Cross Spline Net and a Unified World

• URL: http://arxiv.org/abs/2410.19154v1
• Date: Thu, 24 Oct 2024 20:45:48 GMT
• Title: Cross Spline Net and a Unified World
• Authors: Linwei Hu, Ye Jin Choi, Vijayan N. Nair,
• Abstract summary: Cross spline net (CSN) is based on a combination of spline transformation and cross-network. CSN provides a unified modeling framework that puts the above set of non-neural network models under the same neural network framework. We will show CSN is as performant and convenient to use, and is less complicated, more interpretable and robust.
• Score: 41.69175843713757
• License:
• Abstract: In today's machine learning world for tabular data, XGBoost and fully connected neural network (FCNN) are two most popular methods due to their good model performance and convenience to use. However, they are highly complicated, hard to interpret, and can be overfitted. In this paper, we propose a new modeling framework called cross spline net (CSN) that is based on a combination of spline transformation and cross-network (Wang et al. 2017, 2021). We will show CSN is as performant and convenient to use, and is less complicated, more interpretable and robust. Moreover, the CSN framework is flexible, as the spline layer can be configured differently to yield different models. With different choices of the spline layer, we can reproduce or approximate a set of non-neural network models, including linear and spline-based statistical models, tree, rule-fit, tree-ensembles (gradient boosting trees, random forest), oblique tree/forests, multi-variate adaptive regression spline (MARS), SVM with polynomial kernel, etc. Therefore, CSN provides a unified modeling framework that puts the above set of non-neural network models under the same neural network framework. By using scalable and powerful gradient descent algorithms available in neural network libraries, CSN avoids some pitfalls (such as being ad-hoc, greedy or non-scalable) in the case-specific optimization methods used in the above non-neural network models. We will use a special type of CSN, TreeNet, to illustrate our point. We will compare TreeNet with XGBoost and FCNN to show the benefits of TreeNet. We believe CSN will provide a flexible and convenient framework for practitioners to build performant, robust and more interpretable models.

Related papers

• Learning Load Balancing with GNN in MPTCP-Enabled Heterogeneous Networks [13.178956651532213]
  We propose a graph neural network (GNN)-based model to tackle the LB problem for MP TCP-enabled HetNets. Compared to the conventional deep neural network (DNN), the proposed GNN-based model exhibits two key strengths.
  arXiv  Detail & Related papers  (2024-10-22T15:49:53Z)
• Bayesian Inference Accelerator for Spiking Neural Networks [3.145754107337963]
  spiking neural networks (SNNs) have the potential to reduce computational area and power. In this work, we demonstrate an optimization framework for developing and implementing efficient Bayesian SNNs in hardware. We demonstrate accuracies comparable to Bayesian binary networks with full-precision Bernoulli parameters, while requiring up to $25times$ less spikes.
  arXiv  Detail & Related papers  (2024-01-27T16:27:19Z)
• Stitchable Neural Networks [40.8842135978138]
  We present Stitchable Neural Networks (SN-Net), a novel scalable and efficient framework for model deployment. SN-Net splits the anchors across the blocks/layers and then stitches them together with simple stitching layers to map the activations from one anchor to another. Experiments on ImageNet classification demonstrate that SN-Net can obtain on-par or even better performance than many individually trained networks.
  arXiv  Detail & Related papers  (2023-02-13T18:37:37Z)
• Neural Attentive Circuits [93.95502541529115]
  We introduce a general purpose, yet modular neural architecture called Neural Attentive Circuits (NACs) NACs learn the parameterization and a sparse connectivity of neural modules without using domain knowledge. NACs achieve an 8x speedup at inference time while losing less than 3% performance.
  arXiv  Detail & Related papers  (2022-10-14T18:00:07Z)
• XBNet : An Extremely Boosted Neural Network [0.0]
  XBNet tries to combine tree-based models with that of neural networks to create a robust architecture trained by using a novel optimization technique, Boosted Gradient Descent for Tabular Data. In this paper, we describe a novel architecture XBNet, which tries to combine tree-based models with that of neural networks to create a robust architecture trained by using a novel optimization technique, Boosted Gradient Descent for Tabular Data.
  arXiv  Detail & Related papers  (2021-06-09T17:31:50Z)
• Growing Deep Forests Efficiently with Soft Routing and Learned Connectivity [79.83903179393164]
  This paper further extends the deep forest idea in several important aspects. We employ a probabilistic tree whose nodes make probabilistic routing decisions, a.k.a., soft routing, rather than hard binary decisions. Experiments on the MNIST dataset demonstrate that our empowered deep forests can achieve better or comparable performance than [1],[3].
  arXiv  Detail & Related papers  (2020-12-29T18:05:05Z)
• Dynamic Graph: Learning Instance-aware Connectivity for Neural Networks [78.65792427542672]
  Dynamic Graph Network (DG-Net) is a complete directed acyclic graph, where the nodes represent convolutional blocks and the edges represent connection paths. Instead of using the same path of the network, DG-Net aggregates features dynamically in each node, which allows the network to have more representation ability.
  arXiv  Detail & Related papers  (2020-10-02T16:50:26Z)
• Binarizing MobileNet via Evolution-based Searching [66.94247681870125]
  We propose a use of evolutionary search to facilitate the construction and training scheme when binarizing MobileNet. Inspired by one-shot architecture search frameworks, we manipulate the idea of group convolution to design efficient 1-Bit Convolutional Neural Networks (CNNs) Our objective is to come up with a tiny yet efficient binary neural architecture by exploring the best candidates of the group convolution.
  arXiv  Detail & Related papers  (2020-05-13T13:25:51Z)
• Model Fusion via Optimal Transport [64.13185244219353]
  We present a layer-wise model fusion algorithm for neural networks. We show that this can successfully yield "one-shot" knowledge transfer between neural networks trained on heterogeneous non-i.i.d. data.
  arXiv  Detail & Related papers  (2019-10-12T22:07:15Z)

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.