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- Quanying LiuSuggested from co-authorship
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Works16 from public data
- Combining DC-GAN with ResNet for blood cell image classification145
A new blood cell image classification framework which is based on a deep convolutional generative adversarial network (DC-GAN) and a residual neural network (ResNet) and introduced a new loss function which is improved the discriminative power of the deeply learned features.
- Machine learning applications on neuroimaging for diagnosis and prognosis of epilepsy: A review79
The application of machine learning on epilepsy neuroimaging, such as segmentation, localization, and lateralization tasks, as well as tasks directly related to diagnosis and prognosis are looked into in detail.
- Detecting out-of-distribution samples via variational auto-encoder with reliable uncertainty estimation69
An improved noise contrastive prior (INCP) to be able to integrate into the encoder of VAEs, called INCPVAE, which obtains reliable uncertainty estimation for OOD inputs and solves the OOD problem in VAE models.
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- Brain-inspired continual pre-trained learner via silent synaptic consolidation9
The Artsy is introduced, inspired by the activation mechanisms of silent synapses via spike-timing-dependent plasticity observed in mature brains, to enhance the continual learning capabilities of pre-trained models and offers a promising avenue for simulating biological synaptic mechanisms, potentially advancing the understanding of neural plasticity in both artificial and biological systems.
- Efficient ANN-SNN Conversion with Error Compensation Learning8
This paper introduces a learnable threshold clipping function, dual-threshold neurons, and an optimized membrane potential initialization strategy to mitigate the conversion error, and proposes a novel ANN-to-SNN conversion framework based on error compensation learning.
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Publication data from OpenAlex; citation counts are the higher of OpenAlex and Semantic Scholar, last synced 2026-10-11. One-sentence summaries under some papers are written by Semantic Scholar’s model. Citation counts may be lower than on Google Scholar, which indexes more sources.
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