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[HTML][HTML] An overview of variational autoencoders for source separation, finance, and bio-signal applications
Autoencoders are a self-supervised learning system where, during training, the output is an
approximation of the input. Typically, autoencoders have three parts: Encoder (which …
approximation of the input. Typically, autoencoders have three parts: Encoder (which …
Neural decoding for intracortical brain–computer interfaces
Brain–computer interfaces have revolutionized the field of neuroscience by providing a
solution for paralyzed patients to control external devices and improve the quality of daily …
solution for paralyzed patients to control external devices and improve the quality of daily …
A high-performance speech neuroprosthesis
Speech brain–computer interfaces (BCIs) have the potential to restore rapid communication
to people with paralysis by decoding neural activity evoked by attempted speech into text, or …
to people with paralysis by decoding neural activity evoked by attempted speech into text, or …
Long-term stability of cortical population dynamics underlying consistent behavior
Animals readily execute learned behaviors in a consistent manner over long periods of time,
and yet no equally stable neural correlate has been demonstrated. How does the cortex …
and yet no equally stable neural correlate has been demonstrated. How does the cortex …
Understanding self-training for gradual domain adaptation
Abstract Machine learning systems must adapt to data distributions that evolve over time, in
applications ranging from sensor networks and self-driving car perception modules to brain …
applications ranging from sensor networks and self-driving car perception modules to brain …
A unified, scalable framework for neural population decoding
Our ability to use deep learning approaches to decipher neural activity would likely benefit
from greater scale, in terms of both the model size and the datasets. However, the …
from greater scale, in terms of both the model size and the datasets. However, the …
Machine learning for neural decoding
Despite rapid advances in machine learning tools, the majority of neural decoding
approaches still use traditional methods. Modern machine learning tools, which are versatile …
approaches still use traditional methods. Modern machine learning tools, which are versatile …
Adversarial self-training improves robustness and generalization for gradual domain adaptation
Abstract Gradual Domain Adaptation (GDA), in which the learner is provided with additional
intermediate domains, has been theoretically and empirically studied in many contexts …
intermediate domains, has been theoretically and empirically studied in many contexts …
Neural Latents Benchmark'21: Evaluating latent variable models of neural population activity
Advances in neural recording present increasing opportunities to study neural activity in
unprecedented detail. Latent variable models (LVMs) are promising tools for analyzing this …
unprecedented detail. Latent variable models (LVMs) are promising tools for analyzing this …
Subject-aware contrastive learning for biosignals
Datasets for biosignals, such as electroencephalogram (EEG) and electrocardiogram (ECG),
often have noisy labels and have limited number of subjects (< 100). To handle these …
often have noisy labels and have limited number of subjects (< 100). To handle these …