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Found 6 papers, 3 papers with code
Ankh: Optimized Protein Language Model Unlocks General-Purpose Modelling
As opposed to scaling-up protein language models (PLMs), we seek improving performance via protein-specific optimization.
CodeTrans: Towards Cracking the Language of Silicon's Code Through Self-Supervised Deep Learning and High Performance Computing
Simultaneously, the transformer model, especially its combination with transfer learning, has been proven to be a powerful technique for natural language processing tasks.
ProtTrans: Towards Cracking the Language of Life's Code Through Self-Supervised Deep Learning and High Performance Computing
Here, we trained two auto-regressive models (Transformer-XL, XLNet) and four auto-encoder models (BERT, Albert, Electra, T5) on data from UniRef and BFD containing up to 393 billion amino acids.
Ranked #1 on
Protein Secondary Structure Prediction
on CASP12
Dimensionality Reduction
Protein Secondary Structure Prediction
Multi-Task Deep Learning for Legal Document Translation, Summarization and Multi-Label Classification
A potential solution to this problem is multi-task DL to enable transfer learning.
Stop Illegal Comments: A Multi-Task Deep Learning Approach
Deep learning methods are often difficult to apply in the legal domain due to the large amount of labeled data required by deep learning methods.
Named-Entity Linking Using Deep Learning For Legal Documents: A Transfer Learning Approach
However, this paper focuses especially on the aspect of applying transfer learning approach using networks trained for NEL to legal documents.
