In this paper, we propose a new peptide data augmentation scheme, where we train peptide language models on artificially constructed peptides that are small contiguous subsets of longer, wild-type proteins; we refer to the training peptides as "chopped proteins".
We propose a method to retrieve similar items, based on a query item image and textual refinement properties.
Recurrent Neural Networks (RNNs) have had considerable success in classifying and predicting sequences.
In this work, we are using the Fisher Vector as a sentence representation by pooling the word2vec embedding of each word in the sentence.
Ranked #14 on Video Retrieval on YouCook2
The second Mixture Model presented is a Hybrid Gaussian-Laplacian Mixture Model (HGLMM) which is based on a weighted geometric mean of the Gaussian and Laplacian distribution.