AIMC Topic: Generalization, Psychological

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Derivational morphology reveals analogical generalization in large language models.

Proceedings of the National Academy of Sciences of the United States of America
What mechanisms underlie linguistic generalization in large language models (LLMs)? This question has attracted considerable attention, with most studies analyzing the extent to which the language skills of LLMs resemble rules. As of yet, it is not k...

Generalization analysis of adversarial pairwise learning.

Neural networks : the official journal of the International Neural Network Society
Adversarial pairwise learning has become the predominant method to enhance the discrimination ability of models against adversarial attacks, achieving tremendous success in various application fields. Despite excellent empirical performance, adversar...

Generalization limits of Graph Neural Networks in identity effects learning.

Neural networks : the official journal of the International Neural Network Society
Graph Neural Networks (GNNs) have emerged as a powerful tool for data-driven learning on various graph domains. They are usually based on a message-passing mechanism and have gained increasing popularity for their intuitive formulation, which is clos...

Mask-Shift-Inference: A novel paradigm for domain generalization.

Neural networks : the official journal of the International Neural Network Society
Domain Generalization (DG) focuses on the Out-Of-Distribution (OOD) generalization, which is able to learn a robust model that generalizes the knowledge acquired from the source domain to the unseen target domain. However, due to the existence of the...

GCReID: Generalized continual person re-identification via meta learning and knowledge accumulation.

Neural networks : the official journal of the International Neural Network Society
Person re-identification (ReID) has made good progress in stationary domains. The ReID model must be retrained to adapt to new scenarios (domains) as they emerge unexpectedly, which leads to catastrophic forgetting. Continual learning trains the mode...

A mathematical theory of relational generalization in transitive inference.

Proceedings of the National Academy of Sciences of the United States of America
Humans and animals routinely infer relations between different items or events and generalize these relations to novel combinations of items. This allows them to respond appropriately to radically novel circumstances and is fundamental to advanced co...

Representations and generalization in artificial and brain neural networks.

Proceedings of the National Academy of Sciences of the United States of America
Humans and animals excel at generalizing from limited data, a capability yet to be fully replicated in artificial intelligence. This perspective investigates generalization in biological and artificial deep neural networks (DNNs), in both in-distribu...

Land-use classification based on high-resolution remote sensing imagery and deep learning models.

PloS one
High-resolution imagery and deep learning models have gained increasing importance in land-use mapping. In recent years, several new deep learning network modeling methods have surfaced. However, there has been a lack of a clear understanding of the ...

Small object detection algorithm incorporating swin transformer for tea buds.

PloS one
Accurate identification of small tea buds is a key technology for tea harvesting robots, which directly affects tea quality and yield. However, due to the complexity of the tea plantation environment and the diversity of tea buds, accurate identifica...

Can neural networks benefit from objectives that encourage iterative convergent computations? A case study of ResNets and object classification.

PloS one
Recent work has suggested that feedforward residual neural networks (ResNets) approximate iterative recurrent computations. Iterative computations are useful in many domains, so they might provide good solutions for neural networks to learn. However,...