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Found 5 papers, 1 papers with code
Understanding Visual Feature Reliance through the Lens of Complexity
Third, we investigate where within the network simple and complex features flow, and find that simpler features tend to bypass the visual hierarchy via residual connections.
Towards Responsible Development of Generative AI for Education: An Evaluation-Driven Approach
A major challenge facing the world is the provision of equitable and universal access to quality education.
Learned feature representations are biased by complexity, learning order, position, and more
We find that their learned feature representations are systematically biased towards representing some features more strongly than others, depending upon extraneous properties such as feature complexity, the order in which features are learned, and the distribution of features over the inputs.
Self-Refine: Iterative Refinement with Self-Feedback
Motivated by how humans refine their written text, we introduce Self-Refine, an approach for improving initial outputs from LLMs through iterative feedback and refinement.
Data for free: Fewer-shot algorithm learning with parametricity data augmentation
We address the problem of teaching an RNN to approximate list-processing algorithms given a small number of input-output training examples.
