CQural: A Novel CNN based Hybrid Architecture for Quantum Continual Machine Learning

• URL: http://arxiv.org/abs/2305.09738v1
• Date: Tue, 16 May 2023 18:19:12 GMT
• Title: CQural: A Novel CNN based Hybrid Architecture for Quantum Continual Machine Learning
• Authors: Sanyam Jain
• Abstract summary: We show that it is possible to circumvent catastrophic forgetting in continual learning with novel hybrid classical-quantum neural networks. We also claim that if the model is trained with these explanations, it tends to give better performance and learn specific features that are far from the decision boundary.
• Score: 0.0
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Training machine learning models in an incremental fashion is not only important but also an efficient way to achieve artificial general intelligence. The ability that humans possess of continuous or lifelong learning helps them to not forget previously learned tasks. However, current neural network models are prone to catastrophic forgetting when it comes to continual learning. Many researchers have come up with several techniques in order to reduce the effect of forgetting from neural networks, however, all techniques are studied classically with a very less focus on changing the machine learning model architecture. In this research paper, we show that it is not only possible to circumvent catastrophic forgetting in continual learning with novel hybrid classical-quantum neural networks, but also explains what features are most important to learn for classification. In addition, we also claim that if the model is trained with these explanations, it tends to give better performance and learn specific features that are far from the decision boundary. Finally, we present the experimental results to show comparisons between classical and classical-quantum hybrid architectures on benchmark MNIST and CIFAR-10 datasets. After successful runs of learning procedure, we found hybrid neural network outperforms classical one in terms of remembering the right evidences of the class-specific features.

Related papers

• Towards Scalable and Versatile Weight Space Learning [51.78426981947659]
  This paper introduces the SANE approach to weight-space learning. Our method extends the idea of hyper-representations towards sequential processing of subsets of neural network weights.
  arXiv  Detail & Related papers  (2024-06-14T13:12:07Z)
• Bridging Classical and Quantum Machine Learning: Knowledge Transfer From Classical to Quantum Neural Networks Using Knowledge Distillation [0.0]
  This paper introduces a new method to transfer knowledge from classical to quantum neural networks using knowledge distillation. We adapt classical convolutional neural network (CNN) architectures like LeNet and AlexNet to serve as teacher networks. Quantum models achieve an average accuracy improvement of 0.80% on the MNIST dataset and 5.40% on the more complex Fashion MNIST dataset.
  arXiv  Detail & Related papers  (2023-11-23T05:06:43Z)
• Neural Networks for Programming Quantum Annealers [6.531395267592592]
  Quantum machine learning has the potential to enable advances in artificial intelligence, such as solving problems intractable on classical computers. In this study, we consider a similar but not quite the same case, where a classical fully-fledged neural network is connected with a small quantum annealer. We simulate this system to learn several common datasets, including those for image and sound recognition.
  arXiv  Detail & Related papers  (2023-08-13T16:43:07Z)
• Meta-Learning in Spiking Neural Networks with Reward-Modulated STDP [2.179313476241343]
  We propose a bio-plausible meta-learning model inspired by the hippocampus and the prefrontal cortex. Our new model can easily be applied to spike-based neuromorphic devices and enables fast learning in neuromorphic hardware.
  arXiv  Detail & Related papers  (2023-06-07T13:08:46Z)
• How neural networks learn to classify chaotic time series [77.34726150561087]
  We study the inner workings of neural networks trained to classify regular-versus-chaotic time series. We find that the relation between input periodicity and activation periodicity is key for the performance of LKCNN models.
  arXiv  Detail & Related papers  (2023-06-04T08:53:27Z)
• Efficient Neural Architecture Search with Performance Prediction [0.0]
  We use a neural architecture search to find the best network architecture for the task at hand. Existing NAS algorithms generally evaluate the fitness of a new architecture by fully training from scratch. An end-to-end offline performance predictor is proposed to accelerate the evaluation of sampled architectures.
  arXiv  Detail & Related papers  (2021-08-04T05:44:16Z)
• Incremental Deep Neural Network Learning using Classification Confidence Thresholding [4.061135251278187]
  Most modern neural networks for classification fail to take into account the concept of the unknown. This paper proposes the Classification Confidence Threshold approach to prime neural networks for incremental learning.
  arXiv  Detail & Related papers  (2021-06-21T22:46:28Z)
• Gone Fishing: Neural Active Learning with Fisher Embeddings [55.08537975896764]
  There is an increasing need for active learning algorithms that are compatible with deep neural networks. This article introduces BAIT, a practical representation of tractable, and high-performing active learning algorithm for neural networks.
  arXiv  Detail & Related papers  (2021-06-17T17:26:31Z)
• Reservoir Memory Machines as Neural Computers [70.5993855765376]
  Differentiable neural computers extend artificial neural networks with an explicit memory without interference. We achieve some of the computational capabilities of differentiable neural computers with a model that can be trained very efficiently.
  arXiv  Detail & Related papers  (2020-09-14T12:01:30Z)
• 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)
• The large learning rate phase of deep learning: the catapult mechanism [50.23041928811575]
  We present a class of neural networks with solvable training dynamics. We find good agreement between our model's predictions and training dynamics in realistic deep learning settings. We believe our results shed light on characteristics of models trained at different learning rates.
  arXiv  Detail & Related papers  (2020-03-04T17:52:48Z)

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.