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Found 7 papers, 1 papers with code
Towards Stable Machine Learning Model Retraining via Slowly Varying Sequences
In this study, we develop a mixed integer optimization algorithm that holistically considers the problem of retraining machine learning models across different data batch updates.
SNE-RoadSegV2: Advancing Heterogeneous Feature Fusion and Fallibility Awareness for Freespace Detection
Feature-fusion networks with duplex encoders have proven to be an effective technique to solve the freespace detection problem.
Robust Regression over Averaged Uncertainty
We propose a new formulation of robust regression by integrating all realizations of the uncertainty set and taking an averaged approach to obtain the optimal solution for the ordinary least-squared regression problem.
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%.
Integrated multimodal artificial intelligence framework for healthcare applications
The generalizable properties and flexibility of our Holistic AI in Medicine (HAIM) framework could offer a promising pathway for future multimodal predictive systems in clinical and operational healthcare settings.
Augmented LiDAR Simulator for Autonomous Driving
Instead, we can simply deploy a vehicle with a LiDAR scanner to sweep the street of interests to obtain the background point cloud, based on which annotated point cloud can be automatically generated.
Exploration of Numerical Precision in Deep Neural Networks
Reduced numerical precision is a common technique to reduce computational cost in many Deep Neural Networks (DNNs).
