On the Effectiveness of Interpretable Feedforward Neural Network
- URL: http://arxiv.org/abs/2111.02303v1
- Date: Wed, 3 Nov 2021 15:39:24 GMT
- Title: On the Effectiveness of Interpretable Feedforward Neural Network
- Authors: Miles Q. Li, Benjamin C. M. Fung, Adel Abusitta
- Abstract summary: interpretable feedforward neural network (IFFNN) proposed that achieves both high classification performance and interpretability for malware detection.
In this paper, we propose a way to generalize the interpretable feedforward neural network to multi-class classification scenarios and any type of feedforward neural networks.
- Score: 3.9124823111588176
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- Abstract: Deep learning models have achieved state-of-the-art performance in many
classification tasks. However, most of them cannot provide an interpretation
for their classification results. Machine learning models that are
interpretable are usually linear or piecewise linear and yield inferior
performance. Non-linear models achieve much better classification performance,
but it is hard to interpret their classification results. This may have been
changed by an interpretable feedforward neural network (IFFNN) proposed that
achieves both high classification performance and interpretability for malware
detection. If the IFFNN can perform well in a more flexible and general form
for other classification tasks while providing meaningful interpretations, it
may be of great interest to the applied machine learning community. In this
paper, we propose a way to generalize the interpretable feedforward neural
network to multi-class classification scenarios and any type of feedforward
neural networks, and evaluate its classification performance and
interpretability on intrinsic interpretable datasets. We conclude by finding
that the generalized IFFNNs achieve comparable classification performance to
their normal feedforward neural network counterparts and provide meaningful
interpretations. Thus, this kind of neural network architecture has great
practical use.
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) - Hidden Classification Layers: Enhancing linear separability between
classes in neural networks layers [0.0]
We investigate the impact on deep network performances of a training approach.
We propose a neural network architecture which induces an error function involving the outputs of all the network layers.
arXiv Detail & Related papers (2023-06-09T10:52:49Z) - Wide and Deep Neural Networks Achieve Optimality for Classification [23.738242876364865]
We identify and construct an explicit set of neural network classifiers that achieve optimality.
In particular, we provide explicit activation functions that can be used to construct networks that achieve optimality.
Our results highlight the benefit of using deep networks for classification tasks, in contrast to regression tasks, where excessive depth is harmful.
arXiv Detail & Related papers (2022-04-29T14:27:42Z) - Do We Really Need a Learnable Classifier at the End of Deep Neural
Network? [118.18554882199676]
We study the potential of learning a neural network for classification with the classifier randomly as an ETF and fixed during training.
Our experimental results show that our method is able to achieve similar performances on image classification for balanced datasets.
arXiv Detail & Related papers (2022-03-17T04:34:28Z) - Interpretable part-whole hierarchies and conceptual-semantic
relationships in neural networks [4.153804257347222]
We present Agglomerator, a framework capable of providing a representation of part-whole hierarchies from visual cues.
We evaluate our method on common datasets, such as SmallNORB, MNIST, FashionMNIST, CIFAR-10, and CIFAR-100.
arXiv Detail & Related papers (2022-03-07T10:56:13Z) - 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) - FF-NSL: Feed-Forward Neural-Symbolic Learner [70.978007919101]
This paper introduces a neural-symbolic learning framework, called Feed-Forward Neural-Symbolic Learner (FF-NSL)
FF-NSL integrates state-of-the-art ILP systems based on the Answer Set semantics, with neural networks, in order to learn interpretable hypotheses from labelled unstructured data.
arXiv Detail & Related papers (2021-06-24T15:38:34Z) - Gone Fishing: Neural Active Learning with Fisher Embeddings [55.08537975896764]
There is an increasing need for active learning algorithms that are compatible with deep neural networks.
This article introduces BAIT, a practical representation of tractable, and high-performing active learning algorithm for neural networks.
arXiv Detail & Related papers (2021-06-17T17:26:31Z) - Neural networks adapting to datasets: learning network size and topology [77.34726150561087]
We introduce a flexible setup allowing for a neural network to learn both its size and topology during the course of a gradient-based training.
The resulting network has the structure of a graph tailored to the particular learning task and dataset.
arXiv Detail & Related papers (2020-06-22T12:46:44Z) - Adaptive Explainable Neural Networks (AxNNs) [8.949704905866888]
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.
arXiv Detail & Related papers (2020-04-05T23:40:57Z)
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.