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Found 5 papers, 4 papers with code
Towards Time Series Reasoning with LLMs
We show that our model learns a latent representation that reflects specific time-series features (e. g. slope, frequency), as well as outperforming GPT-4o on a set of zero-shot reasoning tasks on a variety of domains.
Temporally Multi-Scale Sparse Self-Attention for Physical Activity Data Imputation
Wearable sensors enable health researchers to continuously collect data pertaining to the physiological state of individuals in real-world settings.
REBAR: Retrieval-Based Reconstruction for Time-series Contrastive Learning
The success of self-supervised contrastive learning hinges on identifying positive data pairs, such that when they are pushed together in embedding space, the space encodes useful information for subsequent downstream tasks.
PulseImpute: A Novel Benchmark Task for Pulsative Physiological Signal Imputation
The promise of Mobile Health (mHealth) is the ability to use wearable sensors to monitor participant physiology at high frequencies during daily life to enable temporally-precise health interventions.
Efficient Learning and Decoding of the Continuous-Time Hidden Markov Model for Disease Progression Modeling
We solve the first challenge by reformulating the estimation problem as an equivalent discrete time-inhomogeneous hidden Markov model.
