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Tag

#multi-task-learning

4 articles tagged #multi-task-learning

arxivJul 23bullish

SUM: Unified Geometric Surgery on Spatio-Temporal Adaptation Vectors for Federated Class Incremental Learning

arXiv:2607.19384v1 Announce Type: new Abstract: Real-world intelligent systems often require both distributed collaboration across data-isolated clients and continual adaptation to evolving tasks. This setting naturally gives rise to Federated Class Incremental Learning (FCIL), which combines Federa

#federated-learning#continual-learning#multi-task-learningRead on arxiv →
arxivJul 1

JL1-CC&QA: Extending the JL1-CD Benchmark with Change Captioning and Question Answering

arXiv:2606.31745v1 Announce Type: cross Abstract: Remote sensing change detection (CD) traditionally focuses on pixel-level binary segmentation, which identifies where changes occur but neither what nor why. To bridge this semantic gap, we introduce JL1-CC&QA, a multi-task benchmark that extends the

LA1 model#remote-sensing#change-detection#multi-task-learningRead on arxiv →
arxivJun 26bullish

RecallRisk-BERT: A Multi-Task Framework for Post-Report Medical Device Recall Triage

arXiv:2606.27174v1 Announce Type: new Abstract: Medical device recalls are a critical regulatory mechanism for protecting patient safety. The growing volume of FDA recall records presents challenges in post-report recall triage, severity assessment, and root-cause interpretation. Existing studies mo

REMILI3 models#medical-device#recall-prediction#multi-task-learningRead on arxiv →
arxivJun 24bullish

PACT: Preserving Anchored Cores in Task-vectors for Model Merging

arXiv:2606.18627v3 Announce Type: replace Abstract: Model merging has emerged as a training-free alternative to multi-task learning, aiming to combine multiple task-specific fine-tuned models into a single multi-task model. Most existing model merging approaches follow the Task Arithmetic paradigm,

#model-merging#multi-task-learning#pre-trained-modelsRead on arxiv →
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