Adaptive, Unlabeled and Real-time Approximate-Learning Platform (AURA) for Personalized Epileptic Seizure Forecasting

Yang, Yikai; Truong, Nhan Duy; Eshraghian, Jason K.; Nikpour, Armin; Kavehei, Omid

doi:10.1101/2021.09.30.21264287

Cited by 2 publications

(3 citation statements)

References 84 publications

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“…The surrogate gradient is limited to the range of ∂ z/∂u ∈ [0, 1], which can lead to vanishing gradients when training deep networks. 3 With respect to Equation ( 5), as k → 0, the fast sigmoid surrogate function approaches a straight line with a constant gradient ∂ z/∂u → 1. That is to say, in the limit, the surrogate gradient converges to a STE.…”

Section: Discussionmentioning

confidence: 99%

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Navigating Local Minima in Quantized Spiking Neural Networks

Eshraghian¹,

Lammie²,

Azghadi³

et al. 2022

Preprint

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Spiking and Quantized Neural Networks (NNs) are becoming exceedingly important for hyper-efficient implementations of Deep Learning (DL) algorithms. However, these networks face challenges when trained using error backpropagation, due to the absence of gradient signals when applying hard thresholds. The broadly accepted trick to overcoming this is through the use of biased gradient estimators: surrogate gradients which approximate thresholding in Spiking Neural Networks (SNNs), and Straight-Through Estimators (STEs), which completely bypass thresholding in Quantized Neural Networks (QNNs). While noisy gradient feedback has enabled reasonable performance on simple supervised learning tasks, it is thought that such noise increases the difficulty of finding optima in loss landscapes, especially during the later stages of optimization. By periodically boosting the Learning Rate (LR) during training, we expect the network can navigate unexplored solution spaces that would otherwise be difficult to reach due to local minima, barriers, or flat surfaces. This paper presents a systematic evaluation of a cosineannealed LR schedule coupled with weight-independent adaptive moment estimation as applied to Quantized SNNs (QSNNs). We provide a rigorous empirical evaluation of this technique on high precision and 4-bit quantized SNNs across three datasets, demonstrating (close to) state-of-the-art performance on the more complex datasets. Our source code is available at this link: https://github.com/jeshraghian/QSNNs.

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Section: Discussionmentioning

confidence: 99%

“…L OW-POWER implementations of NNs are essential for operation on portable, edge devices [1]- [3]. Most resourceconstrained algorithmic options either reduce memory usage or memory access frequency.…”

Section: Introductionmentioning

confidence: 99%

Navigating Local Minima in Quantized Spiking Neural Networks

Eshraghian¹,

Lammie²,

Azghadi³

et al. 2022

Preprint

Self Cite

View full text Add to dashboard Cite

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“…In reality, it is relatively easy to obtain unlabeled EEG data, but obtaining expert manually labeled data is very difficult and expensive. Unlabeled EEG data may be overlooked or underutilized in many studies, and semi-supervised learning makes these data a valuable resource to help improve their utilization [40]. The dataset used in this paper is exemplary segmented EEG time series recordings of ten epilepsy patients collected from the Neurology and Sleep Center, Hauz Khas, New Delhi [34].…”

Section: Eeg Datasetmentioning

confidence: 99%

Small Sample Time Series Classification Based on Data Augmentation and Semi-supervised Learning

Liu,

Yao,

Wang

et al. 2024

ITC

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Realistic scenarios produce labeled data and unlabeled data, however, there are significant challenges in labeling time series data. It is imperative to effectively integrate the relationship between labeled and unlabeled data within semi-supervised classification model. This paper presents a novel semi-supervised classification method, namely Data Augmentation-Fast Shapelet Semi-Supervised Classification, which employs a data augmentation module to enhance the diversity of data and improve the generalization ability of the model, as well as a feature fusion module to enhance the semi-supervised network. A conditional generative adversarial network is used to synthesize excellent labeled time series samples to enhance the homogeneous data in the sample space, the fast shapelets method is used to quickly extract the important shape feature vectors in the time series, self-supervised and supervised learning are combined to fully learn the unlabeled and labeled data of the time series dataset. Thejoint loss function combines the loss functions of the two networks to optimize multiple objectives. Reinforcement learning is used to determine the weight coefficients of the joint loss function, at the same time, the reward function is modified to bias the supervisory loss, which improves the classification performance of the model under limited labeled data, and the model can also better achieve the semi-supervised classification task. The proposed method is validated on the UCR benchmark dataset, Electrocardiogram dataset, and Electroencephalogram dataset, the results show that the semi-supervised classification method can perform a more accurate semi-supervised classification of the time series, with an accuracy better than the comparison methods. Meanwhile, we use the plant electrical signal dataset obtained from actual measurements for testing, the visualizationanalysis can clearly show the model role in the semi-supervised classification task, and the experimental results fully demonstrate the effectiveness and applicability of the proposed method.

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Adaptive, Unlabeled and Real-time Approximate-Learning Platform (AURA) for Personalized Epileptic Seizure Forecasting

Cited by 2 publications

References 84 publications

Navigating Local Minima in Quantized Spiking Neural Networks

Navigating Local Minima in Quantized Spiking Neural Networks

Small Sample Time Series Classification Based on Data Augmentation and Semi-supervised Learning

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