
BHEISR: Nudging from Bias to Balance -- Promoting Belief Harmony by Eliminating Ideological Segregation in Knowledge-based Recommendations
This approach endeavors to transcend the constraints of the filter bubble, enrich recommendation diversity, and strike a belief balance among users while also catering to user preferences and system-specific business requirements.
AaKOS: Aspect-adaptive Knowledge-based Opinion Summarization
In this paper, we propose an Aspect-adaptive Knowledge-based Opinion Summarization model for product reviews, which effectively captures the adaptive nature required for opinion summarization.
Soft Prompt Guided Joint Learning for Cross-Domain Sentiment Analysis
Aspect term extraction is a fundamental task in fine-grained sentiment analysis, which aims at detecting customer's opinion targets from reviews on product or service.
Simple and Scalable Nearest Neighbor Machine Translation
$k$NN-MT is a straightforward yet powerful approach for fast domain adaptation, which directly plugs pre-trained neural machine translation (NMT) models with domain-specific token-level $k$-nearest-neighbor ($k$NN) retrieval to achieve domain adaptation without retraining.
DOR: A Novel Dual-Observation-Based Approach for News Recommendation Systems
In this paper, we propose a novel method to address the complex problem of news recommendation.
Anti-aliasing Predictive Coding Network for Future Video Frame Prediction
Inspired by the predictive coding hypothesis and related works, the total model is updated through a combination of bottom-up and top-down information flows, which can enhance the interaction between different network levels.
Predictive Coding Based Multiscale Network with Encoder-Decoder LSTM for Video Prediction
We present a multi-scale predictive coding model for future video frames prediction.
KATSum: Knowledge-aware Abstractive Text Summarization
The results reveal that the proposed framework can effectively utilise the information from Knowledge Graph and significantly reduce the factual errors in the summary.
A Light-weight, Effective and Efficient Model for Label Aggregation in Crowdsourcing
Due to the dynamic nature, the proposed algorithms can also estimate true labels online without re-visiting historical data.
Swinv2-Imagen: Hierarchical Vision Transformer Diffusion Models for Text-to-Image Generation
Recently, diffusion models have been proven to perform remarkably well in text-to-image synthesis tasks in a number of studies, immediately presenting new study opportunities for image generation.
Ranked #1 on
Text-to-Image Generation
on Multi-Modal-CelebA-HQ
Pyramidal Predictive Network: A Model for Visual-frame Prediction Based on Predictive Coding Theory
The update frequency of neural units on each of the layer decreases with the increasing of network levels, which results in neurons of higher-level can capture information in longer time dimensions.
GAC: A Deep Reinforcement Learning Model Toward User Incentivization in Unknown Social Networks
The experimental results indicate that GAC can learn and apply effective incentive allocation policies in unknown social networks and outperform existing incentive allocation approaches.
Identifying Influential Users in Unknown Social Networks for Adaptive Incentive Allocation Under Budget Restriction
To tackle the aforementioned challenges, in this paper, we propose a novel algorithm for exploring influential users in unknown networks, which can estimate the influential relationships among users based on their historical behaviors and without knowing the topology of the network.
Graph-based Joint Pandemic Concern and Relation Extraction on Twitter
However, detecting concerns in time from massive information in social media turns out to be a big challenge, especially when sufficient manually labeled data is in the absence of public health emergencies, e. g., COVID-19.
Bidirectional LSTM-CRF Attention-based Model for Chinese Word Segmentation
Chinese word segmentation (CWS) is the basic of Chinese natural language processing (NLP).
ABEM: An Adaptive Agent-based Evolutionary Approach for Mining Influencers in Online Social Networks
A key step in influence maximization in online social networks is the identification of a small number of users, known as influencers, who are able to spread influence quickly and widely to other users.
The 4th International Workshop on Smart Simulation and Modelling for Complex Systems
Computer-based modelling and simulation have become useful tools to facilitate humans to understand systems in different domains, such as physics, astrophysics, chemistry, biology, economics, engineering and social science.
Privacy Information Classification: A Hybrid Approach
A large amount of information has been published to online social networks every day.
Kill Two Birds With One Stone: Boosting Both Object Detection Accuracy and Speed With adaptive Patch-of-Interest Composition
Object detection is an important yet challenging task in video understanding & analysis, where one major challenge lies in the proper balance between two contradictive factors: detection accuracy and detection speed.
