Abstract: The engineering of stable proteins is crucial for various industrial purposes. Several machine learning methods have been developed to predict changes in the stability of proteins ...

We study lossless acceleration for seq2seq generation with a novel decoding algorithm — Aggressive Decoding. Unlike the previous efforts (e.g., non-autoregressive decoding) speeding up seq2seq ...

Abstract: In this work, we propose a realistic semantic network called seq2seq-SC, designed to be compatible with 5G NR and capable of working with generalized text datasets using a pre- trained ...

Sequence-to-Sequence (Seq2Seq) models have revolutionised the field of natural language processing and machine translation. These models have the remarkable capability to handle both input and output ...

Sequence to sequence modelling (seq2seq) with neural networks has become the de facto standard for sequence prediction tasks such as those found in language modelling and machine translation. The ...

I've been doing multi-GPU evaluation for some weeks using a Transformers pull from Feb 12th, just using the example scripts for training/evaluating custom datasets (specifically ...