Papers Multi-Task Learning
“Multi-Task Learning” 태그가 달린 논문 4,109편 · 필터 해제
Flexible Kernels for Protein Property Prediction
Despite its importance to applications in protein design, predicting protein properties like binding affinity and thermostability from sparse experimental data remains a significant challenge. Accordingly, we introduce a…
Multi-Task LearningGaussian ProcessesProtein DesignData Synthesis and Parameter-Efficient Fine-Tuning for Low-Resource NMT: A Case Study on Q'eqchi' Mayan
Neural machine translation for digitally low-resource Indigenous languages is often hindered by extreme data scarcity, prompting reliance on extractive web-scraping. To ensure data sovereignty, this study introduces a da…
parameter-efficient fine-tuningMachine TranslationMulti-Task LearningEGTR-Review: Efficient Evidence-Grounded Scientific Peer Review Generation via Multi-Agent Teacher Distillation
Scientific peer review generation has attracted increasing attention for reducing reviewing burdens and providing timely feedback. However, existing Large Language Model (LLM)-based methods often produce generic comments…
Multi-Task LearningMulti-task Learning is Not Enough: Representational Entanglement in Dual-output Second Language Speech Recognition
Second-language (L2) speech recognition often requires transcriptions of pronunciations and intended meanings. Multi-task learning (MTL) is a natural approach because it assumes that shared representations benefit both o…
Multi-Task LearningSpeech RecognitionMind the Gap: Bridging Behavioral Silos with LLMs in Multi-Vertical Recommendations
In multi-vertical e-commerce platforms like DoorDash, relatively newer product verticals such as grocery and retail present a significant opportunity for personalization innovation. A key challenge lies in solving the "c…
Multi-Task LearningGeneralizable Multi-Task Learning for Wireless Networks Using Prompt Decision Transformers
Future wireless networks demand rapid adaptation to highly heterogeneous environments and dynamic task configurations, necessitating a shift from conventional rule-based and optimization-driven radio resource management …
Reinforcement LearningMulti-Task LearningFood-R1: A Unified Multi-Task Food Vision-Language Model with Reinforcement Learning
Recent studies have explored Vision-Language Models (VLMs) for food analysis. However, most existing methods rely primarily on supervised fine-tuning (SFT), which often limits reasoning and generalization capabilities. M…
Reinforcement LearningMulti-Task LearningUniCAD: A Unified Benchmark and Universal Model for Multi-Modal Multi-Task CAD
Computer-Aided Design (CAD) underpins modern engineering and manufacturing by enabling the creation of precise, editable 3D models. However, CAD research typically studies tasks in isolation, and multi-modal, multi-task …
Multi-Task LearningCAD ReconstructionQuestion AnsweringPoint CloudsTowards Compact Autonomous Driving Perception with Balanced Learning and Multi-sensor Fusion
We present a novel compact deep multi-task learning model to handle various autonomous driving perception tasks in one forward pass. The model performs multiple views of semantic segmentation, depth estimation, light det…
Semantic SegmentationMulti-Task LearningAutonomous DrivingDepth EstimationAQIFormer: A Transformer-Based Multi-View Architecture for Cross-City Air Quality Classification
Air pollution represents one of the most critical environmental and public health challenges globally, with traditional sensor-based monitoring systems facing significant scalability and economic constraints. Image-based…
Multi-Task LearningMAdam: Metric-Aware Multi-Objective Adam
Multi-objective optimization (MOO) underlies many machine learning problems, yet MOO solvers across the loss-balancing, gradient-balancing, and Pareto-based families almost universally hand their reconciled directions to…
Multi-Task LearningCORE-MTL: Rethinking Gradient Balancing via Causal Orthogonal Representations
Multi-task learning (MTL) aims to construct a joint model for multiple tasks by sharing a common representation across domains. To achieve this goal, existing optimization-centric methods either balance task gradients or…
Multi-Task LearningParameter-Efficient Subspace Decoupling ViT for Mitigating Multi-Task Negative Transfer in Histological Scoring
Histological scoring is essential for diagnosing Non-Alcoholic Fatty Liver Disease (NAFLD), yet its automation remains challenging due to the high annotation cost and negative transfer among the strongly correlated NAFLD…
Multi-Task LearningEigeNet: Geometry-Informed Multi-Modal Learning for Few-shot Novel View RIR Prediction
Predicting spatially varying Room Impulse Response (RIR) from sparse observations is a critical but highly challenging inverse problem for immersive spatial audio rendering. In this work, we present EIGENET, a geometry-i…
Multi-Task LearningWhen Gradients Collide: Failure Modes of Multi-Objective Prompt Optimization for LLM Judges
Customizing an LLM judge to a specific problem or domain often involves optimizing its prompt across multiple evaluation criteria simultaneously. Textual gradient methods automate this for a single judge criterion, howev…
Multi-Task LearningExplainable Multi-Task Retinal Imaging Reveals Microvascular Signals for Systemic Risk Stratification in Type 2 Diabetes: A Pilot Study
Retinal imaging provides a non-invasive window into systemic microvascular health and has emerged as a potential biomarker for systemic diseases. However, whether retinal features encode biologically meaningful systemic …
Multi-Task LearningWinner-Take-All bottlenecks enforce disentangled symbolic representations in multi-task learning
Winner-take-all (WTA) networks constitute a central circuit motif in cortical networks of the brain. In addition, WTA-like activations are abundant in modern deep learning models in the form of the softmax activation for…
Multi-Task LearningBenchmarking Machine Learning Architectures for Antimicrobial Stewardship in Pediatric ICUs
Antimicrobial stewardship (AMS) is critical in pediatric intensive care units (PICUs), where diagnostic uncertainty often drives broad-spectrum antibiotic use, increasing antimicrobial resistance and potential long-term …
Multi-Task LearningThe Double Dilemma in Multi-Task Radiology Report Generation: A Gradient Dynamics Analysis and Solution
While multi-task learning based automatic radiology report generation (RRG) is widely adopted to ensure clinical consistency, most focus on architectural designs yet remain limited to coarse linear scalarization strategi…
Multi-Task LearningMultiple Neural Operators Achieve Near-Optimal Rates for Multi-Task Learning
We study the approximation and statistical complexity of learning collections of operators in a shared multi-task setting, with a focus on the Multiple Neural Operators (MNO) architecture. For broad classes of Lipschitz …
Multi-Task Learning