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Found 10 papers, 2 papers with code
On consequences of finetuning on data with highly discriminative features
In the era of transfer learning, training neural networks from scratch is becoming obsolete.
Classification and Self-Supervised Regression of Arrhythmic ECG Signals Using Convolutional Neural Networks
Machine learning tasks can be divided into regression and classification.
Reinforcement learning with experience replay and adaptation of action dispersion
To that end, the dispersion should be tuned to assure a sufficiently high probability (densities) of the actions in the replay buffer and the modes of the distributions that generated them, yet this dispersion should not be higher.
Logarithmic Continual Learning
Our approach leverages allocation of past data in a~set of generative models such that most of them do not require retraining after a~task.
On robustness of generative representations against catastrophic forgetting
Catastrophic forgetting of previously learned knowledge while learning new tasks is a widely observed limitation of contemporary neural networks.
Multiband VAE: Latent Space Alignment for Knowledge Consolidation in Continual Learning
We propose a new method for unsupervised generative continual learning through realignment of Variational Autoencoder's latent space.
Reinforcement learning for optimization of variational quantum circuit architectures
The study of Variational Quantum Eigensolvers (VQEs) has been in the spotlight in recent times as they may lead to real-world applications of near-term quantum devices.
BinPlay: A Binary Latent Autoencoder for Generative Replay Continual Learning
We introduce a binary latent space autoencoder architecture to rehearse training samples for the continual learning of neural networks.
Reducing catastrophic forgetting with learning on synthetic data
Our experimental results on Split-MNIST dataset show that training a model on such synthetic data in sequence does not result in catastrophic forgetting.
Effective training of deep convolutional neural networks for hyperspectral image classification through artificial labeling
This approach can be applied to many of the hyperspectral classification problems.
General Classification
Hyperspectral Image Classification
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