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# Active Learning Edit

119 papers with code · Methodology

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# JointMap: Joint Query Intent Understanding For Modeling Intent Hierarchies in E-commerce Search

28 May 2020

In this paper, we introduce Joint Query Intent Understanding (JointMap), a deep learning model to simultaneously learn two different high-level user intent tasks: 1) identifying a query's commercial vs. non-commercial intent, and 2) associating a set of relevant product categories in taxonomy to a product query.

# Minimizing Supervision in Multi-label Categorization

26 May 2020

The approach we adopt is one of active learning, i. e., incrementally selecting a set of samples that need supervision based on the current model, obtaining supervision for these samples, retraining the model with the additional set of supervised samples and proceeding again to select the next set of samples.

# Discriminative Active Learning for Domain Adaptation

24 May 2020

To this end, we introduce a discriminative active learning approach for domain adaptation to reduce the efforts of data annotation.

# Active Learning for Skewed Data Sets

23 May 2020

Furthermore, there is usually only a small amount of initial training data available when building machine-learned models to solve such problems.

# Batch Decorrelation for Active Metric Learning

20 May 2020

We present an active learning strategy for training parametric models of distance metrics, given triplet-based similarity assessments: object $x_i$ is more similar to object $x_j$ than to $x_k$.

# Stopping criterion for active learning based on deterministic generalization bounds

15 May 2020

Active learning is a framework in which the learning machine can select the samples to be used for training.

# Empowering Active Learning to Jointly Optimize System and User Demands

9 May 2020

Existing approaches to active learning maximize the system performance by sampling unlabeled instances for annotation that yield the most efficient training.

# Deeply Supervised Active Learning for Finger Bones Segmentation

7 May 2020

Segmentation is a prerequisite yet challenging task for medical image analysis.

# Active Learning with Multiple Kernels

7 May 2020

In this paper, we introduce a new research problem, termed (stream-based) active multiple kernel learning (AMKL), in which a learner is allowed to label selected data from an oracle according to a selection criterion.

# Modeling nanoconfinement effects using active learning

6 May 2020

At this scale, the fluid properties are affected by nanoconfinement effects due to the increased fluid-solid interactions.