Natasha Jaques
Natasha Jaques
Awards
Press
Featured
Publications
Topics
Talks
Communities
Light
Dark
Automatic
1
Interactive Musical Improvisation with Magenta
This demo deployed RL Tuner and other Magenta music generation models into an interactive interface in which users can collaborate creatively with a machine learning model. The interface supports call and response interaction, automatically generating an accompaniment to the user’s melody, or melody morphing: responding both with variations on the user’s melody and a bass accompaniment.
A. Roberts
,
J. Engel
,
C. Hawthorne
,
I. Simon
,
E. Waite
,
S. Oore
,
Natasha Jaques
,
C. Resnick
,
D. Eck
2016
In
Neural Information Processing Systems (NeurIPS)
Best Demo
Cite
Code
Video
NeurIPS Demo
Magenta
Blog post
Machine Learning of Sleep and Wake Behaviors to Classify Self-Reported Evening Mood
Machine learning applied to nightly data from sensors and smartphones, shows value for predicting college student’s mood the following evening. Using multi-task learning to simultaneously predicted related wellbeing factors like health, energy, stress, and alertness improves performance.
S. Taylor
,
Natasha Jaques
,
A. Sano
,
A. Azaria
,
A. Ghandeharioun
,
R. Picard
2016
In
Sleep
PDF
Cite
Code
Personality, Attitudes, and Bonding in Conversations
We collect observational data from real human conversations, and develop a measure of how much participants experienced bonding or chemistry. We analyze the effects of personality and attitudes on bonding, and find that attentiveness and excitement are more effective at promoting bonding than traits like attractiveness and humour.
Natasha Jaques
,
Y. K. Kim
,
\& Picard R. Picard R
2016
In
Intelligent Virtual Agents (IVA)
PDF
Cite
Tuning Recurrent Neural Networks with Reinforcement Learning
Generating music using traditional supervised sequence models suffers from known failure modes, including the inability to produce coherent global structure. Music is an interesting sequence generation problem, because musical compositions adhere to known rules. We impose these rules with a novel algorithm combining RL and supervised learning.
Natasha Jaques
,
S. Gu
,
R. E. Turner
,
D. Eck
2016
In
International Conference on Learning Representations (ICLR) - workshop
PDF
Cite
Code
Magenta blog
MIT Tech Review article
Understanding and Predicting Bonding in Conversations Using Thin Slices of Facial Expressions and Body Language
Given only one-minute slices of facial expressions and body language, we use machine learning to accurately predict whether two humans having a conversation will bond with each other. We analyze factors which lead to bonding and discover that synchrony in body language and appropriate, empathetic facial expressions lead to higher bonding.
Natasha Jaques
,
D. McDuff
,
Y. K. Kim
,
\& Picard R. Picard R
2016
In
Intelligent Virtual Agents (IVA)
PDF
Cite
Active learning for Electrodermal Activity classification
We use labels provided by domain experts to classify whether artifacts are present in an Electrodermal Activity signal. Through the use of active learning, we improve sample efficiency and reduce the burden on human experts by as much as 84%, while offering the same or improved performance.
V. Xia
,
Natasha Jaques
,
S. Taylor
,
S. Fedor
,
R. Picard
2015
In
IEEE Conference on Signal Processing in Medicine and Biology (SPMB)
PDF
Cite
Automatic identification of artifacts in Electrodermal Activity data
Ambulatory measurement of Electrodermal Activity (EDA) from the wrist has important clinical benefits, such as predicting mood, stress, health, or even seizures. However, ambulatory measurement is noisy, and artifacts can easily be mistaken for true Skin Conductance Responses (SCRs). In addition to our paper which describes a machine learning method for detecting artifacts with 95% test accuracy, we built EDA Explorer, an open-source tool that allows users to automatically detect artifacts and SCRs within their data.
S. Taylor
*
,
Natasha Jaques
*
,
W. Chen
,
S. Fedor
,
A. Sano
,
R. Picard
2015
In
International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC)
PDF
Cite
Code
EDA Explorer tool
Artifact detection tutorial
SCR detection tutorial
Research which uses EDA Explorer
Engaging the workplace with challenges
The Challenge is a tool aimed at promoting social connections and decreasing sedentary activity in a workplace environment. Participants are paired with a partner to complete short physical challenges, leveraging social obligation and social consensus to drive behavior change.
Natasha Jaques
,
N. Farve
2015
In
International Conference on Persuasive Technologies
PDF
Cite
Video
Extended paper
Multi-task Multi-Kernel Learning for Estimating Individual Wellbeing
Wellbeing is a complex internal state consisting of several related dimensions, such as happiness, stress, energy, and health. We use Multi-task Multi-kernel learning to classify them simultaneously, leading to significant performance approvements.
Natasha Jaques
*
,
S. Taylor
*
,
A. Sano
,
R. Picard
2015
In
Neural Information Processing Systems (NeurIPS) Workshop on Multimodal Machine Learning
PDF
Cite
Code
Predicting students' happiness from physiology, phone, mobility, and behavioral data
We train machine learning models to predict students’ happiness from extensive data comprising physiological signals, location, smartphone logs, and behavioral questions. Analyzing which features provide the highest information gain reveals that skin conductance during sleep, social interaction, exercise, and fewer phone screen hours are all positively associated with happiness.
Natasha Jaques
*
,
S. Taylor
*
,
A. Azaria
,
A. Ghandeharioun
,
A. Sano
,
R. Picard
2015
In
International Conference on Affective Computing and Intelligent Interaction (ACII)
PDF
Cite
NCBI link
«
»
Cite
×