Search Results for author:
Found 25 papers, 5 papers with code
AdBERT: An Effective Few Shot Learning Framework for Aligning Tweets to Superbowl Advertisements
As part of this study, we have curated and developed two datasets that can prove to be useful for Social TV research: 1) dataset of ad-related tweets and 2) dataset of ad descriptions of Superbowl advertisements.
Low-light phase retrieval with implicit generative priors
Phase retrieval (PR) is fundamentally important in scientific imaging and is crucial for nanoscale techniques like coherent diffractive imaging (CDI).
Explain Variance of Prediction in Variational Time Series Models for Clinical Deterioration Prediction
To achieve this goal, we propose variance SHAP with variational time series models, an application of Shapley Additive Expanation(SHAP) algorithm to attribute epistemic prediction uncertainty.
A Kalman Filter Based Framework for Monitoring the Performance of In-Hospital Mortality Prediction Models Over Time
Therefore, in this study, for binary classifiers running in a long time period, we proposed to adjust these performance metrics for sample size and class distribution, so that a fair comparison can be made between two time periods.
Which Modality should I use -- Text, Motif, or Image? : Understanding Graphs with Large Language Models
Our research integrates graph data with Large Language Models (LLMs), which, despite their advancements in various fields using large text corpora, face limitations in encoding entire graphs due to context size constraints.
Filling out the missing gaps: Time Series Imputation with Semi-Supervised Learning
Our results indicate that the proposed method outperforms the existing supervised and unsupervised time series imputation methods measured on the imputation quality as well as on the downstream tasks ingesting imputed time series.
Embarrassingly Simple MixUp for Time-series
Labeling time series data is an expensive task because of domain expertise and dynamic nature of the data.
Hierarchical clustering by aggregating representatives in sub-minimum-spanning-trees
One of the main challenges for hierarchical clustering is how to appropriately identify the representative points in the lower level of the cluster tree, which are going to be utilized as the roots in the higher level of the cluster tree for further aggregation.
PARIS: Personalized Activity Recommendation for Improving Sleep Quality
The quality of sleep has a deep impact on people's physical and mental health.
MOOCRep: A Unified Pre-trained Embedding of MOOC Entities
Many machine learning models have been built to tackle information overload issues on Massive Open Online Courses (MOOC) platforms.
Learning Student Interest Trajectory for MOOC Thread Recommendation
The projection operation learns to estimate future embedding of students and threads.
Assessing Individual and Community Vulnerability to Fake News in Social Networks
In this paper we base our idea on Computational Trust in social networks to propose a novel Community Health Assessment model against fake news.
Community Detection
Social and Information Networks
Continual Deterioration Prediction for Hospitalized COVID-19 Patients
In this paper, we first verify the assumption that clinical variables could have time-varying effects on COVID-19 outcomes.
An Empirical Comparison of Deep Learning Models for Knowledge Tracing on Large-Scale Dataset
Recent release of large-scale student performance dataset \cite{choi2019ednet} motivates the analysis of performance of deep learning approaches that have been proposed to solve KT.
Learning Student Interest Trajectory for MOOCThread Recommendation
The projection operation learns to estimate future embedding of students and threads.
RKT : Relation-Aware Self-Attention for Knowledge Tracing
The aim of KT is to model student's knowledge level based on their answers to a sequence of exercises referred as interactions.
Using Clinical Notes with Time Series Data for ICU Management
Monitoring patients in ICU is a challenging and high-cost task.
Adversarial Unsupervised Representation Learning for Activity Time-Series
Sufficient physical activity and restful sleep play a major role in the prevention and cure of many chronic conditions.
A Structured Learning Approach with Neural Conditional Random Fields for Sleep Staging
We propose an end-to-end framework that uses a combination of deep convolution and recurrent neural networks to extract high-level features from raw flow signal with a structured output layer based on a conditional random field to model the temporal transition structure of the sleep stages.
Hyperedge2vec: Distributed Representations for Hyperedges
We present a number of interesting baselines, some of which adapt existing node-level embedding models to the hyperedge-level, as well as sequence based language techniques which are adapted for set structured hypergraph topology.
Co-Morbidity Exploration on Wearables Activity Data Using Unsupervised Pre-training and Multi-Task Learning
This is a critical barrier for the use of this new source of signal for healthcare decision making.
Impact of Physical Activity on Sleep:A Deep Learning Based Exploration
In this paper we explore the use of deep learning to build sleep quality prediction models based on actigraphy data.
Robust Automated Human Activity Recognition and its Application to Sleep Research
Human Activity Recognition (HAR) is a powerful tool for understanding human behaviour.
Cross-Language Domain Adaptation for Classifying Crisis-Related Short Messages
Scarcity of labeled data causes poor performance in machine training.
Predictive Overlapping Co-Clustering
In this paper, we present the novel idea of Predictive Overlapping Co-Clustering (POCC) as an optimization problem for a more effective and improved predictive analysis.
