mlsanityData to 7 Oct 2026

AdaNet: Adaptive Structural Learning of Artificial Neural Networks

arXiv 1607.01097 ↗First posted 5 Jul 2016cs.LGcited by 54

Not on the map. Why?

Abstract (arXiv)

We present new algorithms for adaptively learning artificial neural networks. Our algorithms (AdaNet) adaptively learn both the structure of the network and its weights. They are based on a solid theoretical analysis, including data-dependent generalization guarantees that we prove and discuss in detail. We report the results of large-scale experiments with one of our algorithms on several binary classification tasks extracted from the CIFAR-10 dataset. The results demonstrate that our algorithm can automatically learn network structures with very competitive performance accuracies when compared with those achieved for neural networks found by standard approaches.

Key facts. Cited by 54 papers in the map (data through 7 Oct 2026). First posted on arXiv 5 Jul 2016.

Builds on

No reference list yet.

Built on by

Cited by year (2026 to 7 Oct)

Citing papers per year: 2021: 20; 2022: 18; 2023: 2; 2024: 3; 2025: 9; 2026: 2.

54 papers.

Sort:

By area:

  1. 2021Ensemble deep learning: A reviewcited by 283
  2. 2022GAPartNet: Cross-Category Domain-Generalizable Object Perception and Manipulation via Generalizable and Actionable Partscited by 135
  3. 2022GradMax: Growing Neural Networks using Gradient Informationcited by 64
  4. 2024Neeko: Leveraging Dynamic LoRA for Efficient Multi-Character Role-Playing Agentcited by 52
  5. 2025FastCache: Fast Caching for Diffusion Transformer Through Learnable Linear Approximationcited by 23
  6. 2021Whither AutoML? Understanding the Role of Automation in Machine Learning Workflowscited by 22
  7. 2021Designing the Topology of Graph Neural Networks: A Novel Feature Fusion Perspectivecited by 19
  8. 2021Lifelong Teacher-Student Network Learningcited by 18
  9. 2021Lifelong Unsupervised Domain Adaptive Person Re-identification with Coordinated Anti-forgetting and Adaptationcited by 18
  10. 2022Winning solutions and post-challenge analyses of the ChaLearn AutoDL challenge 2019cited by 16
  11. 2025Balancing the Scales: A Theoretical and Algorithmic Framework for Learning from Imbalanced Datacited by 15
  12. 2021Lifelong Mixture of Variational Autoencoderscited by 10
38 more
  1. 2022AutoGPart: Intermediate Supervision Search for Generalizable 3D Part Segmentationcited by 10
  2. 2021Machine Learning for the Detection and Identification of Internet of Things (IoT) Devices: A Surveycited by 9
  3. 2021Continual Learning on the Edge with TensorFlow Litecited by 9
  4. 2021Does your graph need a confidence boost? Convergent boosted smoothing on graphs with tabular node featurescited by 9
  5. 2022Numerical Computation of Partial Differential Equations by Hidden-Layer Concatenated Extreme Learning Machinecited by 9
  6. 2025Improved Balanced Classification with Theoretically Grounded Loss Functionscited by 9
  7. 2022A Recursively Recurrent Neural Network (R2N2) Architecture for Learning Iterative Algorithmscited by 8
  8. 2022Boosted Dynamic Neural Networkscited by 8
  9. 2021Lifelong Infinite Mixture Model Based on Knowledge-Driven Dirichlet Processcited by 7
  10. 2021Neural Ensemble Search via Bayesian Samplingcited by 7
  11. 2021Towards continual task learning in artificial neural networks: current approaches and insights from neurosciencecited by 6
  12. 2022Task Selection for AutoML System Evaluationcited by 6
  13. 2024MobilityDL: A Review of Deep Learning From Trajectory Datacited by 6
  14. 2021Local Critic Training for Model-Parallel Learning of Deep Neural Networkscited by 5
  15. 2022Self-Activating Neural Ensembles for Continual Reinforcement Learningcited by 5
  16. 2021DOC3-Deep One Class Classification using Contradictionscited by 4
  17. 2024Continual Domain Adversarial Adaptation via Double-Head Discriminatorscited by 4
  18. 2025Random Feature Representation Boostingcited by 4
  19. 2022An Optimal Time Variable Learning Framework for Deep Neural Networkscited by 3
  20. 2022Learning an evolved mixture model for task-free continual learningcited by 3
  21. 2025Global Convergence of Continual Learning on Non-IID Datacited by 3
  22. 2021CUAB: Convolutional Uncertainty Attention Block Enhanced the Chest X-ray Image Analysiscited by 2
  23. 2022Towards Theoretical Analysis of Transformation Complexity of ReLU DNNscited by 2
  24. 2025Documenting Deployment with Fabric: A Repository of Real-World AI Governancecited by 2
  25. 2026Optimized Deferral for Imbalanced Settingscited by 2
  26. 2021Itsy Bitsy SpiderNet: Fully Connected Residual Network for Fraud Detectioncited by 1
  27. 2021Greedy UnMixing for Q-Learning in Multi-Agent Reinforcement Learningcited by 1
  28. 2021Reviewing continual learning from the perspective of human-level intelligencecited by 1
  29. 2022Meta-Learning of NAS for Few-shot Learning in Medical Image Applicationscited by 1
  30. 2022MDE for Machine Learning-Enabled Software Systems: A Case Study and Comparison of MontiAnna & ML-Quadratcited by 1
  31. 2022Review learning: Real world validation of privacy preserving continual learning across medical institutionscited by 1
  32. 2023SensLI: Sensitivity-Based Layer Insertion for Neural Networkscited by 1
  33. 2025Self-Controlled Dynamic Expansion Model for Continual Learningcited by 1
  34. 2021Targeted Deep Learning: Framework, Methods, and Applicationscited by 0
  35. 2021Proceedings of the First Workshop on Weakly Supervised Learning (WeaSuL)cited by 0
  36. 2022Look Closer to Your Enemy: Learning to Attack via Teacher-Student Mimickingcited by 0
  37. 2022A Survey of Open Source Automation Tools for Data Science Predictionscited by 0
  38. 2022CPMLHO:Hyperparameter Tuning via Cutting Plane and Mixed-Level Optimizationcited by 0

50 most cited of 54 All 54 (JSON)