Papers PICO
“PICO” 태그가 달린 논문 68편 · 필터 해제
Designing Interpretable ML System to Enhance Trust in Healthcare: A Systematic Review to Proposed Responsible Clinician-AI-Collaboration Framework
This paper explores the significant impact of AI-based medical devices, including wearables, telemedicine, large language models, and digital twins, on clinical decision support systems. It emphasizes the importance of p…
Model SelectionPICOBio-SIEVE: Exploring Instruction Tuning Large Language Models for Systematic Review Automation
Medical systematic reviews can be very costly and resource intensive. We explore how Large Language Models (LLMs) can support and be trained to perform literature screening when provided with a detailed set of selection …
PICOTowards Effective Visual Representations for Partial-Label Learning
Under partial-label learning (PLL) where, for each training instance, only a set of ambiguous candidate labels containing the unknown true label is accessible, contrastive learning has recently boosted the performance of…
Contrastive Learningimage-classificationImage ClassificationPartial Label Learning+2Intermittent Upwelling Events Trigger Delayed, Major, and Reproducible Pico-Nanophytoplankton Responses in Coastal Oligotrophic Waters
Pico-nanophytoplankton organisms are dominant in oceanic oligotrophic areas but their adaptive growth rates make their contribution to the carbon cycle difficult to estimate. Here we address their response capacities aft…
PICOA Sequential Concept Drift Detection Method for On-Device Learning on Low-End Edge Devices
A practical issue of edge AI systems is that data distributions of trained dataset and deployed environment may differ due to noise and environmental changes over time. Such a phenomenon is known as a concept drift, and …
Drift DetectionPICO3D Scene Inference from Transient Histograms
Time-resolved image sensors that capture light at pico-to-nanosecond timescales were once limited to niche applications but are now rapidly becoming mainstream in consumer devices. We propose low-cost and low-power imagi…
3D geometryPICOSimulating single-photon detector array sensors for depth imaging
Single-Photon Avalanche Detector (SPAD) arrays are a rapidly emerging technology. These multi-pixel sensors have single-photon sensitivities and pico-second temporal resolutions thus they can rapidly generate depth image…
object-detectionObject DetectionPICOModeling Adaptive Fine-grained Task Relatedness for Joint CTR-CVR Estimation
In modern advertising and recommender systems, multi-task learning (MTL) paradigm has been widely employed to jointly predict diverse user feedbacks (e.g. click and purchase). While, existing MTL approaches are either ri…
Contrastive LearningMulti-Task LearningPICORecommendation SystemsDeepPicarMicro: Applying TinyML to Autonomous Cyber Physical Systems
Running deep neural networks (DNNs) on tiny Micro-controller Units (MCUs) is challenging due to their limitations in computing, memory, and storage capacity. Fortunately, recent advances in both MCU hardware and machine …
PICOPre-trained language models with domain knowledge for biomedical extractive summarization
Biomedical text summarization is a critical task for comprehension of an ever-growing amount of biomedical literature. Pre-trained language models (PLMs) with transformer-based architectures have been shown to greatly im…
Extractive SummarizationPICOText SummarizationPixel-level Correspondence for Self-Supervised Learning from Video
While self-supervised learning has enabled effective representation learning in the absence of labels, for vision, video remains a relatively untapped source of supervision. To address this, we propose Pixel-level Corres…
Contrastive Learningimage-classificationImage ClassificationOptical Flow Estimation+3Low-complexity Three-dimensional Discrete Hartley Transform Approximations for Medical Image Compression
The discrete Hartley transform (DHT) is a useful tool for medical image coding. The three-dimensional DHT (3D DHT) can be employed to compress medical image data, such as magnetic resonance and X-ray angiography. However…
Image CompressionPICOSSIMIntra-Template Entity Compatibility based Slot-Filling for Clinical Trial Information Extraction
We present a deep learning based information extraction system that can extract the design and results of a published abstract describing a Randomized Controlled Trial (RCT). In contrast to other approaches, our system d…
PICOslot-fillingSlot FillingDISTANT-CTO: A Zero Cost, Distantly Supervised Approach to Improve Low-Resource Entity Extraction Using Clinical Trials Literature
PICO recognition is an information extraction task for identifying participant, intervention, comparator, and outcome information from clinical literature. Manually identifying PICO information is the most time-consuming…
named-entity-recognitionNamed Entity RecognitionNamed Entity Recognition (NER)NER+1LinkBERT: Pretraining Language Models with Document Links
Language model (LM) pretraining can learn various knowledge from text corpora, helping downstream tasks. However, existing methods such as BERT model a single document, and do not capture dependencies or knowledge that s…
Document ClassificationLanguage ModelingLanguage ModellingMasked Language Modeling+10Addressing Gap between Training Data and Deployed Environment by On-Device Learning
The accuracy of tinyML applications is often affected by various environmental factors, such as noises, location/calibration of sensors, and time-related changes. This article introduces a neural network based on-device …
Anomaly DetectionPICOMulti-Agent Path Finding with Prioritized Communication Learning
Multi-agent pathfinding (MAPF) has been widely used to solve large-scale real-world problems, e.g., automation warehouses. The learning-based, fully decentralized framework has been introduced to alleviate real-time prob…
Multi-Agent Path FindingMulti-agent Reinforcement LearningPICOPiCO+: Contrastive Label Disambiguation for Robust Partial Label Learning
Partial label learning (PLL) is an important problem that allows each training example to be labeled with a coarse candidate set, which well suits many real-world data annotation scenarios with label ambiguity. Despite t…
Contrastive LearningPartial Label LearningPICORepresentation LearningContrastive Label Disambiguation for Partial Label Learning
Partial label learning (PLL) is an important problem that allows each training example to be labeled with a coarse candidate set, which well suits many real-world data annotation scenarios with label ambiguity. Despite …
Contrastive LearningPartial Label LearningPICORepresentation LearningTruth Discovery in Sequence Labels from Crowds
Annotation quality and quantity positively affect the learning performance of sequence labeling, a vital task in Natural Language Processing. Hiring domain experts to annotate a corpus is very costly in terms of money an…
named-entity-recognitionNamed Entity RecognitionNamed Entity Recognition (NER)NER+1