18 citations · 41 across the 15 of their papers we have counts for
Showing 2016 · stat.MLShow all
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stat.ML2016★ 4 cited
Tunable Sensitivity to Large Errors in Neural Network Training
Gil Keren, Sivan Sabato, Björn Schuller
When humans learn a new concept, they might ignore examples that they cannot make sense of at first, and only later focus on such examples, when they are more useful for learning.…
stat.ML2016★ 18 cited
Convolutional RNN: an Enhanced Model for Extracting Features from Sequential Data
Gil Keren, Björn Schuller
Traditional convolutional layers extract features from patches of data by applying a non-linearity on an affine function of the input. We propose a model that enhances this feature…