7.8k citations · 7.9k across the 7 of their papers we have counts for
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cs.LG2019★ 4 cited
An empirical study of pretrained representations for few-shot classification
Tiago Ramalho, Thierry Sousbie, Stefano Peluchetti
Recent algorithms with state-of-the-art few-shot classification results start their procedure by computing data features output by a large pretrained model. In this paper we system…
cs.LG2019
Density estimation in representation space to predict model uncertainty
Tiago Ramalho, Miguel Miranda
Deep learning models frequently make incorrect predictions with high confidence when presented with test examples that are not well represented in their training dataset. We propos…
cs.LG2019★ 10 cited
Adaptive Posterior Learning: few-shot learning with a surprise-based memory module
Tiago Ramalho, Marta Garnelo
The ability to generalize quickly from few observations is crucial for intelligent systems. In this paper we introduce APL, an algorithm that approximates probability distributions…