Neural Rule Ensembles: Encoding Sparse Feature Interactions into Neural Networks

• URL: http://arxiv.org/abs/2002.04319v1
• Date: Tue, 11 Feb 2020 11:22:20 GMT
• Title: Neural Rule Ensembles: Encoding Sparse Feature Interactions into Neural Networks
• Authors: Gitesh Dawer, Yangzi Guo, Sida Liu, Adrian Barbu
• Abstract summary: We use decision trees to capture relevant features and their interactions and define a mapping to encode extracted relationships into a neural network. At the same time through feature selection it enables learning of compact representations compared to state of the art tree-based approaches.
• Score: 3.7277730514654555
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Artificial Neural Networks form the basis of very powerful learning methods. It has been observed that a naive application of fully connected neural networks to data with many irrelevant variables often leads to overfitting. In an attempt to circumvent this issue, a prior knowledge pertaining to what features are relevant and their possible feature interactions can be encoded into these networks. In this work, we use decision trees to capture such relevant features and their interactions and define a mapping to encode extracted relationships into a neural network. This addresses the initialization related concern of fully connected neural networks. At the same time through feature selection it enables learning of compact representations compared to state of the art tree-based approaches. Empirical evaluations and simulation studies show the superiority of such an approach over fully connected neural networks and tree-based approaches

Related papers

• Coding schemes in neural networks learning classification tasks [52.22978725954347]
  We investigate fully-connected, wide neural networks learning classification tasks. We show that the networks acquire strong, data-dependent features. Surprisingly, the nature of the internal representations depends crucially on the neuronal nonlinearity.
  arXiv  Detail & Related papers  (2024-06-24T14:50:05Z)
• Graph Neural Networks for Learning Equivariant Representations of Neural Networks [55.04145324152541]
  We propose to represent neural networks as computational graphs of parameters. Our approach enables a single model to encode neural computational graphs with diverse architectures. We showcase the effectiveness of our method on a wide range of tasks, including classification and editing of implicit neural representations.
  arXiv  Detail & Related papers  (2024-03-18T18:01:01Z)
• Spiking neural network for nonlinear regression [68.8204255655161]
  Spiking neural networks carry the potential for a massive reduction in memory and energy consumption. They introduce temporal and neuronal sparsity, which can be exploited by next-generation neuromorphic hardware. A framework for regression using spiking neural networks is proposed.
  arXiv  Detail & Related papers  (2022-10-06T13:04:45Z)
• How and what to learn:The modes of machine learning [7.085027463060304]
  We propose a new approach, namely the weight pathway analysis (WPA), to study the mechanism of multilayer neural networks. WPA shows that a neural network stores and utilizes information in a "holographic" way, that is, the network encodes all training samples in a coherent structure. It is found that hidden-layer neurons self-organize into different classes in the later stages of the learning process.
  arXiv  Detail & Related papers  (2022-02-28T14:39:06Z)
• Data-driven emergence of convolutional structure in neural networks [83.4920717252233]
  We show how fully-connected neural networks solving a discrimination task can learn a convolutional structure directly from their inputs. By carefully designing data models, we show that the emergence of this pattern is triggered by the non-Gaussian, higher-order local structure of the inputs.
  arXiv  Detail & Related papers  (2022-02-01T17:11:13Z)
• Optimal Approximation with Sparse Neural Networks and Applications [0.0]
  We use deep sparsely connected neural networks to measure the complexity of a function class in $L(mathbb Rd)$. We also introduce representation system - a countable collection of functions to guide neural networks. We then analyse the complexity of a class called $beta$ cartoon-like functions using rate-distortion theory and wedgelets construction.
  arXiv  Detail & Related papers  (2021-08-14T05:14:13Z)
• Creating Powerful and Interpretable Models withRegression Networks [2.2049183478692584]
  We propose a novel architecture, Regression Networks, which combines the power of neural networks with the understandability of regression analysis. We demonstrate that the models exceed the state-of-the-art performance of interpretable models on several benchmark datasets.
  arXiv  Detail & Related papers  (2021-07-30T03:37:00Z)
• Learning Contact Dynamics using Physically Structured Neural Networks [81.73947303886753]
  We use connections between deep neural networks and differential equations to design a family of deep network architectures for representing contact dynamics between objects. We show that these networks can learn discontinuous contact events in a data-efficient manner from noisy observations. Our results indicate that an idealised form of touch feedback is a key component of making this learning problem tractable.
  arXiv  Detail & Related papers  (2021-02-22T17:33:51Z)
• Learning Connectivity of Neural Networks from a Topological Perspective [80.35103711638548]
  We propose a topological perspective to represent a network into a complete graph for analysis. By assigning learnable parameters to the edges which reflect the magnitude of connections, the learning process can be performed in a differentiable manner. This learning process is compatible with existing networks and owns adaptability to larger search spaces and different tasks.
  arXiv  Detail & Related papers  (2020-08-19T04:53:31Z)
• Locality Guided Neural Networks for Explainable Artificial Intelligence [12.435539489388708]
  We propose a novel algorithm for back propagation, called Locality Guided Neural Network(LGNN) LGNN preserves locality between neighbouring neurons within each layer of a deep network. In our experiments, we train various VGG and Wide ResNet (WRN) networks for image classification on CIFAR100.
  arXiv  Detail & Related papers  (2020-07-12T23:45:51Z)

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.