Papers Multi-Task Learning
“Multi-Task Learning” 태그가 달린 논문 4,109편 · 필터 해제
DualOptim+: Bridging Shared and Decoupled Optimizer States for Better Machine Unlearning in Large Language Models
We propose DualOptim+, a novel optimization framework for improving machine unlearning in large language models. It introduces a base state to capture common representations shared by forgetting and retaining objectives …
Multi-Task LearningTask-Routed Mixture-of-Experts with Cognitive Appraisal for Implicit Sentiment Analysis
Implicit sentiment analysis is challenging because sentiment toward an aspect is often inferred from events rather than expressed through explicit opinion words. Existing models typically learn from the final polarity la…
Multi-Task LearningSentiment AnalysisA Two-Phase Adaptive Balanced Penalty Method for Controllable Pareto Front Learning under Split Feasibility Conditions
We address the open problem of training hypernetworks for Controllable Pareto Front Learning (CPFL) under split feasibility conditions with rigorous theoretical guarantees. We reformulate the constrained Pareto problem a…
Multi-Task LearningNetwork Knowledge Prior Guided Learning for Data-Efficient Surface Defect Detection
Deep learning-based methods have become the de facto standard for industrial defect detection. However, their data-hungry nature and inherent "black-box" characteristics often lead to performance bottlenecks and limited …
Multi-Task LearningPMF-CL: Pareto-Minimal-Forgetting Continual Learner for Conflicting Tasks
In the literature, many continual learning (CL) algorithms have been proposed to address the issue of catastrophic forgetting in ML models (i.e., learning new tasks leads to the loss of performance on previously learned …
Multi-Task LearningContinual LearningMulti-task learning on partially labeled datasets via invariant/equivariant semi-supervised learning
We investigate the potential of invariant and equivariant semi-supervised learning for addressing the challenges of training multi-task models on partially labeled datasets with differently structured output tasks. Speci…
Semantic SegmentationMulti-Task LearningObject DetectionMono-Hydra++: Real-Time Monocular Scene Graph Construction with Multi-Task Learning for 3D Indoor Mapping
Autonomous agile robots need more than metric geometry: they must understand objects, rooms, places, and spatial relations for search, inspection, exploration, and human robot interaction. Conventional metric maps suppor…
Multi-Task LearningCollision AvoidanceResponse-free item difficulty modelling for multiple-choice items with fine-tuned transformers: Component-wise representation and multi-task learning
Response-free item difficulty modelling promises to reduce reliance on response-based calibration but is intrinsically difficult on reading-comprehension multiple-choice items, where difficulty depends on inferential dem…
Feature EngineeringMulti-Task LearningQuestion AnsweringSyntax Without Semantics: Teaching Large Language Models to Code in an Unseen Language
Large language models (LLMs) achieve high pass rates on code generation benchmarks, yet whether they can transfer this ability to languages absent from pretraining remains poorly understood. We introduce PyLang, a minima…
Multi-Task LearningCode GenerationDepthPolyp: Pseudo-Depth Guided Lightweight Segmentation for Real-Time Colonoscopy
Accurate polyp segmentation in colonoscopy is essential for early colorectal cancer detection, yet real-world clinical environments pose persistent challenges such as motion blur, specular reflections, and illumination i…
Multi-Task LearningPolyp SegmentationTowards Label-Free Single-Cell Phenotyping Using Multi-Task Learning
Label-free single-cell imaging offers a scalable, non-invasive alternative to fluorescence-based cytometry, yet inferring molecular phenotypes directly from bright-field morphology remains challenging. We present a unifi…
Multi-Task LearningReForge: Refining Merged Models with Anchor-Regularized Regression
Model merging aims to combine multiple task-specific expert models into a single model without joint retraining, offering a practical alternative to multi-task learning when data access or computational budget is limited…
Multi-Task LearningUncertainty-Driven Anomaly Detection for Psychotic Relapse Using Smartwatches: Forecasting and Multi-Task Learning Fusion
Digital phenotyping enables continuous passive monitoring of behavior and physiology, offering a promising paradigm for early detection of psychotic relapse. In this work, we develop and systematically study two smartwat…
Multi-Task LearningAnomaly DetectionPEML: Parameter-efficient Multi-Task Learning with Optimized Continuous Prompts
Parameter-Efficient Fine-Tuning (PEFT) is widely used for adapting Large Language Models (LLMs) for various tasks. Recently, there has been an increasing demand for fine-tuning a single LLM for multiple tasks because it …
parameter-efficient fine-tuningMulti-Task LearningLearning Feature Encoder with Synthetic Anomalies for Weakly Supervised Graph Anomaly Detection
Weakly supervised graph anomaly detection aims to unveil unusual graph instances, e.g., nodes, whose behaviors significantly differ from normal ones, given only a limited number of annotated anomalies and abundant unlabe…
Graph Anomaly DetectionMulti-Task LearningM2A: Synergizing Mathematical and Agentic Reasoning in Large Language Models
While reasoning has become a central capability of large language models (LLMs), the reasoning patterns required for different scenarios are often misaligned. Mathematical reasoning typically relies on intrinsic logic to…
Mathematical ReasoningMulti-Task LearningFLAME: Adaptive Mixture-of-Experts for Continual Multimodal Multi-Task Learning
Real-world model deployment across multiple domains requires multimodal models to operate under two complementary regimes: (1) multi-task pretraining, tasks are co-available at design time where related tasks could borro…
Multi-Task LearningContinual LearningTracking Large-scale Shared Bikes with Inertial Motion Learning in GNSS Blocked Environments
Although Global Navigation Satellite Systems (GNSS) provide a general solution for bike tracking outdoors, there still exist complex riding environments where only inertial navigation systems work, such as urban canyons.…
Multi-Task LearningNon-intrusive Body Composition Assessment from Full-body mmWave Scans
Body composition assessment (BCA) provides detailed information about the distribution of different tissue types in the body, enabling more precise characterization of individuals than BMI or weight alone. Consistent and…
Multi-Task LearningPoint CloudsFlatness and Gradient Alignment Are Both Necessary: Spectral-Aware Gradient-Aligned Exploration for Multi-Distribution Learning
Sharpness-aware and gradient-alignment methods have been shown to improve generalization, however each family of methods targets a single geometric property of the loss landscape, while ignoring the other. In this paper,…
Multi-Task Learning