Discovering Chunks in Neural Embeddings for Interpretability

• URL: http://arxiv.org/abs/2502.01803v1
• Date: Mon, 03 Feb 2025 20:30:46 GMT
• Title: Discovering Chunks in Neural Embeddings for Interpretability
• Authors: Shuchen Wu, Stephan Alaniz, Eric Schulz, Zeynep Akata,
• Abstract summary: We propose leveraging the principle of chunking to interpret artificial neural population activities. We first demonstrate this concept in recurrent neural networks (RNNs) trained on artificial sequences with imposed regularities. We identify similar recurring embedding states corresponding to concepts in the input, with perturbations to these states activating or inhibiting the associated concepts.
• Score: 53.80157905839065
• License:
• Abstract: Understanding neural networks is challenging due to their high-dimensional, interacting components. Inspired by human cognition, which processes complex sensory data by chunking it into recurring entities, we propose leveraging this principle to interpret artificial neural population activities. Biological and artificial intelligence share the challenge of learning from structured, naturalistic data, and we hypothesize that the cognitive mechanism of chunking can provide insights into artificial systems. We first demonstrate this concept in recurrent neural networks (RNNs) trained on artificial sequences with imposed regularities, observing that their hidden states reflect these patterns, which can be extracted as a dictionary of chunks that influence network responses. Extending this to large language models (LLMs) like LLaMA, we identify similar recurring embedding states corresponding to concepts in the input, with perturbations to these states activating or inhibiting the associated concepts. By exploring methods to extract dictionaries of identifiable chunks across neural embeddings of varying complexity, our findings introduce a new framework for interpreting neural networks, framing their population activity as structured reflections of the data they process.

Related papers

• 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)
• Contrastive Learning in Memristor-based Neuromorphic Systems [55.11642177631929]
  Spiking neural networks have become an important family of neuron-based models that sidestep many of the key limitations facing modern-day backpropagation-trained deep networks. In this work, we design and investigate a proof-of-concept instantiation of contrastive-signal-dependent plasticity (CSDP), a neuromorphic form of forward-forward-based, backpropagation-free learning.
  arXiv  Detail & Related papers  (2024-09-17T04:48:45Z)
• Spiking representation learning for associative memories [0.0]
  We introduce a novel artificial spiking neural network (SNN) that performs unsupervised representation learning and associative memory operations. The architecture of our model derives from the neocortical columnar organization and combines feedforward projections for learning hidden representations and recurrent projections for forming associative memories.
  arXiv  Detail & Related papers  (2024-06-05T08:30:11Z)
• 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)
• Contrastive-Signal-Dependent Plasticity: Self-Supervised Learning in Spiking Neural Circuits [61.94533459151743]
  This work addresses the challenge of designing neurobiologically-motivated schemes for adjusting the synapses of spiking networks. Our experimental simulations demonstrate a consistent advantage over other biologically-plausible approaches when training recurrent spiking networks.
  arXiv  Detail & Related papers  (2023-03-30T02:40:28Z)
• Searching for the Essence of Adversarial Perturbations [73.96215665913797]
  We show that adversarial perturbations contain human-recognizable information, which is the key conspirator responsible for a neural network's erroneous prediction. This concept of human-recognizable information allows us to explain key features related to adversarial perturbations.
  arXiv  Detail & Related papers  (2022-05-30T18:04:57Z)
• Data-driven emergence of convolutional structure in neural networks [83.4920717252233]
  We show how fully-connected neural networks solving a discrimination task can learn a convolutional structure directly from their inputs. By carefully designing data models, we show that the emergence of this pattern is triggered by the non-Gaussian, higher-order local structure of the inputs.
  arXiv  Detail & Related papers  (2022-02-01T17:11:13Z)
• Ensembling complex network 'perspectives' for mild cognitive impairment detection with artificial neural networks [5.194561180498554]
  We propose a novel method for mild cognitive impairment detection based on jointly exploiting the complex network and the neural network paradigm. In particular, the method is based on ensembling different brain structural "perspectives" with artificial neural networks.
  arXiv  Detail & Related papers  (2021-01-26T08:38:11Z)
• Complexity for deep neural networks and other characteristics of deep feature representations [0.0]
  We define a notion of complexity, which quantifies the nonlinearity of the computation of a neural network. We investigate these observables both for trained networks as well as explore their dynamics during training.
  arXiv  Detail & Related papers  (2020-06-08T17:59:30Z)

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.