mlsanityData to 7 Oct 2026

Trace Norm Regularised Deep Multi-Task Learning

arXiv 1606.04038 ↗First posted 13 Jun 2016cs.LGcited by 50

Not on the map. Why?

Abstract (arXiv)

We propose a framework for training multiple neural networks simultaneously. The parameters from all models are regularised by the tensor trace norm, so that each neural network is encouraged to reuse others' parameters if possible -- this is the main motivation behind multi-task learning. In contrast to many deep multi-task learning models, we do not predefine a parameter sharing strategy by specifying which layers have tied parameters. Instead, our framework considers sharing for all shareable layers, and the sharing strategy is learned in a data-driven way.

Key facts. Cited by 50 papers in the map (data through 7 Oct 2026). First posted on arXiv 13 Jun 2016.

Builds on

No reference list yet.

Built on by

Cited by year

Citing papers per year: 2021: 11; 2022: 11; 2023: 12; 2024: 8; 2025: 8.

50 papers.

Sort:

By area:

  1. 2021Efficiently Identifying Task Groupings for Multi-Task Learningcited by 218
  2. 2021Invariant Information Bottleneck for Domain Generalizationcited by 86
  3. 2024Multi-Task Dense Prediction via Mixture of Low-Rank Expertscited by 58
  4. 2023Open-World Multi-Task Control Through Goal-Aware Representation Learning and Adaptive Horizon Predictioncited by 39
  5. 2022One Model is All You Need: Multi-Task Learning Enables Simultaneous Histology Image Segmentation and Classificationcited by 38
  6. 2021Annotating Columns with Pre-trained Language Modelscited by 37
  7. 2021Deep Multi-Task Learning for Joint Localization, Perception, and Predictioncited by 22
  8. 2021Revisit Multimodal Meta-Learning through the Lens of Multi-Task Learningcited by 22
  9. 2022Egocentric Video Task Translationcited by 19
  10. 2024Emotion and Intent Joint Understanding in Multimodal Conversation: A Benchmarking Datasetcited by 15
  11. 2025Uni-Retrieval: A Multi-Style Retrieval Framework for STEM's Educationcited by 13
  12. 2025Skill Expansion and Composition in Parameter Spacecited by 13
38 more
  1. 2022Losses over Labels: Weakly Supervised Learning via Direct Loss Constructioncited by 11
  2. 2022Beyond RGB: Scene-Property Synthesis with Neural Radiance Fieldscited by 10
  3. 2022Reinforcement Guided Multi-Task Learning Framework for Low-Resource Stereotype Detectioncited by 7
  4. 2023Efficient Controllable Multi-Task Architecturescited by 7
  5. 2023Gradient Shaping for Multi-Constraint Safe Reinforcement Learningcited by 7
  6. 2024Unsupervised Modality-Transferable Video Highlight Detection with Representation Activation Sequence Learningcited by 7
  7. 2023When Multi-Task Learning Meets Partial Supervision: A Computer Vision Reviewcited by 6
  8. 2024Analytical Uncertainty-Based Loss Weighting in Multi-Task Learningcited by 6
  9. 2022Saliency-Regularized Deep Multi-Task Learningcited by 5
  10. 2023YOLOR-Based Multi-Task Learningcited by 5
  11. 2024Multi-task Heterogeneous Graph Learning on Electronic Health Recordscited by 5
  12. 2023A Comprehensive Review and a Taxonomy of Edge Machine Learning: Requirements, Paradigms, and Techniquescited by 4
  13. 2023A Novel Computational and Modeling Foundation for Automatic Coherence Assessmentcited by 4
  14. 2021Tasks Structure Regularization in Multi-Task Learning for Improving Facial Attribute Predictioncited by 3
  15. 2022PartAL: Efficient Partial Active Learning in Multi-Task Visual Settingscited by 3
  16. 2025ThanoRA: Task Heterogeneity-Aware Multi-Task Low-Rank Adaptationcited by 3
  17. 2021Learning Twofold Heterogeneous Multi-Task by Sharing Similar Convolution Kernel Pairscited by 2
  18. 2022Towards a Better Microcredit Decisioncited by 2
  19. 2023Hybrid Dual Mean-Teacher Network With Double-Uncertainty Guidance for Semi-Supervised Segmentation of MRI Scanscited by 2
  20. 2023Auxiliary-Tasks Learning for Physics-Informed Neural Network-Based Partial Differential Equations Solvingcited by 2
  21. 2023Visual Attention Prompted Prediction and Learningcited by 2
  22. 2024CerberusDet: Unified Multi-Dataset Object Detectioncited by 2
  23. 2025DeepChest: Dynamic Gradient-Free Task Weighting for Effective Multi-Task Learning in Chest X-ray Classificationcited by 2
  24. 2025From Query to Explanation: Uni-RAG for Multi-Modal Retrieval-Augmented Learning in STEMcited by 2
  25. 2021Multi-Task Learning based Convolutional Models with Curriculum Learning for the Anisotropic Reynolds Stress Tensor in Turbulent Duct Flowcited by 1
  26. 2023Challenges and Opportunities of Using Transformer-Based Multi-Task Learning in NLP Through ML Lifecycle: A Surveycited by 1
  27. 2024Residual Multi-Task Learner for Applied Rankingcited by 1
  28. 2021Unsupervised Deep Learning by Injecting Low-Rank and Sparse Priorscited by 0
  29. 2021Exploring Data Aggregation and Transformations to Generalize across Visual Domainscited by 0
  30. 2021Boosting Supervised Learning Performance with Co-trainingcited by 0
  31. 2022High Efficiency Pedestrian Crossing Predictioncited by 0
  32. 2022Joint Learning-based Causal Relation Extraction from Biomedical Literaturecited by 0
  33. 2022Design Perspectives of Multitask Deep Learning Models and Applicationscited by 0
  34. 2023Parameterized Neural Networks for Financecited by 0
  35. 2024CrowdTransfer: Enabling Crowd Knowledge Transfer in AIoT Communitycited by 0
  36. 2025Transforming Vision Transformer: Towards Efficient Multi-Task Asynchronous Learningcited by 0
  37. 2025Multi-task Learning For Joint Action and Gesture Recognitioncited by 0
  38. 2025Vision Generalist Model: A Surveycited by 0