Beyond supervised learning for pervasive healthcare
The integration of machine/deep learning and sensing technologies is transforming
healthcare and medical practice. However, inherent limitations in healthcare data, namely …
healthcare and medical practice. However, inherent limitations in healthcare data, namely …
Hungry hungry hippos: Towards language modeling with state space models
State space models (SSMs) have demonstrated state-of-the-art sequence modeling
performance in some modalities, but underperform attention in language modeling …
performance in some modalities, but underperform attention in language modeling …
Combining recurrent, convolutional, and continuous-time models with linear state space layers
Recurrent neural networks (RNNs), temporal convolutions, and neural differential equations
(NDEs) are popular families of deep learning models for time-series data, each with unique …
(NDEs) are popular families of deep learning models for time-series data, each with unique …
[HTML][HTML] Epileptic multi-seizure type classification using electroencephalogram signals from the Temple University Hospital Seizure Corpus: A review
Epilepsy is one of the most paramount neurological diseases, affecting about 1% of the
world's population. Seizure detection and classification are difficult tasks and are ongoing …
world's population. Seizure detection and classification are difficult tasks and are ongoing …
Hippo: Recurrent memory with optimal polynomial projections
A central problem in learning from sequential data is representing cumulative history in an
incremental fashion as more data is processed. We introduce a general framework (HiPPO) …
incremental fashion as more data is processed. We introduce a general framework (HiPPO) …
Domino: Discovering systematic errors with cross-modal embeddings
Machine learning models that achieve high overall accuracy often make systematic errors
on important subsets (or slices) of data. Identifying underperforming slices is particularly …
on important subsets (or slices) of data. Identifying underperforming slices is particularly …
WRENCH: A comprehensive benchmark for weak supervision
Recent Weak Supervision (WS) approaches have had widespread success in easing the
bottleneck of labeling training data for machine learning by synthesizing labels from multiple …
bottleneck of labeling training data for machine learning by synthesizing labels from multiple …
Seizure detection using wearable sensors and machine learning: Setting a benchmark
Objective Tracking seizures is crucial for epilepsy monitoring and treatment evaluation.
Current epilepsy care relies on caretaker seizure diaries, but clinical seizure monitoring may …
Current epilepsy care relies on caretaker seizure diaries, but clinical seizure monitoring may …
A deep learning framework for epileptic seizure detection based on neonatal EEG signals
A Gramacki, J Gramacki - Scientific reports, 2022 - nature.com
Electroencephalogram (EEG) is one of the main diagnostic tests for epilepsy. The detection
of epileptic activity is usually performed by a human expert and is based on finding specific …
of epileptic activity is usually performed by a human expert and is based on finding specific …
Self-supervised learning for anomalous channel detection in EEG graphs: Application to seizure analysis
TKK Ho, N Armanfard - Proceedings of the AAAI conference on artificial …, 2023 - ojs.aaai.org
Electroencephalogram (EEG) signals are effective tools towards seizure analysis where one
of the most important challenges is accurate detection of seizure events and brain regions in …
of the most important challenges is accurate detection of seizure events and brain regions in …