AIMC Topic: Language

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SGORNN: Combining scalar gates and orthogonal constraints in recurrent networks.

Neural networks : the official journal of the International Neural Network Society
Recurrent Neural Network (RNN) models have been applied in different domains, producing high accuracies on time-dependent data. However, RNNs have long suffered from exploding gradients during training, mainly due to their recurrent process. In this ...

No need to forget, just keep the balance: Hebbian neural networks for statistical learning.

Cognition
Language processing in humans has long been proposed to rely on sophisticated learning abilities including statistical learning. Endress and Johnson (E&J, 2021) recently presented a neural network model for statistical learning based on Hebbian learn...

Human-level play in the game of by combining language models with strategic reasoning.

Science (New York, N.Y.)
Despite much progress in training artificial intelligence (AI) systems to imitate human language, building agents that use language to communicate intentionally with humans in interactive environments remains a major challenge. We introduce Cicero, t...

An imConvNet-based deep learning model for Chinese medical named entity recognition.

BMC medical informatics and decision making
BACKGROUND: With the development of current medical technology, information management becomes perfect in the medical field. Medical big data analysis is based on a large amount of medical and health data stored in the electronic medical system, such...

MLM-based typographical error correction of unstructured medical texts for named entity recognition.

BMC bioinformatics
BACKGROUND: Unstructured text in medical records, such as Electronic Health Records, contain an enormous amount of valuable information for research; however, it is difficult to extract and structure important information because of frequent typograp...

Collectively encoding protein properties enriches protein language models.

BMC bioinformatics
Pre-trained natural language processing models on a large natural language corpus can naturally transfer learned knowledge to protein domains by fine-tuning specific in-domain tasks. However, few studies focused on enriching such protein language mod...

Contrastive language and vision learning of general fashion concepts.

Scientific reports
The steady rise of online shopping goes hand in hand with the development of increasingly complex ML and NLP models. While most use cases are cast as specialized supervised learning problems, we argue that practitioners would greatly benefit from gen...

Systematic tissue annotations of genomics samples by modeling unstructured metadata.

Nature communications
There are currently >1.3 million human -omics samples that are publicly available. This valuable resource remains acutely underused because discovering particular samples from this ever-growing data collection remains a significant challenge. The maj...

Exploiting Textual Information for Fake News Detection.

International journal of neural systems
"Fake news" refers to the deliberate dissemination of news with the purpose to deceive and mislead the public. This paper assesses the accuracy of several Machine Learning (ML) algorithms, using a style-based technique that relies on textual informat...

Biomedical named entity recognition with the combined feature attention and fully-shared multi-task learning.

BMC bioinformatics
BACKGROUND: Biomedical named entity recognition (BioNER) is a basic and important task for biomedical text mining with the purpose of automatically recognizing and classifying biomedical entities. The performance of BioNER systems directly impacts do...