Search Results for author:
Found 18 papers, 6 papers with code
BU-NEmo: an Affective Dataset of Gun Violence News
As such, we introduce a U. S. gun violence news dataset that contains news headline and image pairings from 840 news articles with 15K high-quality, crowdsourced annotations on emotional responses to the news pairings.
HOPE: Human-Centric Off-Policy Evaluation for E-Learning and Healthcare
Reinforcement learning (RL) has been extensively researched for enhancing human-environment interactions in various human-centric tasks, including e-learning and healthcare.
Variational Latent Branching Model for Off-Policy Evaluation
In this work, we propose the variational latent branching model (VLBM) to learn the transition function of MDPs by formulating the environmental dynamics as a compact latent space, from which the next states and rewards are then sampled.
A Geometric-Relational Deep Learning Framework for BIM Object Classification
It can be supplemented using deep learning.
Client Selection in Federated Learning: Principles, Challenges, and Opportunities
As a privacy-preserving paradigm for training Machine Learning (ML) models, Federated Learning (FL) has received tremendous attention from both industry and academia.
Taking a Language Detour: How International Migrants Speaking a Minority Language Seek COVID-Related Information in Their Host Countries
Our data indicated that participants often took language detours, or visits to Mandarin resources for information about the COVID outbreak in their host countries.
Facilitating Global Team Meetings Between Language-Based Subgroups: When and How Can Machine Translation Help?
We manipulated the exchange of subgroup conversation logs prior to team meetings: with MT-mediated exchanges versus without.
Simulating Bandit Learning from User Feedback for Extractive Question Answering
We study learning from user feedback for extractive question answering by simulating feedback using supervised data.
Reconstructing Missing EHRs Using Time-Aware Within- and Cross-Visit Information for Septic Shock Early Prediction
Real-world Electronic Health Records (EHRs) are often plagued by a high rate of missing data.
Modeling and Validating Temporal Rules with Semantic Petri-Net for Digital Twins
Semantic rule checking on RDFS/OWL data has been widely used in the construction industry.
Guidance and Teaching Network for Video Salient Object Detection
Owing to the difficulties of mining spatial-temporal cues, the existing approaches for video salient object detection (VSOD) are limited in understanding complex and noisy scenarios, and often fail in inferring prominent objects.
CloudAAE: Learning 6D Object Pose Regression with On-line Data Synthesis on Point Clouds
In contrast, this domain gap is considerably smaller and easier to fill for depth information.
Early Performance Prediction using Interpretable Patterns in Programming Process Data
From the log data, we extract patterns of behaviors that are predictive of students' success using an approach called differential sequence mining.
Neural Image Compression via Attentional Multi-Scale Back Projection and Frequency Decomposition
In recent years, neural image compression emerges as a rapidly developing topic in computer vision, where the state-of-the-art approaches now exhibit superior compression performance than their conventional counterparts.
6D Object Pose Regression via Supervised Learning on Point Clouds
We use depth information represented by point clouds as the input to both deep networks and geometry-based pose refinement and use separate networks for rotation and translation regression.
Neural Metaphor Detection in Context
We present end-to-end neural models for detecting metaphorical word use in context.
Occlusion Resistant Object Rotation Regression from Point Cloud Segments
Rotation estimation of known rigid objects is important for robotic applications such as dexterous manipulation.
Saliency-guided Adaptive Seeding for Supervoxel Segmentation
We propose a new saliency-guided method for generating supervoxels in 3D space.
