Towards Automated Knowledge Integration From Human-Interpretable Representations

• URL: http://arxiv.org/abs/2402.16105v5
• Date: Wed, 05 Feb 2025 12:40:54 GMT
• Title: Towards Automated Knowledge Integration From Human-Interpretable Representations
• Authors: Katarzyna Kobalczyk, Mihaela van der Schaar,
• Abstract summary: We introduce and motivate theoretically the principles of informed meta-learning enabling automated and controllable inductive bias selection. We empirically demonstrate the potential benefits and limitations of informed meta-learning in improving data efficiency and generalisation.
• Score: 55.2480439325792
• License:
• Abstract: A significant challenge in machine learning, particularly in noisy and low-data environments, lies in effectively incorporating inductive biases to enhance data efficiency and robustness. Despite the success of informed machine learning methods, designing algorithms with explicit inductive biases remains largely a manual process. In this work, we explore how prior knowledge represented in its native formats, e.g. in natural language, can be integrated into machine learning models in an automated manner. Inspired by the learning to learn principles of meta-learning, we consider the approach of learning to integrate knowledge via conditional meta-learning, a paradigm we refer to as informed meta-learning. We introduce and motivate theoretically the principles of informed meta-learning enabling automated and controllable inductive bias selection. To illustrate our claims, we implement an instantiation of informed meta-learning--the Informed Neural Process, and empirically demonstrate the potential benefits and limitations of informed meta-learning in improving data efficiency and generalisation.

Related papers

• Learn while Unlearn: An Iterative Unlearning Framework for Generative Language Models [49.043599241803825]
  Iterative Contrastive Unlearning (ICU) framework consists of three core components. A Knowledge Unlearning Induction module removes specific knowledge through an unlearning loss. A Contrastive Learning Enhancement module to preserve the model's expressive capabilities against the pure unlearning goal. And an Iterative Unlearning Refinement module that dynamically assess the unlearning extent on specific data pieces and make iterative update.
  arXiv  Detail & Related papers  (2024-07-25T07:09:35Z)
• Advances and Challenges in Meta-Learning: A Technical Review [7.149235250835041]
  Meta-learning empowers learning systems with the ability to acquire knowledge from multiple tasks. This review emphasizes its importance in real-world applications where data may be scarce or expensive to obtain.
  arXiv  Detail & Related papers  (2023-07-10T17:32:15Z)
• Deep Active Learning for Computer Vision: Past and Future [50.19394935978135]
  Despite its indispensable role for developing AI models, research on active learning is not as intensive as other research directions. By addressing data automation challenges and coping with automated machine learning systems, active learning will facilitate democratization of AI technologies.
  arXiv  Detail & Related papers  (2022-11-27T13:07:14Z)
• Learning with Limited Samples -- Meta-Learning and Applications to Communication Systems [46.760568562468606]
  Few-shot meta-learning optimize learning algorithms that can efficiently adapt to new tasks quickly. This review monograph provides an introduction to meta-learning by covering principles, algorithms, theory, and engineering applications.
  arXiv  Detail & Related papers  (2022-10-03T17:15:36Z)
• A Metamodel and Framework for Artificial General Intelligence From Theory to Practice [11.756425327193426]
  This paper introduces a new metamodel-based knowledge representation that significantly improves autonomous learning and adaptation. We have applied the metamodel to problems ranging from time series analysis, computer vision, and natural language understanding. One surprising consequence of the metamodel is that it not only enables a new level of autonomous learning and optimal functioning for machine intelligences.
  arXiv  Detail & Related papers  (2021-02-11T16:45:58Z)
• Online Structured Meta-learning [137.48138166279313]
  Current online meta-learning algorithms are limited to learn a globally-shared meta-learner. We propose an online structured meta-learning (OSML) framework to overcome this limitation. Experiments on three datasets demonstrate the effectiveness and interpretability of our proposed framework.
  arXiv  Detail & Related papers  (2020-10-22T09:10:31Z)
• Revisiting Meta-Learning as Supervised Learning [69.2067288158133]
  We aim to provide a principled, unifying framework by revisiting and strengthening the connection between meta-learning and traditional supervised learning. By treating pairs of task-specific data sets and target models as (feature, label) samples, we can reduce many meta-learning algorithms to instances of supervised learning. This view not only unifies meta-learning into an intuitive and practical framework but also allows us to transfer insights from supervised learning directly to improve meta-learning.
  arXiv  Detail & Related papers  (2020-02-03T06:13:01Z)
• From Learning to Meta-Learning: Reduced Training Overhead and Complexity for Communication Systems [40.427909614453526]
  Machine learning methods adapt the parameters of a model, constrained to lie in a given model class, by using a fixed learning procedure based on data or active observations. With a meta-trained inductive bias, training of a machine learning model can be potentially carried out with reduced training data and/or time complexity. This paper provides a high-level introduction to meta-learning with applications to communication systems.
  arXiv  Detail & Related papers  (2020-01-05T12:54:41Z)

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.