A Robust Unsupervised Ensemble of Feature-Based Explanations using Restricted Boltzmann Machines

• URL: http://arxiv.org/abs/2111.07379v1
• Date: Sun, 14 Nov 2021 15:58:21 GMT
• Title: A Robust Unsupervised Ensemble of Feature-Based Explanations using Restricted Boltzmann Machines
• Authors: Vadim Borisov, Johannes Meier, Johan van den Heuvel, Hamed Jalali, Gjergji Kasneci
• Abstract summary: We propose a technique for aggregating the feature attributions of different explanatory algorithms using Restricted Boltzmann Machines (RBMs) Several challenging experiments on real-world datasets show that the proposed RBM method outperforms popular feature attribution methods and basic ensemble techniques.
• Score: 4.821071466968101
• License: http://creativecommons.org/licenses/by-nc-nd/4.0/
• Abstract: Understanding the results of deep neural networks is an essential step towards wider acceptance of deep learning algorithms. Many approaches address the issue of interpreting artificial neural networks, but often provide divergent explanations. Moreover, different hyperparameters of an explanatory method can lead to conflicting interpretations. In this paper, we propose a technique for aggregating the feature attributions of different explanatory algorithms using Restricted Boltzmann Machines (RBMs) to achieve a more reliable and robust interpretation of deep neural networks. Several challenging experiments on real-world datasets show that the proposed RBM method outperforms popular feature attribution methods and basic ensemble techniques.

Related papers

• Perturbation on Feature Coalition: Towards Interpretable Deep Neural Networks [0.1398098625978622]
  The "black box" nature of deep neural networks (DNNs) compromises their transparency and reliability. We introduce a perturbation-based interpretation guided by feature coalitions, which leverages deep information of network to extract correlated features.
  arXiv  Detail & Related papers  (2024-08-23T22:44:21Z)
• Manipulating Feature Visualizations with Gradient Slingshots [54.31109240020007]
  We introduce a novel method for manipulating Feature Visualization (FV) without significantly impacting the model's decision-making process. We evaluate the effectiveness of our method on several neural network models and demonstrate its capabilities to hide the functionality of arbitrarily chosen neurons.
  arXiv  Detail & Related papers  (2024-01-11T18:57:17Z)
• Learning Interpretable Deep Disentangled Neural Networks for Hyperspectral Unmixing [16.02193274044797]
  We propose a new interpretable deep learning method for hyperspectral unmixing that accounts for nonlinearity and endmember variability. The model is learned end-to-end using backpropagation, and trained using a self-supervised strategy. Experimental results on synthetic and real datasets illustrate the performance of the proposed method.
  arXiv  Detail & Related papers  (2023-10-03T18:21:37Z)
• Adversarial Attacks on the Interpretation of Neuron Activation Maximization [70.5472799454224]
  Activation-maximization approaches are used to interpret and analyze trained deep-learning models. In this work, we consider the concept of an adversary manipulating a model for the purpose of deceiving the interpretation.
  arXiv  Detail & Related papers  (2023-06-12T19:54:33Z)
• Robust Explanation Constraints for Neural Networks [33.14373978947437]
  Post-hoc explanation methods used with the intent of neural networks are sometimes said to help engender trust in their outputs. Our training method is the only method able to learn neural networks with insights about robustness tested across all six tested networks.
  arXiv  Detail & Related papers  (2022-12-16T14:40:25Z)
• Interpretable Social Anchors for Human Trajectory Forecasting in Crowds [84.20437268671733]
  We propose a neural network-based system to predict human trajectory in crowds. We learn interpretable rule-based intents, and then utilise the expressibility of neural networks to model scene-specific residual. Our architecture is tested on the interaction-centric benchmark TrajNet++.
  arXiv  Detail & Related papers  (2021-05-07T09:22:34Z)
• A neural anisotropic view of underspecification in deep learning [60.119023683371736]
  We show that the way neural networks handle the underspecification of problems is highly dependent on the data representation. Our results highlight that understanding the architectural inductive bias in deep learning is fundamental to address the fairness, robustness, and generalization of these systems.
  arXiv  Detail & Related papers  (2021-04-29T14:31:09Z)
• Multivariate Deep Evidential Regression [77.34726150561087]
  A new approach with uncertainty-aware neural networks shows promise over traditional deterministic methods. We discuss three issues with a proposed solution to extract aleatoric and epistemic uncertainties from regression-based neural networks.
  arXiv  Detail & Related papers  (2021-04-13T12:20:18Z)
• Towards Robust Explanations for Deep Neural Networks [5.735035463793008]
  We develop a unified theoretical framework for deriving bounds on the maximal manipulability of a model. We present three different techniques to boost robustness against manipulation.
  arXiv  Detail & Related papers  (2020-12-18T18:29:09Z)
• Making Neural Networks Interpretable with Attribution: Application to Implicit Signals Prediction [11.427019313283997]
  We propose a novel formulation of interpretable deep neural networks for the attribution task. Using masked weights, hidden features can be deeply attributed, split into several input-restricted sub-networks and trained as a boosted mixture of experts.
  arXiv  Detail & Related papers  (2020-08-26T06:46:49Z)
• ESPN: Extremely Sparse Pruned Networks [50.436905934791035]
  We show that a simple iterative mask discovery method can achieve state-of-the-art compression of very deep networks. Our algorithm represents a hybrid approach between single shot network pruning methods and Lottery-Ticket type approaches.
  arXiv  Detail & Related papers  (2020-06-28T23:09:27Z)

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.