Search Results for author:
Found 17 papers, 7 papers with code
A/B testing under Interference with Partial Network Information
A/B tests are often required to be conducted on subjects that might have social connections.
Zero-shot Microclimate Prediction with Deep Learning
Weather station data is a valuable resource for climate prediction, however, its reliability can be limited in remote locations.
Machine Learning for Automated Mitral Regurgitation Detection from Cardiac Imaging
Mitral regurgitation (MR) is a heart valve disease with potentially fatal consequences that can only be forestalled through timely diagnosis and treatment.
MultiWave: Multiresolution Deep Architectures through Wavelet Decomposition for Multivariate Time Series Prediction
To address these issues, we introduce MultiWave, a novel framework that enhances deep learning time series models by incorporating components that operate at the intrinsic frequencies of signals.
Progressive Fusion for Multimodal Integration
In this work, we present an iterative representation refinement approach, called Progressive Fusion, which mitigates the issues with late fusion representations.
Structure Mapping for Transferability of Causal Models
Human beings learn causal models and constantly use them to transfer knowledge between similar environments.
Personalized Student Stress Prediction with Deep Multitask Network
With the growing popularity of wearable devices, the ability to utilize physiological data collected from these devices to predict the wearer's mental state such as mood and stress suggests great clinical applications, yet such a task is extremely challenging.
Alzheimer's Disease Brain MRI Classification: Challenges and Insights
In recent years, many papers have reported state-of-the-art performance on Alzheimer's Disease classification with MRI scans from the Alzheimer's Disease Neuroimaging Initiative (ADNI) dataset using convolutional neural networks.
Multi-resolution Networks For Flexible Irregular Time Series Modeling (Multi-FIT)
Vertical FIT (FIT-V) is a variant of FIT which also models the relationship between different temporal signals while creating the informative dense representations for the signal.
FLARe: Forecasting by Learning Anticipated Representations
Computational models that forecast the progression of Alzheimer's disease at the patient level are extremely useful tools for identifying high risk cohorts for early intervention and treatment planning.
A Comprehensive Study of Alzheimer's Disease Classification Using Convolutional Neural Networks
A plethora of deep learning models have been developed for the task of Alzheimer's disease classification from brain MRI scans.
Pedestrian Detection in Thermal Images using Saliency Maps
Thermal images are mainly used to detect the presence of people at night or in bad lighting conditions, but perform poorly at daytime.
Machine Learning for Health (ML4H) Workshop at NeurIPS 2018
This volume represents the accepted submissions from the Machine Learning for Health (ML4H) workshop at the conference on Neural Information Processing Systems (NeurIPS) 2018, held on December 8, 2018 in Montreal, Canada.
ShortFuse: Biomedical Time Series Representations in the Presence of Structured Information
In healthcare applications, temporal variables that encode movement, health status and longitudinal patient evolution are often accompanied by rich structured information such as demographics, diagnostics and medical exam data.
Deep Neural Decision Forests
We present Deep Neural Decision Forests - a novel approach that unifies classification trees with the representation learning functionality known from deep convolutional networks, by training them in an end-to-end manner.
Projection Retrieval for Classification
In many applications classification systems often require in the loop human intervention.
