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Found 14 papers, 4 papers with code
Growing Adaptive Multi-hyperplane Machines
Adaptive Multi-hyperplane Machine (AMM) is an online algorithm for learning Multi-hyperplane Machine (MM), a classification model which allows multiple hyperplanes per class.
A Visualization Approach for Rapid Labeling of Clinical Notes for Smoking Status Extraction
Labeling is typically the most human-intensive step during the development of supervised learning models.
X-Shot: A Unified System to Handle Frequent, Few-shot and Zero-shot Learning Simultaneously in Classification
In recent years, few-shot and zero-shot learning, which learn to predict labels with limited annotated instances, have garnered significant attention.
OpenStance: Real-world Zero-shot Stance Detection
To our knowledge, this is the first work that studies stance detection under the open-domain zero-shot setting.
Group Activity Recognition in Basketball Tracking Data -- Neural Embeddings in Team Sports (NETS)
The objective of this paper is to automatically recognize basketball group activities from tracking data representing locations of players and the ball during a game.
Learning Semi-Structured Representations of Radiology Reports
The results also demonstrate that the trained model produces reasonable semi-structured representations on an out-of-sample corpus of chest x-ray radiology reports from a different medical provider.
Improving Word Embeddings through Iterative Refinement of Word- and Character-level Models
Embedding of rare and out-of-vocabulary (OOV) words is an important open NLP problem.
Multi-Modal Trajectory Prediction of NBA Players
National Basketball Association (NBA) players are highly motivated and skilled experts that solve complex decision making problems at every time point during a game.
Cannot Predict Comment Volume of a News Article before (a few) Users Read It
We show that the early arrival rate of comments is the best indicator of the eventual number of comments.
Spatial Aggregation Facilitates Discovery of Spatial Topics
Spatial aggregation refers to merging of documents created at the same spatial location.
Regular Expression Guided Entity Mention Mining from Noisy Web Data
Finally, a human expert is asked to label a small set of documents and the neural network is fine tuned on those documents.
Semi-supervised Discovery of Informative Tweets During the Emerging Disasters
Given the wide range of possible disasters, using a pre-selected set of disaster-related keywords for the discovery is suboptimal.
Non-linear Label Ranking for Large-scale Prediction of Long-Term User Interests
We consider the problem of personalization of online services from the viewpoint of ad targeting, where we seek to find the best ad categories to be shown to each user, resulting in improved user experience and increased advertisers' revenue.
An expanded evaluation of protein function prediction methods shows an improvement in accuracy
To review progress in the field, the analysis also compared the best methods participating in CAFA1 to those of CAFA2.
Quantitative Methods
