Differential Replication in Machine Learning

• URL: http://arxiv.org/abs/2007.07981v1
• Date: Wed, 15 Jul 2020 20:26:49 GMT
• Title: Differential Replication in Machine Learning
• Authors: Irene Unceta, Jordi Nin and Oriol Pujol
• Abstract summary: We propose a solution based on reusing the knowledge acquired by the already deployed machine learning models. This is the idea behind differential replication of machine learning models.
• Score: 0.90238471756546
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: When deployed in the wild, machine learning models are usually confronted with data and requirements that constantly vary, either because of changes in the generating distribution or because external constraints change the environment where the model operates. To survive in such an ecosystem, machine learning models need to adapt to new conditions by evolving over time. The idea of model adaptability has been studied from different perspectives. In this paper, we propose a solution based on reusing the knowledge acquired by the already deployed machine learning models and leveraging it to train future generations. This is the idea behind differential replication of machine learning models.

Related papers

• Deep Generative Models in Robotics: A Survey on Learning from Multimodal Demonstrations [52.11801730860999]
  In recent years, the robot learning community has shown increasing interest in using deep generative models to capture the complexity of large datasets. We present the different types of models that the community has explored, such as energy-based models, diffusion models, action value maps, or generative adversarial networks. We also present the different types of applications in which deep generative models have been used, from grasp generation to trajectory generation or cost learning.
  arXiv  Detail & Related papers  (2024-08-08T11:34:31Z)
• Machine Unlearning in Contrastive Learning [3.6218162133579694]
  We introduce a novel gradient constraint-based approach for training the model to effectively achieve machine unlearning. Our approach demonstrates proficient performance not only on contrastive learning models but also on supervised learning models.
  arXiv  Detail & Related papers  (2024-05-12T16:09:01Z)
• Constructing Effective Machine Learning Models for the Sciences: A Multidisciplinary Perspective [77.53142165205281]
  We show how flexible non-linear solutions will not always improve upon manually adding transforms and interactions between variables to linear regression models. We discuss how to recognize this before constructing a data-driven model and how such analysis can help us move to intrinsically interpretable regression models.
  arXiv  Detail & Related papers  (2022-11-21T17:48:44Z)
• Synthetic Model Combination: An Instance-wise Approach to Unsupervised Ensemble Learning [92.89846887298852]
  Consider making a prediction over new test data without any opportunity to learn from a training set of labelled data. Give access to a set of expert models and their predictions alongside some limited information about the dataset used to train them.
  arXiv  Detail & Related papers  (2022-10-11T10:20:31Z)
• Towards Interpretable Deep Reinforcement Learning Models via Inverse Reinforcement Learning [27.841725567976315]
  We propose a novel framework utilizing Adversarial Inverse Reinforcement Learning. This framework provides global explanations for decisions made by a Reinforcement Learning model. We capture intuitive tendencies that the model follows by summarizing the model's decision-making process.
  arXiv  Detail & Related papers  (2022-03-30T17:01:59Z)
• Beyond Trivial Counterfactual Explanations with Diverse Valuable Explanations [64.85696493596821]
  In computer vision applications, generative counterfactual methods indicate how to perturb a model's input to change its prediction. We propose a counterfactual method that learns a perturbation in a disentangled latent space that is constrained using a diversity-enforcing loss. Our model improves the success rate of producing high-quality valuable explanations when compared to previous state-of-the-art methods.
  arXiv  Detail & Related papers  (2021-03-18T12:57:34Z)
• Choice modelling in the age of machine learning -- discussion paper [0.27998963147546135]
  Cross-pollination of machine learning models, techniques and practices could help overcome problems and limitations encountered in the current theory-driven paradigm. Despite the potential benefits of using the advances of machine learning to improve choice modelling practices, the choice modelling field has been hesitant to embrace machine learning.
  arXiv  Detail & Related papers  (2021-01-28T11:57:08Z)
• Knowledge as Invariance -- History and Perspectives of Knowledge-augmented Machine Learning [69.99522650448213]
  Research in machine learning is at a turning point. Research interests are shifting away from increasing the performance of highly parameterized models to exceedingly specific tasks. This white paper provides an introduction and discussion of this emerging field in machine learning research.
  arXiv  Detail & Related papers  (2020-12-21T15:07:19Z)
• Generative Temporal Difference Learning for Infinite-Horizon Prediction [101.59882753763888]
  We introduce the $gamma$-model, a predictive model of environment dynamics with an infinite probabilistic horizon. We discuss how its training reflects an inescapable tradeoff between training-time and testing-time compounding errors.
  arXiv  Detail & Related papers  (2020-10-27T17:54:12Z)
• A Hierarchy of Limitations in Machine Learning [0.0]
  This paper attempts a comprehensive, structured overview of the specific conceptual, procedural, and statistical limitations of models in machine learning when applied to society. Modelers themselves can use the described hierarchy to identify possible failure points and think through how to address them. Consumers of machine learning models can know what to question when confronted with the decision about if, where, and how to apply machine learning.
  arXiv  Detail & Related papers  (2020-02-12T19:39:29Z)
• Machine Learning Approaches For Motor Learning: A Short Review [1.5736917233375265]
  We outline existing machine learning models for motor learning and their adaptation capabilities. We identify and describe three types of adaptation: Reinforcement in probabilistic models, Transfer and meta-learning in deep neural networks, and Planning adaptation by reinforcement learning.
  arXiv  Detail & Related papers  (2020-02-11T11:11:26Z)

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.