Search Results for author:
Found 23 papers, 6 papers with code
Multipar-T: Multiparty-Transformer for Capturing Contingent Behaviors in Group Conversations
As we move closer to real-world AI systems, AI agents must be able to deal with multiparty (group) conversations.
Mixed Effects Random Forests for Personalised Predictions of Clinical Depression Severity
We suggest that this improved performance results from the ability of the mixed effects random forest to personalise model parameters to individuals in the dataset.
Computational Empathy Counteracts the Negative Effects of Anger on Creative Problem Solving
In an online experiment with 1, 006 participants randomly assigned to an emotion elicitation intervention (with a control elicitation condition and anger elicitation condition) and a computational empathy intervention (with a control virtual agent and an empathic virtual agent), we examine how anger and empathy influence participants' performance in solving a word game based on Wordle.
Human Detection of Political Speech Deepfakes across Transcripts, Audio, and Video
Recent advances in technology for hyper-realistic visual and audio effects provoke the concern that deepfake videos of political speeches will soon be indistinguishable from authentic video recordings.
Predicting Driver Self-Reported Stress by Analyzing the Road Scene
Several studies have shown the relevance of biosignals in driver stress recognition.
Deepfake Detection by Human Crowds, Machines, and Machine-informed Crowds
The recent emergence of machine-manipulated media raises an important societal question: how can we know if a video that we watch is real or fake?
Human-centric Dialog Training via Offline Reinforcement Learning
We start by hosting models online, and gather human feedback from real-time, open-ended conversations, which we then use to train and improve the models using offline reinforcement learning (RL).
Dyadic Speech-based Affect Recognition using DAMI-P2C Parent-child Multimodal Interaction Dataset
Automatic speech-based affect recognition of individuals in dyadic conversation is a challenging task, in part because of its heavy reliance on manual pre-processing.
Way Off-Policy Batch Deep Reinforcement Learning of Human Preferences in Dialog
This is a critical shortcoming for applying RL to real-world problems where collecting data is expensive, and models must be tested offline before being deployed to interact with the environment -- e. g. systems that learn from human interaction.
Hierarchical Reinforcement Learning for Open-Domain Dialog
Open-domain dialog generation is a challenging problem; maximum likelihood training can lead to repetitive outputs, models have difficulty tracking long-term conversational goals, and training on standard movie or online datasets may lead to the generation of inappropriate, biased, or offensive text.
Pain Detection with fNIRS-Measured Brain Signals: A Personalized Machine Learning Approach Using the Wavelet Transform and Bayesian Hierarchical Modeling with Dirichlet Process Priors
Currently self-report pain ratings are the gold standard in clinical pain assessment.
Detection of Real-world Driving-induced Affective State Using Physiological Signals and Multi-view Multi-task Machine Learning
This may lead to empathic automotive user interfaces that account for the driver's emotional state and influence the driver in order to improve safety.
Way Off-Policy Batch Deep Reinforcement Learning of Implicit Human Preferences in Dialog
Most deep reinforcement learning (RL) systems are not able to learn effectively from off-policy data, especially if they cannot explore online in the environment.
Approximating Interactive Human Evaluation with Self-Play for Open-Domain Dialog Systems
To investigate the strengths of this novel metric and interactive evaluation in comparison to state-of-the-art metrics and human evaluation of static conversations, we perform extended experiments with a set of models, including several that make novel improvements to recent hierarchical dialog generation architectures through sentiment and semantic knowledge distillation on the utterance level.
Deep Reinforcement Learning for Optimal Critical Care Pain Management with Morphine using Dueling Double-Deep Q Networks
Our results demonstrate that reinforcement learning may be used to aid decision making in the intensive care setting by providing personalized pain management interventions.
Multi-task multiple kernel machines for personalized pain recognition from functional near-infrared spectroscopy brain signals
Currently there is no validated objective measure of pain.
Learning via social awareness: Improving a deep generative sketching model with facial feedback
We use a Latent Constraints GAN (LC-GAN) to learn from the facial feedback of a small group of viewers, by optimizing the model to produce sketches that it predicts will lead to more positive facial expressions.
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.
Physiological and behavioral profiling for nociceptive pain estimation using personalized multitask learning
Pain is a subjective experience commonly measured through patient's self report.
Multi-task Neural Networks for Personalized Pain Recognition from Physiological Signals
Pain is a complex and subjective experience that poses a number of measurement challenges.
DeepFaceLIFT: Interpretable Personalized Models for Automatic Estimation of Self-Reported Pain
Previous research on automatic pain estimation from facial expressions has focused primarily on "one-size-fits-all" metrics (such as PSPI).
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.
