Uncertainty Regularized Multi-Task Learning
By sharing parameters and providing task-independent shared features, multi-task deep neural networks are considered one of the most interesting ways for parallel learning from different tasks and domains. However, fine-tuning on one task may compromise the performance of other tasks or restrict the generalization of the shared learned features. To address this issue, we propose to use task uncertainty to gauge the effect of the shared feature changes on other tasks and prevent the model from overfitting or over-generalizing. We conducted an experiment on 16 text classification tasks, and findings showed that the proposed method consistently improves the performance of the baseline, facilitates the knowledge transfer of learned features to unseen data, and provides explicit control over the generalization of the shared model.
Code (0)
등록된 구현이 없습니다.
Tasks
Multi-Task Learningtext-classificationText ClassificationTransfer LearningSimilar Papers 제목 키워드 기반
Quantifying Classification Uncertainty using Regularized Evidential Neural Networks
Traditional deep neural nets (NNs) have shown the state-of-the-art performance in the task of classification in various applications. However, NNs have not considered any types of uncertainty associated with the class pr…
ClassificationGeneral ClassificationEntropy-regularized Point-based Value Iteration
Model-based planners for partially observable problems must accommodate both model uncertainty during planning and goal uncertainty during objective inference. However, model-based planners may be brittle under these typ…
Multi-agent learning under uncertainty: Recurrence vs. concentration
In this paper, we examine the convergence landscape of multi-agent learning under uncertainty. Specifically, we analyze two stochastic models of regularized learning in continuous games -- one in continuous and one in di…
Deep Deterministic Uncertainty: A New Simple Baseline
Reliable uncertainty from deterministic single-forward pass models is sought after because conventional methods of uncertainty quantification are computationally expensive. We take two complex single-forward-pass unc…
Active LearningSemantic SegmentationUncertainty QuantificationMURO: Deployment Constrained Reinforcement Learning with Model-based Uncertainty Regularized Batch Optimization
In many contemporary applications such as healthcare, finance, robotics, and recommendation systems, continuous deployment of new policies for data collection and online learning is either cost ineffective or impractical…
Recommendation Systemsreinforcement-learningReinforcement Learning (RL)Uncertainty Quantification