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Found 8 papers, 1 papers with code
Understanding the Functional Roles of Modelling Components in Spiking Neural Networks
By designing and evaluating several variants of the classic model, we systematically investigate the functional roles of key modelling components, leakage, reset, and recurrence, in leaky integrate-and-fire (LIF) based SNNs.
Enhancing Graph Representation Learning with Attention-Driven Spiking Neural Networks
Graph representation learning has become a crucial task in machine learning and data mining due to its potential for modeling complex structures such as social networks, chemical compounds, and biological systems.
Exploiting Spiking Dynamics with Spatial-temporal Feature Normalization in Graph Learning
Biological spiking neurons with intrinsic dynamics underlie the powerful representation and learning capabilities of the brain for processing multimodal information in complex environments.
Bigeminal Priors Variational auto-encoder
The likelihood-based generative models have been reported to be highly robust to the out-of-distribution (OOD) inputs and can be a detector by assuming that the model assigns higher likelihoods to the samples from the in-distribution (ID) dataset than an OOD dataset.
Detecting Out-of-distribution Samples via Variational Auto-encoder with Reliable Uncertainty Estimation
To address this problem, a reliable uncertainty estimation is considered to be critical for in-depth understanding of OOD inputs.
Brain-inspired global-local learning incorporated with neuromorphic computing
We demonstrate the advantages of this model in multiple different tasks, including few-shot learning, continual learning, and fault-tolerance learning in neuromorphic vision sensors.
Adversarial symmetric GANs: bridging adversarial samples and adversarial networks
Generative adversarial networks have achieved remarkable performance on various tasks but suffer from training instability.
BRIDGING ADVERSARIAL SAMPLES AND ADVERSARIAL NETWORKS
Generative adversarial networks have achieved remarkable performance on various tasks but suffer from sensitivity to hyper-parameters, training instability, and mode collapse.
