Adaptive Explainable Neural Networks (AxNNs)
- URL: http://arxiv.org/abs/2004.02353v2
- Date: Tue, 2 Jun 2020 06:18:04 GMT
- Title: Adaptive Explainable Neural Networks (AxNNs)
- Authors: Jie Chen, Joel Vaughan, Vijayan N. Nair, Agus Sudjianto
- Abstract summary: We develop a new framework called Adaptive Explainable Neural Networks (AxNN) for achieving the dual goals of good predictive performance and model interpretability.
For predictive performance, we build a structured neural network made up of ensembles of generalized additive model networks and additive index models.
For interpretability, we show how to decompose the results of AxNN into main effects and higher-order interaction effects.
- Score: 8.949704905866888
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- Abstract: While machine learning techniques have been successfully applied in several
fields, the black-box nature of the models presents challenges for interpreting
and explaining the results. We develop a new framework called Adaptive
Explainable Neural Networks (AxNN) for achieving the dual goals of good
predictive performance and model interpretability. For predictive performance,
we build a structured neural network made up of ensembles of generalized
additive model networks and additive index models (through explainable neural
networks) using a two-stage process. This can be done using either a boosting
or a stacking ensemble. For interpretability, we show how to decompose the
results of AxNN into main effects and higher-order interaction effects. The
computations are inherited from Google's open source tool AdaNet and can be
efficiently accelerated by training with distributed computing. The results are
illustrated on simulated and real datasets.
Related papers
- Preserving Information: How does Topological Data Analysis improve Neural Network performance? [0.0]
We introduce a method for integrating Topological Data Analysis (TDA) with Convolutional Neural Networks (CNN) in the context of image recognition.
Our approach, further referred to as Vector Stitching, involves combining raw image data with additional topological information.
The results of our experiments highlight the potential of incorporating results of additional data analysis into the network's inference process.
arXiv Detail & Related papers (2024-11-27T14:56:05Z) - GINN-KAN: Interpretability pipelining with applications in Physics Informed Neural Networks [5.2969467015867915]
We introduce the concept of interpretability pipelineing, to incorporate multiple interpretability techniques to outperform each individual technique.
We evaluate two recent models selected for their potential to incorporate interpretability into standard neural network architectures.
We introduce a novel interpretable neural network GINN-KAN that synthesizes the advantages of both models.
arXiv Detail & Related papers (2024-08-27T04:57:53Z) - 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) - Efficient and Flexible Neural Network Training through Layer-wise Feedback Propagation [49.44309457870649]
We present Layer-wise Feedback Propagation (LFP), a novel training principle for neural network-like predictors.
LFP decomposes a reward to individual neurons based on their respective contributions to solving a given task.
Our method then implements a greedy approach reinforcing helpful parts of the network and weakening harmful ones.
arXiv Detail & Related papers (2023-08-23T10:48:28Z) - NAR-Former: Neural Architecture Representation Learning towards Holistic
Attributes Prediction [37.357949900603295]
We propose a neural architecture representation model that can be used to estimate attributes holistically.
Experiment results show that our proposed framework can be used to predict the latency and accuracy attributes of both cell architectures and whole deep neural networks.
arXiv Detail & Related papers (2022-11-15T10:15:21Z) - Batch-Ensemble Stochastic Neural Networks for Out-of-Distribution
Detection [55.028065567756066]
Out-of-distribution (OOD) detection has recently received much attention from the machine learning community due to its importance in deploying machine learning models in real-world applications.
In this paper we propose an uncertainty quantification approach by modelling the distribution of features.
We incorporate an efficient ensemble mechanism, namely batch-ensemble, to construct the batch-ensemble neural networks (BE-SNNs) and overcome the feature collapse problem.
We show that BE-SNNs yield superior performance on several OOD benchmarks, such as the Two-Moons dataset, the FashionMNIST vs MNIST dataset, FashionM
arXiv Detail & Related papers (2022-06-26T16:00:22Z) - Causal Discovery and Knowledge Injection for Contestable Neural Networks
(with Appendices) [10.616061367794385]
We propose a two-way interaction whereby neural-network-empowered machines can expose the underpinning learnt causal graphs.
We show that our method improves predictive performance up to 2.4x while producing parsimonious networks, up to 7x smaller in the input layer.
arXiv Detail & Related papers (2022-05-19T18:21:12Z) - Dynamic Inference with Neural Interpreters [72.90231306252007]
We present Neural Interpreters, an architecture that factorizes inference in a self-attention network as a system of modules.
inputs to the model are routed through a sequence of functions in a way that is end-to-end learned.
We show that Neural Interpreters perform on par with the vision transformer using fewer parameters, while being transferrable to a new task in a sample efficient manner.
arXiv Detail & Related papers (2021-10-12T23:22:45Z) - 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) - How Neural Networks Extrapolate: From Feedforward to Graph Neural
Networks [80.55378250013496]
We study how neural networks trained by gradient descent extrapolate what they learn outside the support of the training distribution.
Graph Neural Networks (GNNs) have shown some success in more complex tasks.
arXiv Detail & Related papers (2020-09-24T17:48:59Z) - Neural Additive Models: Interpretable Machine Learning with Neural Nets [77.66871378302774]
Deep neural networks (DNNs) are powerful black-box predictors that have achieved impressive performance on a wide variety of tasks.
We propose Neural Additive Models (NAMs) which combine some of the expressivity of DNNs with the inherent intelligibility of generalized additive models.
NAMs learn a linear combination of neural networks that each attend to a single input feature.
arXiv Detail & Related papers (2020-04-29T01:28:32Z)
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.