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Found 8 papers, 7 papers with code
OmniPred: Language Models as Universal Regressors
Over the broad landscape of experimental design, regression has been a powerful tool to accurately predict the outcome metrics of a system or model given a set of parameters, but has been traditionally restricted to methods which are only applicable to a specific task.
Variance-Reduced Gradient Estimation via Noise-Reuse in Online Evolution Strategies
Unrolled computation graphs are prevalent throughout machine learning but present challenges to automatic differentiation (AD) gradient estimation methods when their loss functions exhibit extreme local sensitivtiy, discontinuity, or blackbox characteristics.
Two Sides of Meta-Learning Evaluation: In vs. Out of Distribution
We categorize meta-learning evaluation into two settings: $\textit{in-distribution}$ [ID], in which the train and test tasks are sampled $\textit{iid}$ from the same underlying task distribution, and $\textit{out-of-distribution}$ [OOD], in which they are not.
Label Leakage and Protection in Two-party Split Learning
Two-party split learning is a popular technique for learning a model across feature-partitioned data.
Is Support Set Diversity Necessary for Meta-Learning?
Meta-learning is a popular framework for learning with limited data in which an algorithm is produced by training over multiple few-shot learning tasks.
Interpretable Image Recognition with Hierarchical Prototypes
Hence, we may find distinct explanations for the prediction an image receives at each level of the taxonomy.
This Looks Like That: Deep Learning for Interpretable Image Recognition
In this work, we introduce a deep network architecture -- prototypical part network (ProtoPNet), that reasons in a similar way: the network dissects the image by finding prototypical parts, and combines evidence from the prototypes to make a final classification.
Deep Learning for Case-Based Reasoning through Prototypes: A Neural Network that Explains Its Predictions
This architecture contains an autoencoder and a special prototype layer, where each unit of that layer stores a weight vector that resembles an encoded training input.
