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Found 9 papers, 7 papers with code
Adversarially Robust Spiking Neural Networks Through Conversion
Spiking neural networks (SNNs) provide an energy-efficient alternative to a variety of artificial neural network (ANN) based AI applications.
TS-MoCo: Time-Series Momentum Contrast for Self-Supervised Physiological Representation Learning
Limited availability of labeled physiological data often prohibits the use of powerful supervised deep learning models in the biomedical machine intelligence domain.
Restoring Vision in Adverse Weather Conditions with Patch-Based Denoising Diffusion Models
Image restoration under adverse weather conditions has been of significant interest for various computer vision applications.
EEG2Vec: Learning Affective EEG Representations via Variational Autoencoders
There is a growing need for sparse representational formats of human affective states that can be utilized in scenarios with limited computational memory resources.
Memory-enriched computation and learning in spiking neural networks through Hebbian plasticity
Memory is a key component of biological neural systems that enables the retention of information over a huge range of temporal scales, ranging from hundreds of milliseconds up to years.
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Exploiting Multiple EEG Data Domains with Adversarial Learning
Electroencephalography (EEG) is shown to be a valuable data source for evaluating subjects' mental states.
Domain-Invariant Representation Learning from EEG with Private Encoders
Deep learning based electroencephalography (EEG) signal processing methods are known to suffer from poor test-time generalization due to the changes in data distribution.
Improving Robustness Against Stealthy Weight Bit-Flip Attacks by Output Code Matching
Experimental benchmark evaluations show that output code matching is superior to existing regularized weight quantization based defenses, and an effective defense against stealthy weight bit-flip attacks.
Causal interpretation rules for encoding and decoding models in neuroimaging
Causal terminology is often introduced in the interpretation of encoding and decoding models trained on neuroimaging data.
