Search Results for author:
Found 11 papers, 7 papers with code
DISSECT: Disentangled Simultaneous Explanations via Concept Traversals
Explaining deep learning model inferences is a promising venue for scientific understanding, improving safety, uncovering hidden biases, evaluating fairness, and beyond, as argued by many scholars.
Personalized Federated Deep Learning for Pain Estimation From Face Images
A potential approach to tackling this is Federated Learning (FL), which enables multiple parties to collaboratively learn a shared prediction model by using parameters of locally trained models while keeping raw training data locally.
openXDATA: A Tool for Multi-Target Data Generation and Missing Label Completion
A common problem in machine learning is to deal with datasets with disjoint label spaces and missing labels.
Characterizing Sources of Uncertainty to Proxy Calibration and Disambiguate Annotator and Data Bias
Supporting model interpretability for complex phenomena where annotators can legitimately disagree, such as emotion recognition, is a challenging machine learning task.
Multi-modal Active Learning From Human Data: A Deep Reinforcement Learning Approach
Human behavior expression and experience are inherently multi-modal, and characterized by vast individual and contextual heterogeneity.
Meta-Weighted Gaussian Process Experts for Personalized Forecasting of AD Cognitive Changes
We introduce a novel personalized Gaussian Process Experts (pGPE) model for predicting per-subject ADAS-Cog13 cognitive scores -- a significant predictor of Alzheimer's Disease (AD) in the cognitive domain -- over the future 6, 12, 18, and 24 months.
Estimating Carotid Pulse and Breathing Rate from Near-infrared Video of the Neck
Objective: Non-contact physiological measurement is a growing research area that allows capturing vital signs such as heart rate (HR) and breathing rate (BR) comfortably and unobtrusively with remote devices.
Personalized Gaussian Processes for Forecasting of Alzheimer's Disease Assessment Scale-Cognition Sub-Scale (ADAS-Cog13)
In this paper, we introduce the use of a personalized Gaussian Process model (pGP) to predict per-patient changes in ADAS-Cog13 -- a significant predictor of Alzheimer's Disease (AD) in the cognitive domain -- using data from each patient's previous visits, and testing on future (held-out) data.
Personalized Gaussian Processes for Future Prediction of Alzheimer's Disease Progression
In this paper, we introduce the use of a personalized Gaussian Process model (pGP) to predict the key metrics of Alzheimer's Disease progression (MMSE, ADAS-Cog13, CDRSB and CS) based on each patient's previous visits.
Multimodal Autoencoder: A Deep Learning Approach to Filling In Missing Sensor Data and Enabling Better Mood Prediction
To accomplish forecasting of mood in real-world situations, affective computing systems need to collect and learn from multimodal data collected over weeks or months of daily use.
Advancements in Noncontact, Multiparameter Physiological Measurements Using a Webcam
We present a simple, low-cost method for measuring multiple physiological parameters using a basic webcam.
