Search Results for author:
Found 19 papers, 5 papers with code
Retrieval Based Response Letter Generation For a Customer Care Setting
Letter-like communications (such as email) are a major means of customer relationship management within customer-facing organizations.
Masked LoGoNet: Fast and Accurate 3D Image Analysis for Medical Domain
In this paper, we introduce a new neural network architecture, termed LoGoNet, with a tailored self-supervised learning (SSL) method to mitigate such challenges.
CrashFormer: A Multimodal Architecture to Predict the Risk of Crash
Reducing traffic accidents is a crucial global public safety concern.
Novel Physics-Based Machine-Learning Models for Indoor Air Quality Approximations
Machine learning (ML) models are capable of performing air-quality "ahead-of-time" approximations.
Judge Me in Context: A Telematics-Based Driving Risk Prediction Framework in Presence of Weak Risk Labels
Driving risk prediction has been a topic of much research over the past few decades to minimize driving risk and increase safety.
Will there be a construction? Predicting road constructions based on heterogeneous spatiotemporal data
This approach is costly and difficult to scale.
Scalable Deep Graph Clustering with Random-walk based Self-supervised Learning
Though other methods (particularly those based on Laplacian Smoothing) have reported better accuracy, a fundamental limitation of all the work is a lack of scalability.
Driving Style Representation in Convolutional Recurrent Neural Network Model of Driver Identification
Using CNN, we capture semantic patterns of driver behavior from trajectories (such as a turn or a braking event).
Sequence-to-Set Semantic Tagging for Complex Query Reformulation and Automated Text Categorization in Biomedical IR using Self-Attention
Novel contexts, comprising a set of terms referring to one or more concepts, may often arise in complex querying scenarios such as in evidence-based medicine (EBM) involving biomedical literature.
Sequence-to-Set Semantic Tagging: End-to-End Multi-label Prediction using Neural Attention for Complex Query Reformulation and Automated Text Categorization
Our approach to generate document encodings employing our sequence-to-set models for inference of semantic tags, gives to the best of our knowledge, the state-of-the-art for both, the unsupervised query expansion task for the TREC CDS 2016 challenge dataset when evaluated on an Okapi BM25--based document retrieval system; and also over the MLTM baseline (Soleimani et al, 2016), for both supervised and semi-supervised multi-label prediction tasks on the del. icio. us and Ohsumed datasets.
Towards Successful Social Media Advertising: Predicting the Influence of Commercial Tweets
Businesses communicate using Twitter for a variety of reasons -- to raise awareness of their brands, to market new products, to respond to community comments, and to connect with their customers and potential customers in a targeted manner.
Learning to Answer Subjective, Specific Product-Related Queries using Customer Reviews by Adversarial Domain Adaptation
Online customer reviews on large-scale e-commerce websites, represent a rich and varied source of opinion data, often providing subjective qualitative assessments of product usage that can help potential customers to discover features that meet their personal needs and preferences.
Accident Risk Prediction based on Heterogeneous Sparse Data: New Dataset and Insights
Further, we have shown the impact of traffic information, time, and points-of-interest data for real-time accident prediction.
Tweets Can Tell: Activity Recognition using Hybrid Long Short-Term Memory Model
This paper presents techniques to detect the "offline" activity a person is engaged in when she is tweeting (such as dining, shopping or entertainment), in order to create a dynamic profile of the user, for uses such as better targeting of advertisements.
A Countrywide Traffic Accident Dataset
Reducing traffic accidents is an important public safety challenge.
Databases Computers and Society
Phrase2VecGLM: Neural generalized language model--based semantic tagging for complex query reformulation in medical IR
In this work, we develop a novel, completely unsupervised, neural language model-based document ranking approach to semantic tagging of documents, using the document to be tagged as a query into the GLM to retrieve candidate phrases from top-ranked related documents, thus associating every document with novel related concepts extracted from the text.
Discovery of Driving Patterns by Trajectory Segmentation
We apply the segmentation approach on a real-word, rich dataset of personal car trajectories provided by a major insurance company based in Columbus, Ohio.
QDEE: Question Difficulty and Expertise Estimation in Community Question Answering Sites
We also propose a model to route newly posted questions to appropriate users based on the difficulty level of the question and the expertise of the user.
Characterizing Driving Context from Driver Behavior
Because of the increasing availability of spatiotemporal data, a variety of data-analytic applications have become possible.
