Steering Neural Network Training through Interpretable Constraints Based on Partial Dependence

• URL: http://arxiv.org/abs/2607.08641v1
• Date: Thu, 09 Jul 2026 16:16:27 GMT
• Title: Steering Neural Network Training through Interpretable Constraints Based on Partial Dependence
• Abstract summary: We introduce a new approach to steering neural networks based on partial dependence.<n>We demonstrate that models whose training has been controlled perform better than unconstrained models.<n>We highlight that interpretations obtained from the former actually align with the user-provided knowledge, whereas those obtained from the latter do not.
• Score: 2.5106710808160497
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Over the last few years, there has been an increased interest in making machine learning models more interpretable. Although a great deal of effort goes into developing techniques for interpreting the interactions learned by a given model, fewer studies focus on assessing the quality of such explanations. Even fewer focus on how to adjust the model to produce explanations faithful to prior knowledge, a process known as explanation-guided learning. Furthermore, most approaches in this area focus on classification problems and usually assume prior knowledge about which input features or regions are most important. In this work, we introduce a new approach to steering neural networks based on partial dependence, such that their average response to certain features aligns with specific functional domain knowledge about the problem. We empirically demonstrate on a range of regression problems, including dynamical systems forecasting, that models whose training has been controlled using our method perform better than unconstrained models and are more data-efficient. Moreover, we highlight that interpretations obtained from the former actually align with the user-provided knowledge, whereas those obtained from the latter do not.

Related papers

• Provenance Networks: End-to-End Exemplar-Based Explainability [0.0]
  We introduce provenance networks, a novel class of neural models designed to provide end-to-end, training-data-driven explainability.<n>Provenance networks learn to link each prediction directly to its supporting training examples as part of the model's normal operation.<n>It addresses critical challenges in modern deep learning, including model opaqueness, hallucination, and the assignment of credit to data contributors.
  arXiv  Detail & Related papers  (2025-10-03T01:48:38Z)
• Neural Network Reprogrammability: A Unified Theme on Model Reprogramming, Prompt Tuning, and Prompt Instruction [57.19302613163439]
  We introduce neural network reprogrammability as a unifying framework for model adaptation.<n>We present a taxonomy that categorizes such information manipulation approaches across four key dimensions.<n>We also analyze remaining technical challenges and ethical considerations.
  arXiv  Detail & Related papers  (2025-06-05T05:42:27Z)
• Knowledge Graph Enhanced Generative Multi-modal Models for Class-Incremental Learning [51.0864247376786]
  We introduce a Knowledge Graph Enhanced Generative Multi-modal model (KG-GMM) that builds an evolving knowledge graph throughout the learning process.<n>During testing, we propose a Knowledge Graph Augmented Inference method that locates specific categories by analyzing relationships within the generated text.
  arXiv  Detail & Related papers  (2025-03-24T07:20:43Z)
• How to Probe: Simple Yet Effective Techniques for Improving Post-hoc Explanations [69.72654127617058]
  Post-hoc importance attribution methods are a popular tool for "explaining" Deep Neural Networks (DNNs)<n>In this work we bring forward empirical evidence that challenges this very notion.<n>We discover a strong dependency on and demonstrate that the training details of a pre-trained model's classification layer play a crucial role.
  arXiv  Detail & Related papers  (2025-03-01T22:25:11Z)
• Explaining Deep Models through Forgettable Learning Dynamics [12.653673008542155]
  We visualize the learning behaviour during training by tracking how often samples are learned and forgotten in subsequent training epochs. Inspired by this phenomenon, we present a novel segmentation method that actively uses this information to alter the data representation within the model.
  arXiv  Detail & Related papers  (2023-01-10T21:59:20Z)
• Reinforcement Learning in System Identification [0.0]
  System identification, also known as learning forward models, transfer functions, system dynamics, etc., has a long tradition both in science and engineering. Here we explore the use of Reinforcement Learning in this problem. We elaborate on why and how this problem fits naturally and sound as a Reinforcement Learning problem, and present some experimental results that demonstrate RL is a promising technique to solve these kind of problems.
  arXiv  Detail & Related papers  (2022-12-14T09:20:42Z)
• Class-Incremental Learning by Knowledge Distillation with Adaptive Feature Consolidation [39.97128550414934]
  We present a novel class incremental learning approach based on deep neural networks. It continually learns new tasks with limited memory for storing examples in the previous tasks. Our algorithm is based on knowledge distillation and provides a principled way to maintain the representations of old models.
  arXiv  Detail & Related papers  (2022-04-02T16:30:04Z)
• On Generalizing Beyond Domains in Cross-Domain Continual Learning [91.56748415975683]
  Deep neural networks often suffer from catastrophic forgetting of previously learned knowledge after learning a new task. Our proposed approach learns new tasks under domain shift with accuracy boosts up to 10% on challenging datasets such as DomainNet and OfficeHome.
  arXiv  Detail & Related papers  (2022-03-08T09:57:48Z)
• Multicriteria interpretability driven Deep Learning [0.0]
  Deep Learning methods are renowned for their performances, yet their lack of interpretability prevents them from high-stakes contexts. Recent model methods address this problem by providing post-hoc interpretability methods by reverse-engineering the model's inner workings. We propose a Multicriteria agnostic technique that allows to control the feature effects on the model's outcome by injecting knowledge in the objective function.
  arXiv  Detail & Related papers  (2021-11-28T09:41:13Z)
• Model-Based Deep Learning [155.063817656602]
  Signal processing, communications, and control have traditionally relied on classical statistical modeling techniques. Deep neural networks (DNNs) use generic architectures which learn to operate from data, and demonstrate excellent performance. We are interested in hybrid techniques that combine principled mathematical models with data-driven systems to benefit from the advantages of both approaches.
  arXiv  Detail & Related papers  (2020-12-15T16:29:49Z)
• Developing Constrained Neural Units Over Time [81.19349325749037]
  This paper focuses on an alternative way of defining Neural Networks, that is different from the majority of existing approaches. The structure of the neural architecture is defined by means of a special class of constraints that are extended also to the interaction with data. The proposed theory is cast into the time domain, in which data are presented to the network in an ordered manner.
  arXiv  Detail & Related papers  (2020-09-01T09:07:25Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.