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Found 5 papers, 1 papers with code
Human-Like Geometric Abstraction in Large Pre-trained Neural Networks
Humans possess a remarkable capacity to recognize and manipulate abstract structure, which is especially apparent in the domain of geometry.
Relational Constraints On Neural Networks Reproduce Human Biases towards Abstract Geometric Regularity
Uniquely among primates, humans possess a remarkable capacity to recognize and manipulate abstract structure in the service of task goals across a broad range of behaviors.
The Relational Bottleneck as an Inductive Bias for Efficient Abstraction
A central challenge for cognitive science is to explain how abstract concepts are acquired from limited experience.
Deep Learning for Suicide and Depression Identification with Unsupervised Label Correction
Early detection of suicidal ideation in depressed individuals can allow for adequate medical attention and support, which in many cases is life-saving.
Context Matters: Recovering Human Semantic Structure from Machine Learning Analysis of Large-Scale Text Corpora
Applying machine learning algorithms to large-scale, text-based corpora (embeddings) presents a unique opportunity to investigate at scale how human semantic knowledge is organized and how people use it to judge fundamental relationships, such as similarity between concepts.
