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Found 7 papers, 3 papers with code
Is forgetting less a good inductive bias for forward transfer?
We argue that the measure of forward transfer to a task should not be affected by the restrictions placed on the continual learner in order to preserve knowledge of previous tasks.
Architecture Matters in Continual Learning
However, in this work, we show that the choice of architecture can significantly impact the continual learning performance, and different architectures lead to different trade-offs between the ability to remember previous tasks and learning new ones.
A Response to Economics as Gauge Theory
We provide an analysis of the recent work by Malaney-Weinstein on "Economics as Gauge Theory" presented on November 10, 2021 at the Money and Banking Workshop hosted by University of Chicago.
Dataset Distillation with Infinitely Wide Convolutional Networks
The effectiveness of machine learning algorithms arises from being able to extract useful features from large amounts of data.
Dataset Meta-Learning from Kernel-Ridge Regression
One of the most fundamental aspects of any machine learning algorithm is the training data used by the algorithm.
Dataset Meta-Learning from Kernel Ridge-Regression
One of the most fundamental aspects of any machine learning algorithm is the training data used by the algorithm.
Measuring Calibration in Deep Learning
In this paper, we perform a comprehensive empirical study of choices in calibration measures including measuring all probabilities rather than just the maximum prediction, thresholding probability values, class conditionality, number of bins, bins that are adaptive to the datapoint density, and the norm used to compare accuracies to confidences.
