Papers Multi-Task Learning
“Multi-Task Learning” 태그가 달린 논문 4,109편 · 필터 해제
AurigaNet: A Real-Time Multi-Task Network for Enhanced Urban Driving Perception
Self-driving cars hold significant potential to reduce traffic accidents, alleviate congestion, and enhance urban mobility. However, developing reliable AI systems for autonomous vehicles remains a substantial challenge.…
Computational EfficiencyInstance SegmentationMulti-Task LearningAutonomous VehiclesRxnNano:Training Compact LLMs for Chemical Reaction and Retrosynthesis Prediction via Hierarchical Curriculum Learning
Chemical reaction prediction is pivotal for accelerating drug discovery and synthesis planning. Despite advances in data-driven models, current approaches are hindered by an overemphasis on parameter and dataset scaling.…
Chemical Reaction PredictionMulti-Task LearningDrug DiscoveryGeoFormer: A Lightweight Swin Transformer for Joint Building Height and Footprint Estimation from Sentinel Imagery
Building height (BH) and footprint (BF) are fundamental urban morphological parameters required by climate modelling, disaster-risk assessment, and population mapping, yet globally consistent data remain scarce. In this …
Computational EfficiencyMulti-Task LearningA Generative Model for Joint Multiple Intent Detection and Slot Filling
In task-oriented dialogue systems, spoken language understanding (SLU) is a critical component, which consists of two sub-tasks, intent detection and slot filling. Most existing methods focus on the single-intent SLU, wh…
Task-Oriented Dialogue SystemsSpoken Language UnderstandingMulti-Task LearningIntent DetectionEquitable Multi-Task Learning for AI-RANs
AI-enabled Radio Access Networks (AI-RANs) are expected to serve heterogeneous users with time-varying learning tasks over shared edge resources. Ensuring equitable inference performance across these users requires adapt…
Multi-Task LearningOmniReview: A Large-scale Benchmark and LLM-enhanced Framework for Realistic Reviewer Recommendation
Academic peer review remains the cornerstone of scholarly validation, yet the field faces some challenges in data and methods. From the data perspective, existing research is hindered by the scarcity of large-scale, veri…
Multi-Task LearningFine-Tuning a Large Vision-Language Model for Artwork's Scoring and Critique
Assessing artistic creativity is foundational to creativity research and arts education, yet manual scoring (e.g., Torrance Tests of Creative Thinking) is labor-intensive at scale. Prior machine-learning approaches show …
Multi-Task LearningAn Explainable Multi-Task Similarity Measure: Integrating Accumulated Local Effects and Weighted Fréchet Distance
In many machine learning contexts, tasks are often treated as interconnected components with the goal of leveraging knowledge transfer between them, which is the central aim of Multi-Task Learning (MTL). Consequently, th…
Multi-Task LearningMDL: A Unified Multi-Distribution Learner in Large-scale Industrial Recommendation through Tokenization
Industrial recommender systems increasingly adopt multi-scenario learning (MSL) and multi-task learning (MTL) to handle diverse user interactions and contexts, but existing approaches suffer from two critical drawbacks: …
Multi-Task LearningCross-talk based multi-task learning for fault classification of machine system influenced by multiple variables
Machine systems inherently generate signals in which fault conditions and various variables influence signals measured from machine system. Although many existing fault classification studies rely solely on direct fault …
Multi-Task LearningFrom Images to Decisions: Assistive Computer Vision for Non-Metallic Content Estimation in Scrap Metal
Scrap quality directly affects energy use, emissions, and safety in steelmaking. Today, the share of non-metallic inclusions (contamination) is judged visually by inspectors - an approach that is subjective and hazardous…
Multi-Task LearningExploring the Temporal Consistency for Point-Level Weakly-Supervised Temporal Action Localization
Point-supervised Temporal Action Localization (PTAL) adopts a lightly frame-annotated paradigm (\textit{i.e.}, labeling only a single frame per action instance) to train a model to effectively locate action instances wit…
Weakly-supervised Temporal Action LocalizationMulti-Task LearningSTProtein: predicting spatial protein expression from multi-omics data
The integration of spatial multi-omics data from single tissues is crucial for advancing biological research. However, a significant data imbalance impedes progress: while spatial transcriptomics data is relatively abund…
Multi-Task LearningOne Model, Many Skills: Parameter-Efficient Fine-Tuning for Multitask Code Analysis
Large language models have recently surpassed specialized systems on code generation, yet their effectiveness on other code-analysis tasks remains less clear. At the same time, multi-task learning offers a way to unify d…
parameter-efficient fine-tuningMulti-Task LearningCode GenerationSEIS: Subspace-based Equivariance and Invariance Scores for Neural Representations
Understanding how neural representations respond to geometric transformations is essential for evaluating whether learned features preserve meaningful spatial structure. Existing approaches primarily assess robustness pr…
Multi-Task LearningData AugmentationiSight: Towards expert-AI co-assessment for improved immunohistochemistry staining interpretation
Immunohistochemistry (IHC) provides information on protein expression in tissue sections and is commonly used to support pathology diagnosis and disease triage. While AI models for H\&E-stained slides show promise, their…
Multi-Task LearningUV-M3TL: A Unified and Versatile Multimodal Multi-Task Learning Framework for Assistive Driving Perception
Advanced Driver Assistance Systems (ADAS) need to understand human driver behavior while perceiving their navigation context, but jointly learning these heterogeneous tasks would cause inter-task negative transfer and im…
Multi-Task LearningToward Enhancing Representation Learning in Federated Multi-Task Settings
Federated multi-task learning (FMTL) seeks to collaboratively train customized models for users with different tasks while preserving data privacy. Most existing approaches assume model congruity (i.e., the use of fully …
Representation LearningContrastive LearningMulti-Task LearningMulti-Task Learning for Robot Perception with Imbalanced Data
Multi-task problem solving has been shown to improve the accuracy of the individual tasks, which is an important feature for robots, as they have a limited resource. However, when the number of labels for each task is no…
Semantic SegmentationMulti-Task LearningDepth EstimationLiME: Lightweight Mixture of Experts for Efficient Multimodal Multi-task Learning
MoE-PEFT methods combine Mixture of Experts with parameter-efficient fine-tuning for multi-task adaptation, but require separate adapters per expert causing trainable parameters to scale linearly with expert count and li…
parameter-efficient fine-tuningMulti-Task Learning