17 citations · 32 across the 7 of their papers we have counts for
8 papers
iMINDBench: iEEG Multi-Institution Neural Decoding Benchmark
Geeling Chau, Saba Hashemi, Yonghyeon Gwon +9
Intracranial electroencephalography (iEEG) is widely used to record electrical activity directly from electrodes inside the human brain, making it an attractive modality for neural…
Wind-Informed Rapid Flight-Planning in Complex Urban Topologies via Machine Learning and Experimental Validation
Peter I. Renn, Alejandro A. Stefan-Zavala, Julian Humml +8
Advanced air mobility operations hold the potential to enhance and expand regional transportation of both people and goods in populated areas. However, hazardous flight conditions…
Population Transformer: Learning Population-level Representations of Neural Activity
Geeling Chau, Christopher Wang, Sabera Talukder +5
We present a self-supervised framework that learns population-level codes for arbitrary ensembles of neural recordings at scale. We address key challenges in scaling models with ne…
Generalizability Under Sensor Failure: Tokenization + Transformers Enable More Robust Latent Spaces
Geeling Chau, Yujin An, Ahamed Raffey Iqbal +3
A major goal in neuroscience is to discover neural data representations that generalize. This goal is challenged by variability along recording sessions (e.g. environment), subject…
TOTEM: TOkenized Time Series EMbeddings for General Time Series Analysis
Sabera Talukder, Yisong Yue, Georgia Gkioxari
This work studies the problem of time series analysis with generalist (or foundation) models, which are models trained across many data domains. Drawing inspiration from the widesp…
Deep Neural Imputation: A Framework for Recovering Incomplete Brain Recordings
Sabera Talukder, Jennifer J. Sun, Matthew Leonard +2
Neuroscientists and neuroengineers have long relied on multielectrode neural recordings to study the brain. However, in a typical experiment, many factors corrupt neural recordings…
Architecture Agnostic Neural Networks
Sabera Talukder, Guruprasad Raghavan, Yisong Yue
In this paper, we explore an alternate method for synthesizing neural network architectures, inspired by the brain's stochastic synaptic pruning. During a person's lifetime, numero…
On the Benefits of Early Fusion in Multimodal Representation Learning
George Barnum, Sabera Talukder, Yisong Yue
Intelligently reasoning about the world often requires integrating data from multiple modalities, as any individual modality may contain unreliable or incomplete information. Prior…