Correlation aided Neural Networks: A correlation based approach of using feature importance to improve performance

2012 ◽  
Author(s):  
Ridwan Al Iqbal
Keyword(s):  
Neural Networks ◽  
Improve Performance ◽  
Feature Importance

scholarly journals Interpretation of Neural Networks Is Fragile

2019 ◽  
Vol 33 ◽  
pp. 3681-3688 ◽  
Author(s):  
Amirata Ghorbani ◽  
Abubakar Abid ◽  
James Zou
Keyword(s):  
Neural Network ◽  
Machine Learning ◽  
Neural Networks ◽  
Input Data ◽  
Learning Algorithm ◽  
Hessian Matrix ◽  
Machine Learning Algorithm ◽  
Feature Importance ◽  
Adversarial Attack ◽  
Measurement Biases

In order for machine learning to be trusted in many applications, it is critical to be able to reliably explain why the machine learning algorithm makes certain predictions. For this reason, a variety of methods have been developed recently to interpret neural network predictions by providing, for example, feature importance maps. For both scientific robustness and security reasons, it is important to know to what extent can the interpretations be altered by small systematic perturbations to the input data, which might be generated by adversaries or by measurement biases. In this paper, we demonstrate how to generate adversarial perturbations that produce perceptively indistinguishable inputs that are assigned the same predicted label, yet have very different interpretations. We systematically characterize the robustness of interpretations generated by several widely-used feature importance interpretation methods (feature importance maps, integrated gradients, and DeepLIFT) on ImageNet and CIFAR-10. In all cases, our experiments show that systematic perturbations can lead to dramatically different interpretations without changing the label. We extend these results to show that interpretations based on exemplars (e.g. influence functions) are similarly susceptible to adversarial attack. Our analysis of the geometry of the Hessian matrix gives insight on why robustness is a general challenge to current interpretation approaches.

scholarly journals Explaining Neural Networks with Functional DataUsing PCA and Feature Importance.

2020 ◽  
Author(s):  
Katherine Goode ◽  
Daniel Ries ◽  
Joshua Zollweg
Keyword(s):  
Neural Networks ◽  
Feature Importance

scholarly journals Classifier-Based Evaluation of Image Feature Importance

10.29007/p655 ◽  
2018 ◽  
Author(s):  
Sai Prabhakar Pandi Selvaraj ◽  
Manuela Veloso ◽  
Stephanie Rosenthal
Keyword(s):  
Neural Networks ◽  
Image Classification ◽  
Deep Neural Networks ◽  
Relative Size ◽  
Objective Measure ◽  
Image Feature ◽  
Important Region ◽  
Feature Importance ◽  
Image Pixels

Significant advances in the performance of deep neural networks, such as Convolutional Neural Networks (CNNs) for image classification, have created a drive for understanding how they work. Different techniques have been proposed to determine which features (e.g., image pixels) are most important for a CNN’s classification. However, the important features output by these techniques have typically been judged subjectively by a human to assess whether the important features capture the features relevant to the classification and not whether the features were actually important to classifier itself. We address the need for an objective measure to assess the quality of different feature importance measures. In particular, we propose measuring the ratio of a CNN’s accuracy on the whole image com- pared to an image containing only the important features. We also consider scaling this ratio by the relative size of the important region in order to measure the conciseness. We demonstrate that our measures correlate well with prior subjective comparisons of important features, but importantly do not require their human studies. We also demonstrate that the features on which multiple techniques agree are important have a higher impact on accuracy than those features that only one technique finds.

scholarly journals HalluciNet-ing Spatiotemporal Representations Using a 2D-CNN

Signals ◽  
2021 ◽  
Vol 2 (3) ◽  
pp. 604-618
Author(s):  
Paritosh Parmar ◽  
Brendan Morris
Keyword(s):  
Neural Networks ◽  
State Of The Art ◽  
Scene Recognition ◽  
Multitask Learning ◽  
Training Phase ◽  
Dynamic Scene ◽  
Improve Performance ◽  
Computing Power ◽  
Practical Standpoint ◽  
3D Cnn

Spatiotemporal representations learned using 3D convolutional neural networks (CNN) are currently used in state-of-the-art approaches for action-related tasks. However, 3D-CNN are notorious for being memory and compute resource intensive as compared with more simple 2D-CNN architectures. We propose to hallucinate spatiotemporal representations from a 3D-CNN teacher with a 2D-CNN student. By requiring the 2D-CNN to predict the future and intuit upcoming activity, it is encouraged to gain a deeper understanding of actions and how they evolve. The hallucination task is treated as an auxiliary task, which can be used with any other action-related task in a multitask learning setting. Thorough experimental evaluation, it is shown that the hallucination task indeed helps improve performance on action recognition, action quality assessment, and dynamic scene recognition tasks. From a practical standpoint, being able to hallucinate spatiotemporal representations without an actual 3D-CNN can enable deployment in resource-constrained scenarios, such as with limited computing power and/or lower bandwidth. We also observed that our hallucination task has utility not only during the training phase, but also during the pre-training phase.

scholarly journals Brain Age Prediction With Morphological Features Using Deep Neural Networks: Results From Predictive Analytic Competition 2019

2021 ◽  
Vol 11 ◽  
Author(s):  
Angela Lombardi ◽  
Alfonso Monaco ◽  
Giacinto Donvito ◽  
Nicola Amoroso ◽  
Roberto Bellotti ◽  
...  
Keyword(s):  
Neural Networks ◽  
Deep Neural Networks ◽  
Morphological Changes ◽  
Structure Characterization ◽  
Feature Importance ◽  
Mri Scans ◽  
Magnetic Resonance Imaging Mri ◽  
Brain Age ◽  
The Brain ◽  
Age Prediction

Morphological changes in the brain over the lifespan have been successfully described by using structural magnetic resonance imaging (MRI) in conjunction with machine learning (ML) algorithms. International challenges and scientific initiatives to share open access imaging datasets also contributed significantly to the advance in brain structure characterization and brain age prediction methods. In this work, we present the results of the predictive model based on deep neural networks (DNN) proposed during the Predictive Analytic Competition 2019 for brain age prediction of 2638 healthy individuals. We used FreeSurfer software to extract some morphological descriptors from the raw MRI scans of the subjects collected from 17 sites. We compared the proposed DNN architecture with other ML algorithms commonly used in the literature (RF, SVR, Lasso). Our results highlight that the DNN models achieved the best performance with MAE = 4.6 on the hold-out test, outperforming the other ML strategies. We also propose a complete ML framework to perform a robust statistical evaluation of feature importance for the clinical interpretability of the results.

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