mlsanityData to 7 Oct 2026

Deep Multi-task Representation Learning: A Tensor Factorisation Approach

arXiv 1605.06391 ↗First posted 20 May 2016cs.LGcited by 69

Not on the map. Why?

Abstract (arXiv)

Most contemporary multi-task learning methods assume linear models. This setting is considered shallow in the era of deep learning. In this paper, we present a new deep multi-task representation learning framework that learns cross-task sharing structure at every layer in a deep network. Our approach is based on generalising the matrix factorisation techniques explicitly or implicitly used by many conventional MTL algorithms to tensor factorisation, to realise automatic learning of end-to-end knowledge sharing in deep networks. This is in contrast to existing deep learning approaches that need a user-defined multi-task sharing strategy. Our approach applies to both homogeneous and heterogeneous MTL. Experiments demonstrate the efficacy of our deep multi-task representation learning in terms of both higher accuracy and fewer design choices.

Key facts. Cited by 69 papers in the map (data through 7 Oct 2026). First posted on arXiv 20 May 2016.

Builds on

No reference list yet.

Built on by

Cited by year (2026 to 7 Oct)

Citing papers per year: 2021: 18; 2022: 8; 2023: 25; 2024: 8; 2025: 8; 2026: 2.

69 papers.

Sort:

By area:

  1. 2021Domain Generalization: A Surveycited by 760
  2. 2023Modular Deep Learningcited by 82
  3. 2021Tensor Methods in Computer Vision and Deep Learningcited by 52
  4. 2021Modeling the Sequential Dependence among Audience Multi-step Conversions with Multi-task Learning in Targeted Display Advertisingcited by 39
  5. 2023Quality-Agnostic Deepfake Detection with Intra-model Collaborative Learningcited by 32
  6. 2023Tensor Networks Meet Neural Networks: A Survey and Future Perspectivescited by 29
  7. 2024Multilinear Mixture of Experts: Scalable Expert Specialization through Factorizationcited by 28
  8. 2023Multi-Task Recommendations with Reinforcement Learningcited by 26
  9. 2021Revisit Multimodal Meta-Learning through the Lens of Multi-Task Learningcited by 22
  10. 2022Cross-Task Knowledge Distillation in Multi-Task Recommendationcited by 22
  11. 2021MEDIC: A Multi-Task Learning Dataset for Disaster Image Classificationcited by 20
  12. 2023Identification of Negative Transfers in Multitask Learning Using Surrogate Modelscited by 20
38 more
  1. 2025Pareto Set Learning for Multi-Objective Reinforcement Learningcited by 18
  2. 2024Predicting Configuration Performance in Multiple Environments with Sequential Meta-learningcited by 17
  3. 2023Boosting Multitask Learning on Graphs through Higher-Order Task Affinitiescited by 15
  4. 2023Exploring Distributional Shifts in Large Language Models for Code Analysiscited by 14
  5. 2023Generalist: Decoupling Natural and Robust Generalizationcited by 14
  6. 2021Meta-Learning for Effective Multi-task and Multilingual Modellingcited by 13
  7. 2023Prompt Guided Transformer for Multi-Task Dense Predictioncited by 13
  8. 2023Same Same, But Different: Conditional Multi-Task Learning for Demographic-Specific Toxicity Detectioncited by 10
  9. 2023Tensor Decomposition Based Attention Module for Spiking Neural Networkscited by 8
  10. 2024Deep Configuration Performance Learning: A Systematic Survey and Taxonomycited by 8
  11. 2022SHELS: Exclusive Feature Sets for Novelty Detection and Continual Learning Without Class Boundariescited by 7
  12. 2023Efficient Controllable Multi-Task Architecturescited by 7
  13. 2022Multi-Task Learning for Visual Scene Understandingcited by 6
  14. 2023When Multi-Task Learning Meets Partial Supervision: A Computer Vision Reviewcited by 6
  15. 2024Swiss Army Knife: Synergizing Biases in Knowledge from Vision Foundation Models for Multi-Task Learningcited by 6
  16. 2022Domain Generalisation for Object Detection under Covariate and Concept Shiftcited by 5
  17. 2023VVS: Video-to-Video Retrieval with Irrelevant Frame Suppressioncited by 5
  18. 2021Marginalizable Density Modelscited by 4
  19. 2021Provable Adaptation across Multiway Domains via Representation Learningcited by 3
  20. 2021Tasks Structure Regularization in Multi-Task Learning for Improving Facial Attribute Predictioncited by 3
  21. 2021Residual Tensor Train: A Quantum-inspired Approach for Learning Multiple Multilinear Correlationscited by 3
  22. 2021Multi-Task Classification of Sewer Pipe Defects and Properties using a Cross-Task Graph Neural Network Decodercited by 3
  23. 2023Learning Multi-Pattern Normalities in the Frequency Domain for Efficient Time Series Anomaly Detectioncited by 3
  24. 2025Catheter Detection and Segmentation in X-ray Images via Multi-task Learningcited by 3
  25. 2021Multitask Learning for Emotion and Personality Detectioncited by 2
  26. 2021Tensor-Train Networks for Learning Predictive Modeling of Multidimensional Datacited by 2
  27. 2021Compositional Models: Multi-Task Learning and Knowledge Transfer with Modular Networkscited by 2
  28. 2021HierMUD: Hierarchical Multi-task Unsupervised Domain Adaptation between Bridges for Drive-by Damage Diagnosiscited by 2
  29. 2022Compressed Hierarchical Representations for Multi-Task Learning and Task Clusteringcited by 2
  30. 2022Spatial-temporal traffic modeling with a fusion graph reconstructed by tensor decompositioncited by 2
  31. 2023Multi-dimensional domain generalization with low-rank structurescited by 2
  32. 2025Injecting Imbalance Sensitivity for Multi-Task Learningcited by 2
  33. 2025MetaLoRA: Tensor-Enhanced Adaptive Low-Rank Fine-tuningcited by 2
  34. 2022Multi-task Bias-Variance Trade-off Through Functional Constraintscited by 1
  35. 2023Tensorized LSSVMs for Multitask Regressioncited by 1
  36. 2023Low-Rank Multitask Learning based on Tensorized SVMs and LSSVMscited by 1
  37. 2023Ammonia-Net: A Multi-task Joint Learning Model for Multi-class Segmentation and Classification in Tooth-marked Tongue Diagnosiscited by 1
  38. 2023Parameter-efficient Multi-Task and Multi-Domain Learning using Factorized Tensor Networkscited by 1

50 most cited of 69 All 69 (JSON)