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Found 4 papers, 2 papers with code
Heterogeneous Encoders Scaling In The Transformer For Neural Machine Translation
Although the Transformer is currently the best-performing architecture in the homogeneous configuration (self-attention only) in Neural Machine Translation, many State-of-the-Art models in Natural Language Processing are made of a combination of different Deep Learning approaches.
A request for clarity over the End of Sequence token in the Self-Critical Sequence Training
The Image Captioning research field is currently compromised by the lack of transparency and awareness over the End-of-Sequence token (<Eos>) in the Self-Critical Sequence Training.
Exploiting Multiple Sequence Lengths in Fast End to End Training for Image Captioning
We introduce a method called the Expansion mechanism that processes the input unconstrained by the number of elements in the sequence.
Ranked #1 on
Image Captioning
on MS COCO
Exploring the sequence length bottleneck in the Transformer for Image Captioning
Most recent state of the art architectures rely on combinations and variations of three approaches: convolutional, recurrent and self-attentive methods.
