| 2015 |
An Active Learning Approach to Coreference Resolution |
Sachan et al. |
IJCAI |
- |
Coreference Resolution |
informative, DNNs, None, Tra, Hard |
ACE-2008,IC Event Coreference Corpus,Amar Ujala and Navbharat Times |
|
| 2019 |
Active Learning within Constrained Environments through Imitation of an Expert Questioner |
Bullard et al. |
IJCAI |
- |
cognitive capability |
task-centric+ enviroment-centric, DNNs, Imitation learning+Reinforce learning, Tra, Hard |
RGB-D object datasets,UW dataset |
|
| 2019 |
Adaptive ensemble active learning for drifting data stream mining |
Krawczyk and Cano |
IJCAI |
- |
stream-based |
|
|
Ensemble |
| 2019 |
Deep Active Learning for Anchor User Prediction |
Cheng et al. |
IJCAI |
code |
social network prediction |
informative, DNNs, None, Tra, Hard |
Foursquare, Twitter |
|
| 2019 |
Deep Active Learning with Adaptive Acquisition |
Haussmann et al. |
IJCAI |
code |
Image Classification |
Uncertain, BNNs,reinforcement feedback, Tra, Hard |
MNIST, CIFAR-10, FashionMNIST |
Model selection |
| 2019 |
Bidirectional Active Learning with Gold-Instance-Based Human Training |
Tang et al. |
IJCAI |
- |
Classification |
Any, DNNs, None, Tra, New Annotation |
Birds, Leafsnap, Butterflies |
can enhance human related expertise during labeling and improve relabel- ing quality accordingly |
| 2019 |
Multi-View Active Learning for Video Recommendation |
Cai et al. |
IJCAI |
- |
classification datasets and real video recommendation tasks |
Informative, Multi-view DNNs, None, Tra, Hard |
YouTube Multiview Video Games, Wikipedia Articles |
video Recommendation |
| 2019 |
Mindful Active Learning |
Ashari and Ghasemzadeh |
IJCAI |
code |
|
Uncertainty, SVM, None, Tra, Hard |
HART, DAS, AReM |
|