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Found 6 papers, 2 papers with code
TabText: A Flexible and Contextual Approach to Tabular Data Representation
We show that 1) applying our TabText framework enables the generation of high-performing and simple machine learning baseline models with minimal data pre-processing, and 2) augmenting pre-processed tabular data with TabText representations improves the average and worst-case AUC performance of standard machine learning models by as much as 6%.
Holistic Deep Learning
This paper presents a novel holistic deep learning framework that simultaneously addresses the challenges of vulnerability to input perturbations, overparametrization, and performance instability from different train-validation splits.
Geo-Spatiotemporal Features and Shape-Based Prior Knowledge for Fine-grained Imbalanced Data Classification
Fine-grained classification aims at distinguishing between items with similar global perception and patterns, but that differ by minute details.
Hurricane Forecasting: A Novel Multimodal Machine Learning Framework
In summary, our work demonstrates that utilizing machine learning techniques to combine different data sources can lead to new opportunities in tropical cyclone forecasting.
From predictions to prescriptions: A data-driven response to COVID-19
Specifically, we propose a comprehensive data-driven approach to understand the clinical characteristics of COVID-19, predict its mortality, forecast its evolution, and ultimately alleviate its impact.
InsectUp: Crowdsourcing Insect Observations to Assess Demographic Shifts and Improve Classification
Insects play such a crucial role in ecosystems that a shift in demography of just a few species can have devastating consequences at environmental, social and economic levels.
