Search Results for author:
Found 22 papers, 3 papers with code
Device-Directed Speech Detection: Regularization via Distillation for Weakly-Supervised Models
We also show that the ensemble of the LatticeRNN and acoustic-distilled models brings further accuracy improvement of 20%.
Streaming on-device detection of device directed speech from voice and touch-based invocation
When interacting with smart devices such as mobile phones or wearables, the user typically invokes a virtual assistant (VA) by saying a keyword or by pressing a button on the device.
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.
Fast and Effective Adaptation of Facial Action Unit Detection Deep Model
In this paper, we propose a deep learning approach for facial AU detection that can easily and in a fast manner adapt to a new AU or target subject by leveraging only a few labeled samples from the new task (either an AU or subject).
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.
Unsupervised Multi-Target Domain Adaptation: An Information Theoretic Approach
Unsupervised domain adaptation (uDA) models focus on pairwise adaptation settings where there is a single, labeled, source and a single target domain.
Multi-Instance Dynamic Ordinal Random Fields for Weakly-supervised Facial Behavior Analysis
In this framework, we treat instance-labels as temporally-dependent latent variables in an Undirected Graphical Model.
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 Machine Learning for Robot Perception of Affect and Engagement in Autism Therapy
To tackle the heterogeneity in behavioral cues of children with autism, we use the latest advances in deep learning to formulate a personalized machine learning (ML) framework for automatic perception of the childrens affective states and engagement during robot-assisted autism therapy.
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.
Physiological and behavioral profiling for nociceptive pain estimation using personalized multitask learning
Pain is a subjective experience commonly measured through patient's self report.
Unsupervised Domain Adaptation with Copula Models
We study the task of unsupervised domain adaptation, where no labeled data from the target domain is provided during training time.
Personalized Automatic Estimation of Self-reported Pain Intensity from Facial Expressions
To the best of our knowledge, this is the first approach to automatically estimate VAS from face images.
DeepCoder: Semi-parametric Variational Autoencoders for Automatic Facial Action Coding
Potentially, this makes VAEs a suitable approach for learning facial features for AU intensity estimation.
Multi-instance Dynamic Ordinal Random Fields for Weakly-Supervised Pain Intensity Estimation
In this paper, we address the Multi-Instance-Learning (MIL) problem when bag labels are naturally represented as ordinal variables (Multi--Instance--Ordinal Regression).
Variational Gaussian Process Auto-Encoder for Ordinal Prediction of Facial Action Units
In particular, we introduce GP encoders to project multiple observed features onto a latent space, while GP decoders are responsible for reconstructing the original features.
Copula Ordinal Regression for Joint Estimation of Facial Action Unit Intensity
Joint modeling of the intensity of facial action units (AUs) from face images is challenging due to the large number of AUs (30+) and their intensity levels (6).
Gaussian Process Domain Experts for Model Adaptation in Facial Behavior Analysis
The adaptation of the classifier is facilitated in probabilistic fashion by conditioning the target expert on multiple source experts.
Multi-Conditional Latent Variable Model for Joint Facial Action Unit Detection
We propose a novel multi-conditional latent variable model for simultaneous facial feature fusion and detection of facial action units.
Variable-state Latent Conditional Random Fields for Facial Expression Recognition and Action Unit Detection
For instance, in the case of AU detection, the goal is to discriminate between the segments of an image sequence in which this AU is active or inactive.
Heteroscedastic Conditional Ordinal Random Fields for Pain Intensity Estimation from Facial Images
We propose a novel method for automatic pain intensity estimation from facial images based on the framework of kernel Conditional Ordinal Random Fields (KCORF).
