Detecting Information Relays in Deep Neural Networks

• URL: http://arxiv.org/abs/2301.00911v1
• Date: Tue, 3 Jan 2023 01:02:51 GMT
• Title: Detecting Information Relays in Deep Neural Networks
• Authors: Arend Hintze (Dalarna University) and Christoph Adami (Michigan State University)
• Abstract summary: We introduce a new information-theoretic concept that proves useful in understanding and analyzing a network's functional modularity. The relay information measures how much information groups of neurons that participate in a particular function (modules) relay from inputs to outputs. We show that the functionality of modules correlates with the amount of relay information they carry.
• Score: 0.0
• License: http://creativecommons.org/licenses/by-sa/4.0/
• Abstract: Deep-learning of artificial neural networks (ANNs) is creating highly functional tools that are, unfortunately, as hard to interpret as their natural counterparts. While it is possible to identify functional modules in natural brains using technologies such as fMRI, we do not have at our disposal similarly robust methods for artificial neural networks. Ideally, understanding which parts of an artificial neural network perform what function might help us to address a number of vexing problems in ANN research, such as catastrophic forgetting and overfitting. Furthermore, revealing a network's modularity could improve our trust in them by making these black boxes more transparent. Here we introduce a new information-theoretic concept that proves useful in understanding and analyzing a network's functional modularity: the relay information $I_R$. The relay information measures how much information groups of neurons that participate in a particular function (modules) relay from inputs to outputs. Combined with a greedy search algorithm, relay information can be used to {\em identify} computational modules in neural networks. We also show that the functionality of modules correlates with the amount of relay information they carry.

Related papers

• Modular Growth of Hierarchical Networks: Efficient, General, and Robust Curriculum Learning [0.0]
  We show that for a given classical, non-modular recurrent neural network (RNN), an equivalent modular network will perform better across multiple metrics. We demonstrate that the inductive bias introduced by the modular topology is strong enough for the network to perform well even when the connectivity within modules is fixed. Our findings suggest that gradual modular growth of RNNs could provide advantages for learning increasingly complex tasks on evolutionary timescales.
  arXiv  Detail & Related papers  (2024-06-10T13:44:07Z)
• Synergistic information supports modality integration and flexible learning in neural networks solving multiple tasks [107.8565143456161]
  We investigate the information processing strategies adopted by simple artificial neural networks performing a variety of cognitive tasks. Results show that synergy increases as neural networks learn multiple diverse tasks. randomly turning off neurons during training through dropout increases network redundancy, corresponding to an increase in robustness.
  arXiv  Detail & Related papers  (2022-10-06T15:36:27Z)
• A Spiking Neural Network based on Neural Manifold for Augmenting Intracortical Brain-Computer Interface Data [5.039813366558306]
  Brain-computer interfaces (BCIs) transform neural signals in the brain into in-structions to control external devices. With the advent of advanced machine learning methods, the capability of brain-computer interfaces has been enhanced like never before. Here, we use spiking neural networks (SNN) as data generators.
  arXiv  Detail & Related papers  (2022-03-26T15:32:31Z)
• Detecting Modularity in Deep Neural Networks [8.967870619902211]
  We consider the problem of assessing the modularity exhibited by a partitioning of a network's neurons. We propose two proxies for this: importance, which reflects how crucial sets of neurons are to network performance; and coherence, which reflects how consistently their neurons associate with features of the inputs. We show that these partitionings, even ones based only on weights, reveal groups of neurons that are important and coherent.
  arXiv  Detail & Related papers  (2021-10-13T20:33:30Z)
• 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)
• Optimal Approximation with Sparse Neural Networks and Applications [0.0]
  We use deep sparsely connected neural networks to measure the complexity of a function class in $L(mathbb Rd)$. We also introduce representation system - a countable collection of functions to guide neural networks. We then analyse the complexity of a class called $beta$ cartoon-like functions using rate-distortion theory and wedgelets construction.
  arXiv  Detail & Related papers  (2021-08-14T05:14:13Z)
• RE-MIMO: Recurrent and Permutation Equivariant Neural MIMO Detection [85.44877328116881]
  We present a novel neural network for symbol detection in wireless communication systems. It is motivated by several important considerations in wireless communication systems. We compare its performance against existing methods and the results show the ability of our network to efficiently handle a variable number of transmitters.
  arXiv  Detail & Related papers  (2020-06-30T22:43:01Z)
• Incremental Training of a Recurrent Neural Network Exploiting a Multi-Scale Dynamic Memory [79.42778415729475]
  We propose a novel incrementally trained recurrent architecture targeting explicitly multi-scale learning. We show how to extend the architecture of a simple RNN by separating its hidden state into different modules. We discuss a training algorithm where new modules are iteratively added to the model to learn progressively longer dependencies.
  arXiv  Detail & Related papers  (2020-06-29T08:35:49Z)
• Teaching Recurrent Neural Networks to Modify Chaotic Memories by Example [14.91507266777207]
  We show that a recurrent neural network can learn to modify its representation of complex information using only examples. We provide a mechanism for how these computations are learned, and demonstrate that a single network can simultaneously learn multiple computations.
  arXiv  Detail & Related papers  (2020-05-03T20:51:46Z)
• Neural Additive Models: Interpretable Machine Learning with Neural Nets [77.66871378302774]
  Deep neural networks (DNNs) are powerful black-box predictors that have achieved impressive performance on a wide variety of tasks. We propose Neural Additive Models (NAMs) which combine some of the expressivity of DNNs with the inherent intelligibility of generalized additive models. NAMs learn a linear combination of neural networks that each attend to a single input feature.
  arXiv  Detail & Related papers  (2020-04-29T01:28:32Z)
• Non-linear Neurons with Human-like Apical Dendrite Activations [81.18416067005538]
  We show that a standard neuron followed by our novel apical dendrite activation (ADA) can learn the XOR logical function with 100% accuracy. We conduct experiments on six benchmark data sets from computer vision, signal processing and natural language processing.
  arXiv  Detail & Related papers  (2020-02-02T21:09:39Z)

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.