1 code implementation • ICLR 2022 • Asma Ghandeharioun, Been Kim, Chun-Liang Li, Brendan Jou, Brian Eoff, Rosalind W. Picard
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.
1 code implementation • 12 Jan 2021 • Ognjen Rudovic, Nicolas Tobis, Sebastian Kaltwang, Björn Schuller, Daniel Rueckert, Jeffrey F. Cohn, Rosalind W. Picard
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.
1 code implementation • 27 Jul 2020 • Felix Weninger, Yue Zhang, Rosalind W. Picard
A common problem in machine learning is to deal with datasets with disjoint label spaces and missing labels.
1 code implementation • 20 Sep 2019 • Asma Ghandeharioun, Brian Eoff, Brendan Jou, Rosalind W. Picard
Supporting model interpretability for complex phenomena where annotators can legitimately disagree, such as emotion recognition, is a challenging machine learning task.
no code implementations • 7 Jun 2019 • Ognjen Rudovic, Meiru Zhang, Bjorn Schuller, Rosalind W. Picard
Human behavior expression and experience are inherently multi-modal, and characterized by vast individual and contextual heterogeneity.
no code implementations • 19 Apr 2019 • Ognjen Rudovic, Yuria Utsumi, Ricardo Guerrero, Kelly Peterson, Daniel Rueckert, Rosalind W. Picard
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.
no code implementations • 24 May 2018 • Weixuan Chen, Javier Hernandez, Rosalind W. Picard
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.
1 code implementation • 22 Feb 2018 • Yuria Utsumi, Ognjen Rudovic, Kelly Peterson, Ricardo Guerrero, Rosalind W. Picard
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.
1 code implementation • 1 Dec 2017 • Kelly Peterson, Ognjen Rudovic, Ricardo Guerrero, Rosalind W. Picard
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.
1 code implementation • 26 Oct 2017 • Natasha Jaques, Sara Taylor, Akane Sano, Rosalind W. Picard
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.
no code implementations • IEEE Transactions on Biomedical Engineering 2010 • Ming-Zher Poh, Daniel J. McDuff, Rosalind W. Picard
We present a simple, low-cost method for measuring multiple physiological parameters using a basic webcam.