40 citations · 93 across the 10 of their papers we have counts for
10 papers
Emergent Analogical Reasoning in Large Language Models
Taylor Webb, Keith J. Holyoak, Hongjing Lu
The recent advent of large language models has reinvigorated debate over whether human cognitive capacities might emerge in such generic models given sufficient training data. Of p…
Zero-shot visual reasoning through probabilistic analogical mapping
Taylor W. Webb, Shuhao Fu, Trevor Bihl +2
Human reasoning is grounded in an ability to identify highly abstract commonalities governing superficially dissimilar visual inputs. Recent efforts to develop algorithms with this…
CX-ToM: Counterfactual Explanations with Theory-of-Mind for Enhancing Human Trust in Image Recognition Models
Arjun R. Akula, Keze Wang, Changsong Liu +5
We propose CX-ToM, short for counterfactual explanations with theory-of mind, a new explainable AI (XAI) framework for explaining decisions made by a deep convolutional neural netw…
Visual analogy: Deep learning versus compositional models
Nicholas Ichien, Qing Liu, Shuhao Fu +3
Is analogical reasoning a task that must be learned to solve from scratch by applying deep learning models to massive numbers of reasoning problems? Or are analogies solved by comp…
Mapping Moral Valence of Tweets Following the Killing of George Floyd
J. Hunter Priniski, Negar Mokhberian, Bahareh Harandizadeh +4
The viral video documenting the killing of George Floyd by Minneapolis police officer Derek Chauvin inspired nation-wide protests that brought national attention to widespread raci…
Probabilistic Analogical Mapping with Semantic Relation Networks
Hongjing Lu, Nicholas Ichien, Keith J. Holyoak
The human ability to flexibly reason using analogies with domain-general content depends on mechanisms for identifying relations between concepts, and for mapping concepts and thei…
Show Me What You Can Do: Capability Calibration on Reachable Workspace for Human-Robot Collaboration
Xiaofeng Gao, Luyao Yuan, Tianmin Shu +2
Aligning humans' assessment of what a robot can do with its true capability is crucial for establishing a common ground between human and robot partners when they collaborate on a…
Learning Perceptual Inference by Contrasting
Chi Zhang, Baoxiong Jia, Feng Gao +3
"Thinking in pictures," [1] i.e., spatial-temporal reasoning, effortless and instantaneous for humans, is believed to be a significant ability to perform logical induction and a cr…
Theory-based Causal Transfer: Integrating Instance-level Induction and Abstract-level Structure Learning
Mark Edmonds, Xiaojian Ma, Siyuan Qi +3
Learning transferable knowledge across similar but different settings is a fundamental component of generalized intelligence. In this paper, we approach the transfer learning chall…
X-ToM: Explaining with Theory-of-Mind for Gaining Justified Human Trust
Arjun R. Akula, Changsong Liu, Sari Saba-Sadiya +4
We present a new explainable AI (XAI) framework aimed at increasing justified human trust and reliance in the AI machine through explanations. We pose explanation as an iterative c…