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Found 4 papers, 1 papers with code
Language models scale reliably with over-training and on downstream tasks
We fit scaling laws that extrapolate in both the number of model parameters and the ratio of training tokens to parameters.
Interpretable Machine Learning: Moving From Mythos to Diagnostics
Despite increasing interest in the field of Interpretable Machine Learning (IML), a significant gap persists between the technical objectives targeted by researchers' methods and the high-level goals of consumers' use cases.
A Learning Theoretic Perspective on Local Explainability
In this paper, we explore connections between interpretable machine learning and learning theory through the lens of local approximation explanations.
BIG-bench Machine Learning
Interpretable Machine Learning
+1
Differentially Private Meta-Learning
Parameter-transfer is a well-known and versatile approach for meta-learning, with applications including few-shot learning, federated learning, and reinforcement learning.
