Neuron-level Interpretation of Deep NLP Models: A Survey

• URL: http://arxiv.org/abs/2108.13138v1
• Date: Mon, 30 Aug 2021 11:54:21 GMT
• Title: Neuron-level Interpretation of Deep NLP Models: A Survey
• Authors: Hassan Sajjad and Nadir Durrani and Fahim Dalvi
• Abstract summary: A plethora of research has been carried out to analyze and understand components of the deep neural network models. Recent work has concentrated on interpretability at a more granular level, analyzing neurons and groups of neurons in large models.
• Score: 22.035813865470956
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: The proliferation of deep neural networks in various domains has seen an increased need for interpretability of these methods. A plethora of research has been carried out to analyze and understand components of the deep neural network models. Preliminary work done along these lines and papers that surveyed such, were focused on a more high-level representation analysis. However, a recent branch of work has concentrated on interpretability at a more granular level, analyzing neurons and groups of neurons in these large models. In this paper, we survey work done on fine-grained neuron analysis including: i) methods developed to discover and understand neurons in a network, ii) their limitations and evaluation, iii) major findings including cross architectural comparison that such analyses unravel and iv) direct applications of neuron analysis such as model behavior control and domain adaptation along with potential directions for future work.

Related papers

• Retinal Vessel Segmentation via Neuron Programming [17.609169389489633]
  This paper introduces a novel approach to neural network design, termed neuron programming'', to enhance a network's representation ability at the neuronal level. Comprehensive experiments validate that neuron programming can achieve competitive performance in retinal blood segmentation.
  arXiv  Detail & Related papers  (2024-11-17T16:03:30Z)
• Adapting the Biological SSVEP Response to Artificial Neural Networks [5.4712259563296755]
  This paper introduces a novel approach to neuron significance assessment inspired by frequency tagging, a technique from neuroscience. Experiments conducted with a convolutional neural network for image classification reveal notable harmonics and intermodulations in neuron-specific responses under part-based frequency tagging. The proposed method holds promise for applications in network pruning, and model interpretability, contributing to the advancement of explainable artificial intelligence.
  arXiv  Detail & Related papers  (2024-11-15T10:02:48Z)
• Statistical tuning of artificial neural network [0.0]
  This study introduces methods to enhance the understanding of neural networks, focusing specifically on models with a single hidden layer. We propose statistical tests to assess the significance of input neurons and introduce algorithms for dimensionality reduction. This research advances the field of Explainable Artificial Intelligence by presenting robust statistical frameworks for interpreting neural networks.
  arXiv  Detail & Related papers  (2024-09-24T19:47:03Z)
• Graph Neural Networks for Brain Graph Learning: A Survey [53.74244221027981]
  Graph neural networks (GNNs) have demonstrated a significant advantage in mining graph-structured data. GNNs to learn brain graph representations for brain disorder analysis has recently gained increasing attention. In this paper, we aim to bridge this gap by reviewing brain graph learning works that utilize GNNs.
  arXiv  Detail & Related papers  (2024-06-01T02:47:39Z)
• Probing Biological and Artificial Neural Networks with Task-dependent Neural Manifolds [12.037840490243603]
  We investigate the internal mechanisms of neural networks through the lens of neural population geometry. We quantitatively characterize how different learning objectives lead to differences in the organizational strategies of these models. These analyses present a strong direction for bridging mechanistic and normative theories in neural networks through neural population geometry.
  arXiv  Detail & Related papers  (2023-12-21T20:40:51Z)
• Brain-Inspired Machine Intelligence: A Survey of Neurobiologically-Plausible Credit Assignment [65.268245109828]
  We examine algorithms for conducting credit assignment in artificial neural networks that are inspired or motivated by neurobiology. We organize the ever-growing set of brain-inspired learning schemes into six general families and consider these in the context of backpropagation of errors. The results of this review are meant to encourage future developments in neuro-mimetic systems and their constituent learning processes.
  arXiv  Detail & Related papers  (2023-12-01T05:20:57Z)
• Automated Natural Language Explanation of Deep Visual Neurons with Large Models [43.178568768100305]
  This paper proposes a novel post-hoc framework for generating semantic explanations of neurons with large foundation models. Our framework is designed to be compatible with various model architectures and datasets, automated and scalable neuron interpretation.
  arXiv  Detail & Related papers  (2023-10-16T17:04:51Z)
• Extrapolation and Spectral Bias of Neural Nets with Hadamard Product: a Polynomial Net Study [55.12108376616355]
  The study on NTK has been devoted to typical neural network architectures, but is incomplete for neural networks with Hadamard products (NNs-Hp) In this work, we derive the finite-width-K formulation for a special class of NNs-Hp, i.e., neural networks. We prove their equivalence to the kernel regression predictor with the associated NTK, which expands the application scope of NTK.
  arXiv  Detail & Related papers  (2022-09-16T06:36:06Z)
• Dynamic Neural Diversification: Path to Computationally Sustainable Neural Networks [68.8204255655161]
  Small neural networks with a constrained number of trainable parameters, can be suitable resource-efficient candidates for many simple tasks. We explore the diversity of the neurons within the hidden layer during the learning process. We analyze how the diversity of the neurons affects predictions of the model.
  arXiv  Detail & Related papers  (2021-09-20T15:12:16Z)
• Rethinking Generalization of Neural Models: A Named Entity Recognition Case Study [81.11161697133095]
  We take the NER task as a testbed to analyze the generalization behavior of existing models from different perspectives. Experiments with in-depth analyses diagnose the bottleneck of existing neural NER models. As a by-product of this paper, we have open-sourced a project that involves a comprehensive summary of recent NER papers.
  arXiv  Detail & Related papers  (2020-01-12T04:33:53Z)

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.