Quantum-Cognitive Neural Networks: Assessing Confidence and Uncertainty with Human Decision-Making Simulations

• URL: http://arxiv.org/abs/2412.08010v1
• Date: Wed, 11 Dec 2024 01:34:21 GMT
• Title: Quantum-Cognitive Neural Networks: Assessing Confidence and Uncertainty with Human Decision-Making Simulations
• Authors: Milan Maksimovic, Ivan S. Maksymov,
• Abstract summary: We employ the recently proposed quantum-tunnelling neural networks (QT-NNs) to classify image datasets. Our findings suggest that the QT-NN model provides compelling evidence of its potential to replicate human-like decision-making.
• Score: 0.0
• License:
• Abstract: Modern machine learning (ML) systems excel in recognising and classifying images with remarkable accuracy. However, like many computer software systems, they can fail by generating confusing or erroneous outputs or by deferring to human operators to interpret the results and make final decisions. In this paper, we employ the recently proposed quantum-tunnelling neural networks (QT-NNs), inspired by human brain processes, alongside quantum cognition theory, to classify image datasets while emulating human perception and judgment. Our findings suggest that the QT-NN model provides compelling evidence of its potential to replicate human-like decision-making and outperform traditional ML algorithms.

Related papers

• Discovering Chunks in Neural Embeddings for Interpretability [53.80157905839065]
  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.
  arXiv  Detail & Related papers  (2025-02-03T20:30:46Z)
• 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)
• ClaudesLens: Uncertainty Quantification in Computer Vision Models [0.0]
  We show a possible method to quantify and evaluate the uncertainty of the output of different computer vision models based on Shannon entropy. We believe that Shannon entropy may eventually have a bigger role in the SOTA (State-of-the-art) methods to quantify uncertainty in artificial intelligence.
  arXiv  Detail & Related papers  (2024-06-18T18:58:54Z)
• 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)
• Bayes in the age of intelligent machines [11.613278345297399]
  We argue that Bayesian models of cognition and artificial neural networks lie at different levels of analysis and are complementary modeling approaches. We also argue that the same perspective can be applied to intelligent machines, where a Bayesian approach may be uniquely valuable.
  arXiv  Detail & Related papers  (2023-11-16T21:39:54Z)
• ShadowNet for Data-Centric Quantum System Learning [188.683909185536]
  We propose a data-centric learning paradigm combining the strength of neural-network protocols and classical shadows. Capitalizing on the generalization power of neural networks, this paradigm can be trained offline and excel at predicting previously unseen systems. We present the instantiation of our paradigm in quantum state tomography and direct fidelity estimation tasks and conduct numerical analysis up to 60 qubits.
  arXiv  Detail & Related papers  (2023-08-22T09:11:53Z)
• A Survey on Brain-Inspired Deep Learning via Predictive Coding [85.93245078403875]
  Predictive coding (PC) has shown promising performance in machine intelligence tasks. PC can model information processing in different brain areas, can be used in cognitive control and robotics.
  arXiv  Detail & Related papers  (2023-08-15T16:37:16Z)
• Toward stochastic neural computing [11.955322183964201]
  We propose a theory of neural computing in which streams of noisy inputs are transformed and processed through populations of spiking neurons. We demonstrate the application of our method to Intel's Loihi neuromorphic hardware.
  arXiv  Detail & Related papers  (2023-05-23T12:05:35Z)
• Problem-Dependent Power of Quantum Neural Networks on Multi-Class Classification [83.20479832949069]
  Quantum neural networks (QNNs) have become an important tool for understanding the physical world, but their advantages and limitations are not fully understood. Here we investigate the problem-dependent power of QCs on multi-class classification tasks. Our work sheds light on the problem-dependent power of QNNs and offers a practical tool for evaluating their potential merit.
  arXiv  Detail & Related papers  (2022-12-29T10:46:40Z)
• Quantum neural networks with deep residual learning [29.929891641757273]
  In this paper, a novel quantum neural network with deep residual learning (ResQNN) is proposed. Our ResQNN is able to learn an unknown unitary and get remarkable performance.
  arXiv  Detail & Related papers  (2020-12-14T18:11:07Z)
• Quantum Superposition Inspired Spiking Neural Network [4.5727987473456055]
  Despite advances in artificial intelligence models, neural networks still cannot achieve human performance. We propose a quantum superposition spiking neural network inspired by quantum mechanisms and phenomena in the brain. The QS-SNN incorporates quantum theory with brain-inspired spiking neural network models from a computational perspective, resulting in more robust performance compared with traditional ANN models.
  arXiv  Detail & Related papers  (2020-10-23T07:11:53Z)

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.