Papers Missing Elements
“Missing Elements” 태그가 달린 논문 46편 · 필터 해제
A Survey of Link Prediction in N-ary Knowledge Graphs
N-ary Knowledge Graphs (NKGs) are a specialized type of knowledge graph designed to efficiently represent complex real-world facts. Unlike traditional knowledge graphs, where a fact typically involves two entities, NKGs …
Knowledge GraphsLink PredictionMissing ElementsGraph Positional Autoencoders as Self-supervised Learners
Graph self-supervised learning seeks to learn effective graph representations without relying on labeled data. Among various approaches, graph autoencoders (GAEs) have gained significant attention for their efficiency an…
Graph Property PredictionMissing ElementsNode ClassificationProperty Prediction+2Automated Feedback Loops to Protect Text Simplification with Generative AI from Information Loss
Understanding health information is essential in achieving and maintaining a healthy life. We focus on simplifying health information for better understanding. With the availability of generative AI, the simplification p…
Missing ElementsSemantic SimilaritySemantic Textual SimilarityText SimplificationCMEdataset Advancing China Map Detection and Standardization with Digital Image Resources
Digital images of Chinas maps play a crucial role in map detection, particularly in ensuring national sovereignty, territorial integrity, and map compliance. However, there is currently no publicly available dataset spec…
Missing ElementsDeep Frequency Attention Networks for Single Snapshot Sparse Array Interpolation
Sparse arrays have been widely exploited in radar systems because of their advantages in achieving large array aperture at low hardware cost, while significantly reducing mutual coupling. However, sparse arrays suffer fr…
Missing ElementsIs Tokenization Needed for Masked Particle Modelling?
In this work, we significantly enhance masked particle modeling (MPM), a self-supervised learning scheme for constructing highly expressive representations of unordered sets relevant to developing foundation models for h…
DecoderMissing ElementsSelf-Supervised LearningAttention Overflow: Language Model Input Blur during Long-Context Missing Items Recommendation
Large language models (LLMs) can suggest missing elements from items listed in a prompt, which can be used for list completion or recommendations based on users' history. However, their performance degrades when presente…
Language ModelingLanguage ModellingMissing ElementsMovie RecommendationAdaptive Reinforcement Learning Planning: Harnessing Large Language Models for Complex Information Extraction
Existing research on large language models (LLMs) shows that they can solve information extraction tasks through multi-step planning. However, their extraction behavior on complex sentences and tasks is unstable, emergin…
Missing ElementsLogiCode: an LLM-Driven Framework for Logical Anomaly Detection
This paper presents LogiCode, a novel framework that leverages Large Language Models (LLMs) for identifying logical anomalies in industrial settings, moving beyond traditional focus on structural inconsistencies. By harn…
Anomaly DetectionBinary ClassificationCode GenerationLogical Reasoning+1MotionCraft: Physics-based Zero-Shot Video Generation
Generating videos with realistic and physically plausible motion is one of the main recent challenges in computer vision. While diffusion models are achieving compelling results in image generation, video diffusion model…
Image GenerationMissing ElementsOptical Flow EstimationVideo GenerationMIDGARD: Self-Consistency Using Minimum Description Length for Structured Commonsense Reasoning
We study the task of conducting structured reasoning as generating a reasoning graph from natural language input using large language models (LLMs). Previous approaches have explored various prompting schemes, yet they s…
Graph GenerationMissing ElementsAnalysing and Organising Human Communications for AI Fairness-Related Decisions: Use Cases from the Public Sector
AI algorithms used in the public sector, e.g., for allocating social benefits or predicting fraud, often involve multiple public and private stakeholders at various phases of the algorithm's life-cycle. Communication iss…
FairnessMissing ElementsFrom Noise to Signal: Unveiling Treatment Effects from Digital Health Data through Pharmacology-Informed Neural-SDE
Digital health technologies (DHT), such as wearable devices, provide personalized, continuous, and real-time monitoring of patient. These technologies are contributing to the development of novel therapies and personaliz…
counterfactualMissing ElementsRandomized Approach to Matrix Completion: Applications in Collaborative Filtering and Image Inpainting
We present a novel method for matrix completion, specifically designed for matrices where one dimension significantly exceeds the other. Our Columns Selected Matrix Completion (CSMC) method combines Column Subset Selecti…
Collaborative FilteringImage InpaintingLow-Rank Matrix CompletionMatrix Completion+2FuseMoE: Mixture-of-Experts Transformers for Fleximodal Fusion
As machine learning models in critical fields increasingly grapple with multimodal data, they face the dual challenges of handling a wide array of modalities, often incomplete due to missing elements, and the temporal ir…
Missing ElementsMixture-of-ExpertsReconstructing the Invisible: Video Frame Restoration through Siamese Masked Conditional Variational Autoencoder
In the domain of computer vision, the restoration of missing information in video frames is a critical challenge, particularly in applications such as autonomous driving and surveillance systems. This paper introduces th…
Autonomous DrivingMissing ElementsGATGPT: A Pre-trained Large Language Model with Graph Attention Network for Spatiotemporal Imputation
The analysis of spatiotemporal data is increasingly utilized across diverse domains, including transportation, healthcare, and meteorology. In real-world settings, such data often contain missing elements due to issues l…
Graph AttentionImputationLanguage ModelingLanguage Modelling+4Low-rank tensor completion via tensor joint rank with logarithmic composite norm
Low-rank tensor completion (LRTC) aims to recover a complete low-rank tensor from incomplete observed tensor, attracting extensive attention in various practical applications such as image processing and computer vision.…
Missing ElementsIHT-Inspired Neural Network for Single-Snapshot DOA Estimation with Sparse Linear Arrays
Single-snapshot direction-of-arrival (DOA) estimation using sparse linear arrays (SLAs) has gained significant attention in the field of automotive MIMO radars. This is due to the dynamic nature of automotive settings, w…
Matrix CompletionMissing ElementsSpecificityMulti-horizon short-term load forecasting using hybrid of LSTM and modified split convolution
Precise short-term load forecasting (STLF) plays a crucial role in the smooth operation of power systems, future capacity planning, unit commitment, and demand response. However, due to its non-stationary and its dep…
Data AblationLoad ForecastingMissing ElementsMultivariate Time Series Forecasting+3