48 citations · 57 across the 7 of their papers we have counts for
7 papers
TimePre: Bridging Accuracy, Efficiency, and Stability in Probabilistic Time-Series Forecasting
Lingyu Jiang, Lingyu Xu, Peiran Li +14
We propose TimePre, a simple framework that unifies the efficiency of Multilayer Perceptron (MLP)-based models with the distributional flexibility of Multiple Choice Learning (MCL)…
AutoAL: Automated Active Learning with Differentiable Query Strategy Search
Yifeng Wang, Xueying Zhan, Siyu Huang
As deep learning continues to evolve, the need for data efficiency becomes increasingly important. Considering labeling large datasets is both time-consuming and expensive, active…
Pareto Optimization for Active Learning under Out-of-Distribution Data Scenarios
Xueying Zhan, Zeyu Dai, Qingzhong Wang +4
Pool-based Active Learning (AL) has achieved great success in minimizing labeling cost by sequentially selecting informative unlabeled samples from a large unlabeled data pool and…
Deep Active Learning with Noise Stability
Xingjian Li, Pengkun Yang, Yangcheng Gu +4
Uncertainty estimation for unlabeled data is crucial to active learning. With a deep neural network employed as the backbone model, the data selection process is highly challenging…
A Comparative Survey of Deep Active Learning
Xueying Zhan, Qingzhong Wang, Kuan-hao Huang +3
While deep learning (DL) is data-hungry and usually relies on extensive labeled data to deliver good performance, Active Learning (AL) reduces labeling costs by selecting a small p…
Multiple-criteria Based Active Learning with Fixed-size Determinantal Point Processes
Xueying Zhan, Qing Li, Antoni B. Chan
Active learning aims to achieve greater accuracy with less training data by selecting the most useful data samples from which it learns. Single-criterion based methods (i.e., infor…
ALdataset: a benchmark for pool-based active learning
Xueying Zhan, Antoni Bert Chan
Active learning (AL) is a subfield of machine learning (ML) in which a learning algorithm could achieve good accuracy with less training samples by interactively querying a user/or…