Papers Multi-Task Learning
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YEZE at SemEval-2026 Task 9: Detecting Multilingual, Multicultural and Multievent Online Polarization via Heterogeneous Ensembling
This paper presents our system for SemEval-2026 Task 9: Detecting Multilingual, Multicultural and Multievent Online Polarization, which identifies polarized social media content in 22 languages through three subtasks: bi…
Multi-Task LearningData AugmentationCausal-Aware Foundation-Model for Bilevel Optimization in Discrete Choice Settings
We introduce a causal aware foundation-model framework for real time optimal decision making in discrete choice environments. We propose a constrained triple-head price optimization (C3PO) network to solve a bilevel deci…
Bilevel OptimizationMulti-Task LearningDecision MakingAPEX: Large-scale Multi-task Aesthetic-Informed Popularity Prediction for AI-Generated Music
Music popularity prediction has attracted growing research interest, with relevance to artists, platforms, and recommendation systems. However, the explosive rise of AI-generated music platforms has created an entirely n…
Recommendation SystemsMulti-Task LearningDisentangling Shared and Task-Specific Representations from Multi-Modal Clinical Data
Real-world clinical data is inherently multimodal, providing complementary evidence that mirrors the practical necessity of jointly assessing multiple related outcomes. Although multi-task learning can improve efficiency…
Representation LearningMulti-Task LearningFLoRA: Fusion-Latent for Optical Reconstruction and Flood Area Segmentation via Cross-Modal Multi-Task Distillation Network
Accurate flood water mapping is critical for disaster management, yet current methods struggle to fully exploit the potential of spaceborne imagery. Optical data offers high interpretability but is limited by environment…
Multi-Task LearningNear-optimal and Efficient First-Order Algorithm for Multi-Task Learning with Shared Linear Representation
Multi-task learning (MTL) has emerged as a pivotal paradigm in machine learning by leveraging shared structures across multiple related tasks. Despite its empirical success, the development of likelihood-based efficientl…
Multi-Task LearningUni-HOI:A Unified framework for Learning the Joint distribution of Text and Human-Object Interaction
Modeling 4D human-object interaction (HOI) is a compelling challenge in computer vision and an essential technology powering virtual and mixed-reality applications. While existing works have achieved promising results on…
Multi-Task LearningFUN: A Focal U-Net Combining Reconstruction and Object Detection for Snapshot Spectral Imaging
Conventional push-broom hyperspectral imaging suffers from slow acquisition speeds, precluding real-time object detection; in contrast, snapshot spectral imaging enables instantaneous hyperspectral images (HSIs) capture,…
Real-Time Object DetectionMulti-Task LearningNoise2Map: End-to-End Diffusion Model for Semantic Segmentation and Change Detection
Semantic segmentation and change detection are two fundamental challenges in remote sensing, requiring models to capture either spatial semantics or temporal differences from satellite imagery. Existing deep learning mod…
Semantic SegmentationMulti-Task LearningChange DetectionFiLMMeD: Feature-wise Linear Modulation for Cross-Problem Multi-Depot Vehicle Routing
Solving practical multi-depot vehicle routing problems (MDVRP) is a challenging optimization task central to modern logistics, increasingly driven by e-commerce. To address the MDVRP's computational complexity, neural-ba…
Reinforcement LearningMulti-Task LearningConflict-Aware Harmonized Rotational Gradient for Multiscale Kinetic Regimes
In this paper, we propose a harmonized rotational gradient method, termed HRGrad, for simultaneously tackling multiscale time-dependent kinetic problems with varying small parameters. These parameters exhibit asymptotic …
Multi-Task LearningWISE-FM:Operation-Aware, Engineering-Informed Foundation Model for Multi-Task Well Design
Deploying machine learning models across diverse well portfolios requires generalisation to wells with design parameters outside the training distribution. Current data-driven approaches to virtual flow metering (VFM) an…
Multi-Task LearningA Benchmark Suite of Reddit-Derived Datasets for Mental Health Detection
The growing availability of online support groups has opened up new windows to study mental health through natural language processing (NLP). However, it is hindered by a lack of high-quality, well-validated datasets. Ex…
Multi-Task LearningTransferable Physics-Informed Representations via Closed-Form Head Adaptation
Physics-informed neural networks (PINNs) have garnered significant interest for their potential in solving partial differential equations (PDEs) that govern a wide range of physical phenomena. By incorporating physical l…
Multi-Task LearningSMART: A Spectral Transfer Approach to Multi-Task Learning
Multi-task learning is effective for related applications, but its performance can deteriorate when the target sample size is small. Transfer learning can borrow strength from related studies; yet, many existing methods …
Multi-Task LearningTransfer LearningEnhancing Science Classroom Discourse Analysis through Joint Multi-Task Learning for Reasoning-Component Classification
Analyzing the reasoning patterns of students in science classrooms is critical for understanding knowledge construction mechanism and improving instructional practice to maximize cognitive engagement, yet manual coding o…
Component ClassificationMulti-Task LearningData AugmentationSAMoRA: Semantic-Aware Mixture of LoRA Experts for Task-Adaptive Learning
The combination of Mixture-of-Experts (MoE) and Low-Rank Adaptation (LoRA) has shown significant potential for enhancing the multi-task learning capabilities of Large Language Models. However, existing methods face two p…
parameter-efficient fine-tuningMulti-Task LearningOneDrive: Unified Multi-Paradigm Driving with Vision-Language-Action Models
Vision-Language Models(VLMs) excel at autoregressive text generation, yet end-to-end autonomous driving requires multi-task learning with structured outputs and heterogeneous decoding behaviors, such as autoregressive la…
Multi-Task LearningTrajectory PlanningAutonomous DrivingObject DetectionUGD: An Unsupervised Geometric Distance for Evaluating Real-world Noisy Point Cloud Denoising
Point cloud denoising is a fundamental and crucial challenge in real-world point cloud applications. Existing quantitative evaluation metrics for point cloud denoising methods are implemented in a supervised manner, whic…
Multi-Task LearningPoint CloudsFrom Boundaries to Semantics: Prompt-Guided Multi-Task Learning for Petrographic Thin-section Segmentation
Grain-edge segmentation (GES) and lithology semantic segmentation (LSS) are two pivotal tasks for quantifying rock fabric and composition. However, these two tasks are often treated separately, and the segmentation quali…
Semantic SegmentationMulti-Task Learning