EvoLSTM: context-dependent models of sequence evolution using a sequence-to-sequence LSTM
نویسندگان
چکیده
منابع مشابه
Author Masking using Sequence-to-Sequence Models
The paper describes the approach adopted for Author Masking Task at PAN 2017. For the purpose of masking the original author, we use the combination of methods based either on deep learning approach or traditional methods of obfuscation. We obtain sample of obfuscated sentences from original one and choose best of them using language model. We try to change both the content and length of origin...
متن کاملBidirectional LSTM-CRF Models for Sequence Tagging
In this paper, we propose a variety of Long Short-Term Memory (LSTM) based models for sequence tagging. These models include LSTM networks, bidirectional LSTM (BI-LSTM) networks, LSTM with a Conditional Random Field (CRF) layer (LSTM-CRF) and bidirectional LSTM with a CRF layer (BI-LSTM-CRF). Our work is the first to apply a bidirectional LSTM CRF (denoted as BI-LSTM-CRF) model to NLP benchmark...
متن کاملModels of coding sequence evolution
Probabilistic models of sequence evolution are in widespread use in phylogenetics and molecular sequence evolution. These models have become increasingly sophisticated and combined with statistical model comparison techniques have helped to shed light on how genes and proteins evolve. Models of codon evolution have been particularly useful, because, in addition to providing a significant improv...
متن کاملGoogle's Next-Generation Real-Time Unit-Selection Synthesizer Using Sequence-to-Sequence LSTM-Based Autoencoders
A neural network model that significant improves unitselection-based Text-To-Speech synthesis is presented. The model employs a sequence-to-sequence LSTM-based autoencoder that compresses the acoustic and linguistic features of each unit to a fixed-size vector referred to as an embedding. Unit-selection is facilitated by formulating the target cost as an L2 distance in the embedding space. In o...
متن کاملMultiplicative LSTM for sequence modelling
We introduce multiplicative LSTM (mLSTM), a novel recurrent neural network architecture for sequence modelling that combines the long short-term memory (LSTM) and multiplicative recurrent neural network architectures. mLSTM is characterised by its ability to have different recurrent transition functions for each possible input, which we argue makes it more expressive for autoregressive density ...
متن کاملذخیره در منابع من
با ذخیره ی این منبع در منابع من، دسترسی به آن را برای استفاده های بعدی آسان تر کنید
ژورنال
عنوان ژورنال: Bioinformatics
سال: 2020
ISSN: 1367-4803,1460-2059
DOI: 10.1093/bioinformatics/btaa447