13 citations · 14 across the 8 of their papers we have counts for
10 papers
Muse Glimmer 30B: An On-Device Agentic Model
Arash Ebrahim Abtahizadeh, Muna Aghamelu, Adil Ahmed +327
We introduce Muse Glimmer, an open-source 30B parameter causal language model with perception encoder, purpose-built for autonomous agentic tasks on consumer hardware. Muse Glimmer…
UniqueRank: Identifying Important and Difficult-to-Replace Nodes in Attributed Graphs
Erica Cai, Benjamin A. Miller, Olga Simek +1
Node-ranking methods that focus on structural importance are widely used in a variety of applications, from ranking webpages in search engines to identifying key molecules in biomo…
Identifying and Investigating Global News Coverage of Critical Events Such as Disasters and Terrorist Attacks
Erica Cai, Xi Chen, Reagan Grey Keeney +3
Comparative studies of news coverage are challenging to conduct because methods to identify news articles about the same event in different languages require expertise that is diff…
Understanding the Effect of Knowledge Graph Extraction Error on Downstream Graph Analyses: A Case Study on Affiliation Graphs
Erica Cai, Brendan O'Connor
Knowledge graphs (KGs) are useful for analyzing social structures, community dynamics, institutional memberships, and other complex relationships across domains from sociology to p…
Relation Extraction Across Entire Books to Reconstruct Community Networks: The AffilKG Datasets
Erica Cai, Sean McQuade, Kevin Young +1
When knowledge graphs (KGs) are automatically extracted from text, are they accurate enough for downstream analysis? Unfortunately, current annotated datasets can not be used to ev…
From low resource information extraction to identifying influential nodes in knowledge graphs
Erica Cai, Olga Simek, Benjamin A. Miller +3
We propose a pipeline for identifying important entities from intelligence reports that constructs a knowledge graph, where nodes correspond to entities of fine-grained types (e.g.…
A Monte Carlo Language Model Pipeline for Zero-Shot Sociopolitical Event Extraction
Erica Cai, Brendan O'Connor
Current social science efforts automatically populate event databases of "who did what to whom?" tuples, by applying event extraction (EE) to text such as news. The event databases…
Improving the Efficiency of the PC Algorithm by Using Model-Based Conditional Independence Tests
Erica Cai, Andrew McGregor, David Jensen
Learning causal structure is useful in many areas of artificial intelligence, including planning, robotics, and explanation. Constraint-based structure learning algorithms such as…
Adaptive Selection of the Optimal Strategy to Improve Precision and Power in Randomized Trials
Laura B. Balzer, Erica Cai, Lucas Godoy Garraza +1
Benkeser et al. demonstrate how adjustment for baseline covariates in randomized trials can meaningfully improve precision for a variety of outcome types. Their findings build on a…
Measuring Interventional Robustness in Reinforcement Learning
Katherine Avery, Jack Kenney, Pracheta Amaranath +2
Recent work in reinforcement learning has focused on several characteristics of learned policies that go beyond maximizing reward. These properties include fairness, explainability…