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Found 12 papers, 2 papers with code
NL-Augmenter: A Framework for Task-Sensitive Natural Language Augmentation
Data augmentation is an important component in the robustness evaluation of models in natural language processing (NLP) and in enhancing the diversity of the data they are trained on.
OPAD: An Optimized Policy-based Active Learning Framework for Document Content Analysis
We propose novel rewards to account for class imbalance and user feedback in the annotation interface, to improve the active learning method.
Improved Semantic Role Labeling using Parameterized Neighborhood Memory Adaptation
Deep neural models achieve some of the best results for semantic role labeling.
CLAR: A Cross-Lingual Argument Regularizer for Semantic Role Labeling
Although different languages have different argument annotations, polyglot training, the idea of training one model on multiple languages, has previously been shown to outperform monolingual baselines, especially for low resource languages.
An Effective Label Noise Model for DNN Text Classification
In this paper, we propose an approach to training deep networks that is robust to label noise.
Optimizing Taxi Carpool Policies via Reinforcement Learning and Spatio-Temporal Mining
In this paper, we develop a reinforcement learning (RL) based system to learn an effective policy for carpooling that maximizes transportation efficiency so that fewer cars are required to fulfill the given amount of trip demand.
Tensor Matched Kronecker-Structured Subspace Detection for Missing Information
We consider the problem of detecting whether a tensor signal having many missing entities lies within a given low dimensional Kronecker-Structured (KS) subspace.
A Unified Neural Network Approach for Estimating Travel Time and Distance for a Taxi Trip
In building intelligent transportation systems such as taxi or rideshare services, accurate prediction of travel time and distance is crucial for customer experience and resource management.
Learning Deep Networks from Noisy Labels with Dropout Regularization
Large datasets often have unreliable labels-such as those obtained from Amazon's Mechanical Turk or social media platforms-and classifiers trained on mislabeled datasets often exhibit poor performance.
Classification and Representation via Separable Subspaces: Performance Limits and Algorithms
We study the classification performance of Kronecker-structured models in two asymptotic regimes and developed an algorithm for separable, fast and compact K-S dictionary learning for better classification and representation of multidimensional signals by exploiting the structure in the signal.
Effective Object Tracking in Unstructured Crowd Scenes
The analysis shows the advantages and limitations of the proposed approach for tracking an object in unstructured crowd scenes.
SA-CNN: Dynamic Scene Classification using Convolutional Neural Networks
The task of classifying videos of natural dynamic scenes into appropriate classes has gained lot of attention in recent years.
