Finding Interpretable Class-Specific Patterns through Efficient Neural Search

• URL: http://arxiv.org/abs/2312.04311v1
• Date: Thu, 7 Dec 2023 14:09:18 GMT
• Title: Finding Interpretable Class-Specific Patterns through Efficient Neural Search
• Authors: Nils Philipp Walter, Jonas Fischer, Jilles Vreeken
• Abstract summary: We propose a novel, inherently interpretable binary neural network architecture DNAPS that extracts differential patterns from data. DiffNaps is scalable to hundreds of thousands of features and robust to noise. We show on synthetic and real world data, including three biological applications, that, unlike its competitors, DiffNaps consistently yields accurate, succinct, and interpretable class descriptions.
• Score: 43.454121220860564
• License: http://creativecommons.org/licenses/by-nc-sa/4.0/
• Abstract: Discovering patterns in data that best describe the differences between classes allows to hypothesize and reason about class-specific mechanisms. In molecular biology, for example, this bears promise of advancing the understanding of cellular processes differing between tissues or diseases, which could lead to novel treatments. To be useful in practice, methods that tackle the problem of finding such differential patterns have to be readily interpretable by domain experts, and scalable to the extremely high-dimensional data. In this work, we propose a novel, inherently interpretable binary neural network architecture DIFFNAPS that extracts differential patterns from data. DiffNaps is scalable to hundreds of thousands of features and robust to noise, thus overcoming the limitations of current state-of-the-art methods in large-scale applications such as in biology. We show on synthetic and real world data, including three biological applications, that, unlike its competitors, DiffNaps consistently yields accurate, succinct, and interpretable class descriptions

Related papers

• Learning to refine domain knowledge for biological network inference [2.209921757303168]
  Perturbation experiments allow biologists to discover causal relationships between variables of interest. The sparsity and high dimensionality of these data pose significant challenges for causal structure learning algorithms. We propose an amortized algorithm for refining domain knowledge, based on data observations.
  arXiv  Detail & Related papers  (2024-10-18T12:53:23Z)
• Domain adaptation in small-scale and heterogeneous biological datasets [0.0]
  We discuss the benefits and challenges of domain adaptation in biological research. We argue for the incorporation of domain adaptation techniques to the computational biologist's toolkit.
  arXiv  Detail & Related papers  (2024-05-29T16:01:15Z)
• Seeing Unseen: Discover Novel Biomedical Concepts via Geometry-Constrained Probabilistic Modeling [53.7117640028211]
  We present a geometry-constrained probabilistic modeling treatment to resolve the identified issues. We incorporate a suite of critical geometric properties to impose proper constraints on the layout of constructed embedding space. A spectral graph-theoretic method is devised to estimate the number of potential novel classes.
  arXiv  Detail & Related papers  (2024-03-02T00:56:05Z)
• Neural-based classification rule learning for sequential data [0.0]
  We propose a novel differentiable fully interpretable method to discover both local and global patterns for rule-based binary classification. It consists of a convolutional binary neural network with an interpretable neural filter and a training strategy based on dynamically-enforced sparsity. We demonstrate the validity and usefulness of the approach on synthetic datasets and on an open-source peptides dataset.
  arXiv  Detail & Related papers  (2023-02-22T11:05:05Z)
• Self-Supervised Graph Representation Learning for Neuronal Morphologies [75.38832711445421]
  We present GraphDINO, a data-driven approach to learn low-dimensional representations of 3D neuronal morphologies from unlabeled datasets. We show, in two different species and across multiple brain areas, that this method yields morphological cell type clusterings on par with manual feature-based classification by experts. Our method could potentially enable data-driven discovery of novel morphological features and cell types in large-scale datasets.
  arXiv  Detail & Related papers  (2021-12-23T12:17:47Z)
• Discriminative Attribution from Counterfactuals [64.94009515033984]
  We present a method for neural network interpretability by combining feature attribution with counterfactual explanations. We show that this method can be used to quantitatively evaluate the performance of feature attribution methods in an objective manner.
  arXiv  Detail & Related papers  (2021-09-28T00:53:34Z)
• Learning Neural Causal Models with Active Interventions [83.44636110899742]
  We introduce an active intervention-targeting mechanism which enables a quick identification of the underlying causal structure of the data-generating process. Our method significantly reduces the required number of interactions compared with random intervention targeting. We demonstrate superior performance on multiple benchmarks from simulated to real-world data.
  arXiv  Detail & Related papers  (2021-09-06T13:10:37Z)
• Relational Subsets Knowledge Distillation for Long-tailed Retinal Diseases Recognition [65.77962788209103]
  We propose class subset learning by dividing the long-tailed data into multiple class subsets according to prior knowledge. It enforces the model to focus on learning the subset-specific knowledge. The proposed framework proved to be effective for the long-tailed retinal diseases recognition task.
  arXiv  Detail & Related papers  (2021-04-22T13:39:33Z)
• A More Biologically Plausible Local Learning Rule for ANNs [6.85316573653194]
  The proposed learning rule is derived from the concepts of spike timing dependant plasticity and neuronal association. A preliminary evaluation done on the binary classification of MNIST and IRIS datasets shows comparable performance with backpropagation. The local nature of learning gives a possibility of large scale distributed and parallel learning in the network.
  arXiv  Detail & Related papers  (2020-11-24T10:35:47Z)

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.