Search Results for author:
Found 12 papers, 0 papers with code
GenEARL: A Training-Free Generative Framework for Multimodal Event Argument Role Labeling
In this paper, we propose GenEARL, a training-free generative framework that harness the power of the modern generative models to understand event task descriptions given image contexts to perform the EARL task.
Improving Event Definition Following For Zero-Shot Event Detection
We hypothesize that a diverse set of event types and definitions are the key for models to learn to follow event definitions while existing event extraction datasets focus on annotating many high-quality examples for a few event types.
Professional Basketball Player Behavior Synthesis via Planning with Diffusion
To accomplish reward-guided trajectory generation, conditional sampling is introduced to condition the diffusion model on the value function and conduct classifier-guided sampling.
STAR: Boosting Low-Resource Information Extraction by Structure-to-Text Data Generation with Large Language Models
Information extraction tasks such as event extraction require an in-depth understanding of the output structure and sub-task dependencies.
An Analysis of COVID-19 Knowledge Graph Construction and Applications
The construction and application of knowledge graphs have seen a rapid increase across many disciplines in recent years.
Computing Equilibria in Binary Networked Public Goods Games
Public goods games study the incentives of individuals to contribute to a public good and their behaviors in equilibria.
Computer Science and Game Theory
Semi-Supervised First-Person Activity Recognition in Body-Worn Video
Body-worn cameras are now commonly used for logging daily life, sports, and law enforcement activities, creating a large volume of archived footage.
Multivariate Spatiotemporal Hawkes Processes and Network Reconstruction
There is often latent network structure in spatial and temporal data and the tools of network analysis can yield fascinating insights into such data.
Graph-Based Deep Modeling and Real Time Forecasting of Sparse Spatio-Temporal Data
We present a generic framework for spatio-temporal (ST) data modeling, analysis, and forecasting, with a special focus on data that is sparse in both space and time.
Deep Learning for Real-Time Crime Forecasting and its Ternarization
In this work, we first present a proper representation of crime data.
Latent Self-Exciting Point Process Model for Spatial-Temporal Networks
We propose a latent self-exciting point process model that describes geographically distributed interactions between pairs of entities.
