Search Results for author:
Found 15 papers, 3 papers with code
Emergent Analogical Reasoning in Large Language Models
In human cognition, this capacity is closely tied to an ability to reason by analogy.
Learning Perceptual Inference by Contrasting
"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 crucial factor in the intellectual history of technology development.
CX-ToM: Counterfactual Explanations with Theory-of-Mind for Enhancing Human Trust in Image Recognition Models
More concretely, our CX-ToM framework generates sequence of explanations in a dialog by mediating the differences between the minds of machine and human user.
Functional form of motion priors in human motion perception
We then measured human performance for motion tasks and found that we obtained better fit for the L1-norm (Laplace) than for the L2-norm (Gaussian).
A unified model of short-range and long-range motion perception
The human vision system is able to effortlessly perceive both short-range and long-range motion patterns in complex dynamic scenes.
Modeling the spacing effect in sequential category learning
We develop a Bayesian sequential model for category learning.
Model selection and velocity estimation using novel priors for motion patterns
Psychophysical experiments show that humans are better at perceiving rotation and expansion than translation.
The Noisy-Logical Distribution and its Application to Causal Inference
We describe a novel noisy-logical distribution for representing the distribution of a binary output variable conditioned on multiple binary input variables.
X-ToM: Explaining with Theory-of-Mind for Gaining Justified Human Trust
We present a new explainable AI (XAI) framework aimed at increasing justified human trust and reliance in the AI machine through explanations.
Action Recognition
Explainable Artificial Intelligence (XAI)
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Theory-based Causal Transfer: Integrating Instance-level Induction and Abstract-level Structure Learning
Given these general theories, the goal is to train an agent by interactively exploring the problem space to (i) discover, form, and transfer useful abstract and structural knowledge, and (ii) induce useful knowledge from the instance-level attributes observed in the environment.
Show Me What You Can Do: Capability Calibration on Reachable Workspace for Human-Robot Collaboration
Our experiments with human participants demonstrate that a short calibration using REMP can effectively bridge the gap between what a non-expert user thinks a robot can reach and the ground truth.
Probabilistic Analogical Mapping with Semantic Relation Networks
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 their relations across analogs.
Visual analogy: Deep learning versus compositional models
We compared human performance to that of two recent deep learning models (Siamese Network and Relation Network) directly trained to solve these analogy problems, as well as to that of a compositional model that assesses relational similarity between part-based representations.
Zero-shot visual reasoning through probabilistic analogical mapping
Human reasoning is grounded in an ability to identify highly abstract commonalities governing superficially dissimilar visual inputs.
Evidence from counterfactual tasks supports emergent analogical reasoning in large language models
We recently reported evidence that large language models are capable of solving a wide range of text-based analogy problems in a zero-shot manner, indicating the presence of an emergent capacity for analogical reasoning.
