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Found 14 papers, 1 papers with code
Understanding and Shifting Preferences for Battery Electric Vehicles
In this work, we focus on methods for personalizing interventions based on an individual's demographics to shift the preferences of consumers to be more positive towards Battery Electric Vehicles (BEVs).
Accelerating Understanding of Scientific Experiments with End to End Symbolic Regression
We develop a deep neural network (MACSYMA) to address the symbolic regression problem as an end-to-end supervised learning problem.
Few-Shot Learning of an Interleaved Text Summarization Model by Pretraining with Synthetic Data
Interleaved texts, where posts belonging to different threads occur in a sequence, commonly occur in online chat posts, so that it can be time-consuming to quickly obtain an overview of the discussions.
A Visual Analytics Framework for Contrastive Network Analysis
A common network analysis task is comparison of two networks to identify unique characteristics in one network with respect to the other.
Thoracic Disease Identification and Localization using Distance Learning and Region Verification
In this paper, we propose an alternative approach that learns discriminative features among triplets of images and cyclically trains on region features to verify whether attentive regions contain information indicative of a disease.
Network Comparison with Interpretable Contrastive Network Representation Learning
This analysis task could be greatly assisted by contrastive learning, which is an emerging analysis approach to discover salient patterns in one dataset relative to another.
Generating Multi-Sentence Abstractive Summaries of Interleaved Texts
The interleaved posts are encoded hierarchically, i. e., word-to-word (words in a post) followed by post-to-post (posts in a channel).
Addressing Data Bias Problems for Chest X-ray Image Report Generation
Automatic medical report generation from chest X-ray images is one possibility for assisting doctors to reduce their workload.
Adversarial Domain Adaptation Using Artificial Titles for Abstractive Title Generation
A common issue in training a deep learning, abstractive summarization model is lack of a large set of training summaries.
A Hierarchical Decoder with Three-level Hierarchical Attention to Generate Abstractive Summaries of Interleaved Texts
Interleaved texts, where posts belonging to different threads occur in one sequence, are a common occurrence, e. g., online chat conversations.
Harnessing Popularity in Social Media for Extractive Summarization of Online Conversations
We leverage a popularity measure in social media as a distant label for extractive summarization of online conversations.
Learning to Disentangle Interleaved Conversational Threads with a Siamese Hierarchical Network and Similarity Ranking
An enormous amount of conversation occurs online every day, such as on chat platforms where multiple conversations may take place concurrently.
Ranking Convolutional Recurrent Neural Networks for Purchase Stage Identification on Imbalanced Twitter Data
Users often use social media to share their interest in products.
Corpus for Customer Purchase Behavior Prediction in Social Media
These posts may include information about the purchase activity of people, and insights useful to companies can be derived from them: e. g. profile information of a user who mentioned something about their product.
