Search Results for author:
Found 8 papers, 5 papers with code
Baidu Apollo EM Motion Planner
In this manuscript, we introduce a real-time motion planning system based on the Baidu Apollo (open source) autonomous driving platform.
Adaptive Early-Learning Correction for Segmentation from Noisy Annotations
We discover a phenomenon that has been previously reported in the context of classification: the networks tend to first fit the clean pixel-level labels during an "early-learning" phase, before eventually memorizing the false annotations.
Variationally Regularized Graph-based Representation Learning for Electronic Health Records
A feasible approach to improving the representation learning of EHR data is to associate relevant medical concepts and utilize these connections.
Multiple Instance Learning via Iterative Self-Paced Supervised Contrastive Learning
The framework employs a novel self-paced sampling strategy to ensure the accuracy of pseudo labels.
Interpretable Prediction of Lung Squamous Cell Carcinoma Recurrence With Self-supervised Learning
The resulting representations and clusters from self-supervision are used as features of a survival model for recurrence prediction at the patient level.
Variational hybridization and transformation for large inaccurate noisy-or networks
In addition, we propose a transformation ranking algorithm that is very stable to large variances in network prior probabilities, a common issue that arises in medical applications of Bayesian networks.
Deep Probability Estimation
Reliable probability estimation is of crucial importance in many real-world applications where there is inherent (aleatoric) uncertainty.
Making Self-supervised Learning Robust to Spurious Correlation via Learning-speed Aware Sampling
Self-supervised learning (SSL) has emerged as a powerful technique for learning rich representations from unlabeled data.
