Search Results for author:
Found 21 papers, 6 papers with code
CoLafier: Collaborative Noisy Label Purifier With Local Intrinsic Dimensionality Guidance
CoLafier consists of two subnets: LID-dis and LID-gen. LID-dis is a specialized classifier.
UCE-FID: Using Large Unlabeled, Medium Crowdsourced-Labeled, and Small Expert-Labeled Tweets for Foodborne Illness Detection
To overcome these challenges, we propose EGAL, a deep learning framework for foodborne illness detection that uses small expert-labeled tweets augmented by crowdsourced-labeled and massive unlabeled data.
Finding Short Signals in Long Irregular Time Series with Continuous-Time Attention Policy Networks
Using this insight, we then propose CAT, a model that classifies multivariate ITS by explicitly seeking highly-relevant portions of an input series' timeline.
Class-Specific Explainability for Deep Time Series Classifiers
Instead, when a classifier is choosing between many classes, an effective explanation must show what sets the chosen class apart from the rest.
Stop&Hop: Early Classification of Irregular Time Series
We bridge this gap and study early classification of irregular time series, a new setting for early classifiers that opens doors to more real-world problems.
MANI-Rank: Multiple Attribute and Intersectional Group Fairness for Consensus Ranking
This is an extended version of "MANI-Rank: Multiple Attribute and Intersectional Group Fairness for Consensus Ranking", to appear in ICDE 2022.
DeepEmotex: Classifying Emotion in Text Messages using Deep Transfer Learning
We also study the effect of the size of the fine-tuning dataset on the accuracy of our models.
TWEET-FID: An Annotated Dataset for Multiple Foodborne Illness Detection Tasks
To accelerate the development of machine learning-based models for foodborne outbreak detection, we thus present TWEET-FID (TWEET-Foodborne Illness Detection), the first publicly available annotated dataset for multiple foodborne illness incident detection tasks.
One-Shot Learning on Attributed Sequences
In this paper, we study the problem of one-shot learning on attributed sequences, where each instance is composed of a set of attributes (e. g., user profile) and a sequence of categorical items (e. g., clickstream).
Recurrent Bayesian Classifier Chains for Exact Multi-Label Classification
In this work we propose Recurrent Bayesian Classifier Chains (RBCCs), which learn a Bayesian network of class dependencies and leverage this network in order to condition the prediction of child nodes only on their parents.
Outlier Preserving Distribution Mapping Autoencoders
OP-DMA succeeds in mapping outliers to low probability regions in the latent space by leveraging a novel Prior-Weighted Loss (PWL) that utilizes the insight that outliers are likely to have a higher reconstruction error than inliers.
MLAS: Metric Learning on Attributed Sequences
Distance metric learning has attracted much attention in recent years, where the goal is to learn a distance metric based on user feedback.
A Dual-Attention Network for Joint Named Entity Recognition and Sentence Classification of Adverse Drug Events
An adverse drug event (ADE) is an injury resulting from medical intervention related to a drug.
Human Attention Maps for Text Classification: Do Humans and Neural Networks Focus on the Same Words?
Motivated by human attention, computational attention mechanisms have been designed to help neural networks adjust their focus on specific parts of the input data.
Attributed Sequence Embedding
This problem is core to many important data mining tasks ranging from user behavior analysis to the clustering of gene sequences.
Unknown-Aware Deep Neural Network
Unfortunately, although the strong generalization ability of existing CNNs ensures their accuracy when classifying known objects, it also causes them to often assign an unknown to a target class with high confidence.
Context-Aware Object Detection With Convolutional Neural Networks
Although the state-of-the-art object detection methods are successful in detecting and classifying objects by leveraging deep convolutional neural networks (CNNs), these methods overlook the semantic context which implies the probabilities that different classes of objects occur jointly.
Versatile Anomaly Detection with Outlier Preserving Distribution Mapping Autoencoders
State-of-the-art deep learning methods for outlier detection make the assumption that anomalies will appear far away from inlier data in the latent space produced by distribution mapping deep networks.
Reducing Computation in Recurrent Networks by Selectively Updating State Neurons
As a result, even for high-dimensional hidden states, all dimensions are updated at each timestep regardless of the recurrent memory cell.
Adaptive-Halting Policy Network for Early Classification
Early classification of time series is the prediction of the class label of a time series before it is observed in its entirety.
Outlier Detection from Image Data
Modern applications from Autonomous Vehicles to Video Surveillance generate massive amounts of image data.
