FI-CBL: A Probabilistic Method for Concept-Based Learning with Expert Rules

• URL: http://arxiv.org/abs/2406.19897v1
• Date: Fri, 28 Jun 2024 13:05:17 GMT
• Title: FI-CBL: A Probabilistic Method for Concept-Based Learning with Expert Rules
• Authors: Lev V. Utkin, Andrei V. Konstantinov, Stanislav R. Kirpichenko,
• Abstract summary: The main idea behind the method is to divide each concept-annotated image into patches, to transform the patches into embeddings by using an autoencoder, and to cluster the embeddings. To find concepts of a new image, the method implements the frequentist inference by computing prior and posterior probabilities of concepts. Numerical experiments show that FI-CBL outperforms the concept bottleneck model in cases when the number of training data is small.
• Score: 2.2120851074630177
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: A method for solving concept-based learning (CBL) problem is proposed. The main idea behind the method is to divide each concept-annotated image into patches, to transform the patches into embeddings by using an autoencoder, and to cluster the embeddings assuming that each cluster will mainly contain embeddings of patches with certain concepts. To find concepts of a new image, the method implements the frequentist inference by computing prior and posterior probabilities of concepts based on rates of patches from images with certain values of the concepts. Therefore, the proposed method is called the Frequentist Inference CBL (FI-CBL). FI-CBL allows us to incorporate the expert rules in the form of logic functions into the inference procedure. An idea behind the incorporation is to update prior and conditional probabilities of concepts to satisfy the rules. The method is transparent because it has an explicit sequence of probabilistic calculations and a clear frequency interpretation. Numerical experiments show that FI-CBL outperforms the concept bottleneck model in cases when the number of training data is small. The code of proposed algorithms is publicly available.

Related papers

• Bayesian Concept Bottleneck Models with LLM Priors [9.368695619127084]
  Concept Bottleneck Models (CBMs) have been proposed as a compromise between white-box and black-box models, aiming to achieve interpretability without sacrificing accuracy. This work investigates a novel approach that sidesteps these challenges: BC-LLM iteratively searches over a potentially infinite set of concepts within a Bayesian framework, in which Large Language Models (LLMs) serve as both a concept extraction mechanism and prior.
  arXiv  Detail & Related papers  (2024-10-21T01:00:33Z)
• Explain via Any Concept: Concept Bottleneck Model with Open Vocabulary Concepts [8.028021897214238]
  "OpenCBM" is the first CBM with concepts of open vocabularies. Our model significantly outperforms the previous state-of-the-art CBM by 9% in the classification accuracy on the benchmark dataset CUB-200-2011.
  arXiv  Detail & Related papers  (2024-08-05T06:42:00Z)
• Discover-then-Name: Task-Agnostic Concept Bottlenecks via Automated Concept Discovery [52.498055901649025]
  Concept Bottleneck Models (CBMs) have been proposed to address the 'black-box' problem of deep neural networks. We propose a novel CBM approach -- called Discover-then-Name-CBM (DN-CBM) -- that inverts the typical paradigm. Our concept extraction strategy is efficient, since it is agnostic to the downstream task, and uses concepts already known to the model.
  arXiv  Detail & Related papers  (2024-07-19T17:50:11Z)
• Sparse Concept Bottleneck Models: Gumbel Tricks in Contrastive Learning [86.15009879251386]
  We propose a novel architecture and method of explainable classification with Concept Bottleneck Models (CBM) CBMs require an additional set of concepts to leverage. We show a significant increase in accuracy using sparse hidden layers in CLIP-based bottleneck models.
  arXiv  Detail & Related papers  (2024-04-04T09:43:43Z)
• Incorporating Expert Rules into Neural Networks in the Framework of Concept-Based Learning [2.9370710299422598]
  It is proposed how to combine logical rules and neural networks predicting the concept probabilities. We provide several approaches for solving the stated problem and for training neural networks. The code of proposed algorithms is publicly available.
  arXiv  Detail & Related papers  (2024-02-22T17:33:49Z)
• Beyond Concept Bottleneck Models: How to Make Black Boxes Intervenable? [8.391254800873599]
  We introduce a method to perform concept-based interventions on pretrained neural networks, which are not interpretable by design. We formalise the notion of intervenability as a measure of the effectiveness of concept-based interventions and leverage this definition to fine-tune black boxes.
  arXiv  Detail & Related papers  (2024-01-24T16:02:14Z)
• Statistically Significant Concept-based Explanation of Image Classifiers via Model Knockoffs [22.576922942465142]
  Concept-based explanations may cause false positives, which misregards unrelated concepts as important for the prediction task. We propose a method using a deep learning model to learn the image concept and then using the Knockoff samples to select the important concepts for prediction.
  arXiv  Detail & Related papers  (2023-05-27T05:40:05Z)
• Risk Consistent Multi-Class Learning from Label Proportions [64.0125322353281]
  This study addresses a multiclass learning from label proportions (MCLLP) setting in which training instances are provided in bags. Most existing MCLLP methods impose bag-wise constraints on the prediction of instances or assign them pseudo-labels. A risk-consistent method is proposed for instance classification using the empirical risk minimization framework.
  arXiv  Detail & Related papers  (2022-03-24T03:49:04Z)
• Structural Learning of Probabilistic Sentential Decision Diagrams under Partial Closed-World Assumption [127.439030701253]
  Probabilistic sentential decision diagrams are a class of structured-decomposable circuits. We propose a new scheme based on a partial closed-world assumption: data implicitly provide the logical base of the circuit. Preliminary experiments show that the proposed approach might properly fit training data, and generalize well to test data, provided that these remain consistent with the underlying logical base.
  arXiv  Detail & Related papers  (2021-07-26T12:01:56Z)
• Covariance-Free Sparse Bayesian Learning [62.24008859844098]
  We introduce a new SBL inference algorithm that avoids explicit inversions of the covariance matrix. Our method can be up to thousands of times faster than existing baselines. We showcase how our new algorithm enables SBL to tractably tackle high-dimensional signal recovery problems.
  arXiv  Detail & Related papers  (2021-05-21T16:20:07Z)
• Information Theoretic Meta Learning with Gaussian Processes [74.54485310507336]
  We formulate meta learning using information theoretic concepts; namely, mutual information and the information bottleneck. By making use of variational approximations to the mutual information, we derive a general and tractable framework for meta learning.
  arXiv  Detail & Related papers  (2020-09-07T16:47:30Z)

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.