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Automatic identification of artifacts in electrodermal activity data
TLDR
This paper describes the development of a machine learning algorithm for automatically detecting EDA artifacts, and provides an empirical evaluation of classification performance. Expand
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Way Off-Policy Batch Deep Reinforcement Learning of Implicit Human Preferences in Dialog
TLDR
We develop a novel class of off-policy batch RL algorithms, which are able to effectively learn offline, without exploring, from a fixed batch of human interaction data. Expand
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Recognizing academic performance, sleep quality, stress level, and mental health using personality traits, wearable sensors and mobile phones
What can wearable sensors and usage of smart phones tell us about academic performance, self-reported sleep quality, stress and mental health condition? To answer this question, we collectedExpand
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Social Influence as Intrinsic Motivation for Multi-Agent Deep Reinforcement Learning
TLDR
We propose a unified mechanism for achieving coordination and communication in Multi-Agent Reinforcement Learning (MARL), through rewarding agents for having causal influence over other agents' actions. Expand
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Predicting Affect from Gaze Data during Interaction with an Intelligent Tutoring System
TLDR
In this paper we investigate the usefulness of eye tracking data for predicting emotions relevant to learning, specifically boredom and curiosity. Expand
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Sequence Tutor: Conservative Fine-Tuning of Sequence Generation Models with KL-control
TLDR
This paper proposes a general method for improving the structure and quality of sequences generated by a recurrent neural network (RNN), while maintaining information originally learned from data. Expand
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Multimodal autoencoder: A deep learning approach to filling in missing sensor data and enabling better mood prediction
TLDR
This paper describes a new technique for handling missing multimodal data using a specialized denoising autoencoder: the Multimodal AutoenCoder (MMAE). Expand
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Predicting students' happiness from physiology, phone, mobility, and behavioral data
TLDR
In order to model students' happiness, we apply machine learning methods to data collected from undergrad students monitored over the course of one month. Expand
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Personalized Multitask Learning for Predicting Tomorrow's Mood, Stress, and Health
TLDR
We employ Multitask Learning (MTL) techniques to train personalized ML models which are customized to the needs of each individual, but still leverage data from across the population. Expand
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Tuning Recurrent Neural Networks with Reinforcement Learning
TLDR
We propose a novel sequence-learning approach in which we use a pre-trained Recurrent Neural Network (RNN) to supply part of the reward value in a Reinforcement Learning (RL) model. Expand
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