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Found 5 papers, 1 papers with code
Generalizability Under Sensor Failure: Tokenization + Transformers Enable More Robust Latent Spaces
A major goal in neuroscience is to discover neural data representations that generalize.
TOTEM: TOkenized Time Series EMbeddings for General Time Series Analysis
To this end, we explore the impact of discrete, learnt, time series data representations that enable generalist, cross-domain training.
Deep Neural Imputation: A Framework for Recovering Incomplete Brain Recordings
Neuroscientists and neuroengineers have long relied on multielectrode neural recordings to study the brain.
On the Benefits of Early Fusion in Multimodal Representation Learning
To facilitate the study of early multimodal fusion, we create a convolutional LSTM network architecture that simultaneously processes both audio and visual inputs, and allows us to select the layer at which audio and visual information combines.
Architecture Agnostic Neural Networks
Within this sparse, binary paradigm we sample many binary architectures to create families of architecture agnostic neural networks not trained via backpropagation.
