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Found 17 papers, 2 papers with code
Fast, Flexible Models for Discovering Topic Correlation across Weakly-Related Collections
Weak topic correlation across document collections with different numbers of topics in individual collections presents challenges for existing cross-collection topic models.
The Wisdom of Polarized Crowds
Our analysis then reveals that polarized teams---those consisting of a balanced set of politically diverse editors---create articles of higher quality than politically homogeneous teams.
Adaptive Sampling for Coarse Ranking
We consider the problem of active coarse ranking, where the goal is to sort items according to their means into clusters of pre-specified sizes, by adaptively sampling from their reward distributions.
Curvature of Hypergraphs via Multi-Marginal Optimal Transport
We introduce a novel definition of curvature for hypergraphs, a natural generalization of graphs, by introducing a multi-marginal optimal transport problem for a naturally defined random walk on the hypergraph.
Wasserstein Soft Label Propagation on Hypergraphs: Algorithm and Generalization Error Bounds
Inspired by recent interests of developing machine learning and data mining algorithms on hypergraphs, we investigate in this paper the semi-supervised learning algorithm of propagating "soft labels" (e. g. probability distributions, class membership scores) over hypergraphs, by means of optimal transportation.
Science and Technology Advance through Surprise
Breakthrough discoveries and inventions involve unexpected combinations of contents including problems, methods, and natural entities, and also diverse contexts such as journals, subfields, and conferences.
Long-range Event-level Prediction and Response Simulation for Urban Crime and Global Terrorism with Granger Networks
Large-scale trends in urban crime and global terrorism are well-predicted by socio-economic drivers, but focused, event-level predictions have had limited success.
Information Lattice Learning
ILL focuses on explainability and generalizability from "small data", and aims for rules akin to those humans distill from experience (rather than a representation optimized for a specific task like classification).
Accelerating science with human versus alien artificial intelligences
These AI approaches typically ignore the distribution of human prediction engines -- scientists and inventor -- who continuously alter the landscape of discovery and invention.
Semantic maps and metrics for science Semantic maps and metrics for science using deep transformer encoders
Finally, with the goal of leveraging contextual representations from deep encoders, we present a range of measurements for understanding and forecasting research communities in science.
Complementary artificial intelligence designed to augment human discovery
When we evaluate the promise of our predictions with first-principles equations, we demonstrate that increased complementarity of our predictions does not decrease and in some cases increases the probability that the predictions possess the targeted properties.
Accelerating science with human-aware artificial intelligence
Artificial intelligence (AI) models trained on published scientific findings have been used to invent valuable materials and targeted therapies, but they typically ignore the human scientists who continually alter the landscape of discovery.
The Future of Fundamental Science Led by Generative Closed-Loop Artificial Intelligence
Yet, AI has contributed less to fundamental science in part because large data sets of high-quality data for scientific practice and model discovery are more difficult to access.
Evolving AI Collectives to Enhance Human Diversity and Enable Self-Regulation
Large language models steer their behaviors based on texts generated by others.
Aligning Multidimensional Worldviews and Discovering Ideological Differences
The Internet is home to thousands of communities, each with their own unique worldview and associated ideological differences.
