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Found 5 papers, 1 papers with code
Leveraging Learning Metrics for Improved Federated Learning
Currently in the federated setting, no learning schemes leverage the emerging research of explainable artificial intelligence (XAI) in particular the novel learning metrics that help determine how well a model is learning.
Explainable artificial intelligence
Explainable Artificial Intelligence (XAI)
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NoFADE: Analyzing Diminishing Returns on CO2 Investment
Climate change continues to be a pressing issue that currently affects society at-large.
P4AI: Approaching AI Ethics through Principlism
The field of computer vision is rapidly evolving, particularly in the context of new methods of neural architecture design.
CONet: Channel Optimization for Convolutional Neural Networks
To solve this, we introduce an efficient dynamic scaling algorithm -- CONet -- that automatically optimizes channel sizes across network layers for a given CNN.
Reconsidering CO2 emissions from Computer Vision
To address these concerns, we propose adding "enforcement" as a pillar of ethical AI and provide some recommendations for how architecture designers and broader CV community can curb the climate crisis.
