I-BERT: Inductive Generalization of Transformer to Arbitrary Context Lengths. Self-attention has emerged as a vital component of state-of-the-art sequence-to-sequence models for natural language processing in recent years, brought to the forefront by pre-trained bi-directional Transformer models.
Jun 18, 2020
Jun 19, 2020 · The model inductively generalizes on a variety of algorithmic tasks where state-of-the-art Transformer models fail to do so.
I-BERT is proposed, a bi-directional Transformer that replaces positional encodings with a recurrent layer that inductively generalizes on a variety of ...
I-BERT can be simply run from Bash. Below is the most core command line to run I-BERT. python3 AutoEncode.py --net ibert.
Identifying the computational limits of existing self-attention mechanisms, we propose I-BERT, a bi-directional Transformer that replaces positional encodings ...
Hyoungwook Nam, Seung Byum Seo, Vikram Sharma Mailthody, Noor Michael, Lan Li: I-BERT: Inductive Generalization of Transformer to Arbitrary Context Lengths.
The model inductively generalizes on a variety of algorithmic tasks where state-of-the-art Transformer models fail to do so. Language Modelling · Masked ...
Co-authors ; I-BERT: Inductive Generalization of Transformer to Arbitrary Context Lengths. H Nam, SB Seo, VS Mailthody, N Michael, L Li. arXiv preprint arXiv: ...
I-BERT: Inductive Generalization of Transformer to Arbitrary Context Lengths. H Nam, SB Seo, VS Mailthody, N Michael, L Li. arXiv preprint arXiv:2006.10220 ...
The model inductively generalizes on a variety of algorithmic tasks where state-of-the-art Transformer models fail to do so. Language Modelling · Masked ...