New NLP News:

ML on code, Understanding RNNs, Deep Latent Variable Models, Writing Code for NLP Research, Quo vadis, NLP?, Democratizing AI, ML Cheatsheets, Spinning Up in Deep RL, Papers with Code, Unsupervised MT, Multilingual BERT

#NLP #ML #DL #Training #RNN #RL

🌎 Link Review


🗣 @AI_Python_Arxiv
✴️ @AI_Python_EN
❇️ @AI_Python

The Unreasonable Effectiveness of Recurrent Neural Networks

Blog (2015) by Andrej Karpathy: https://lnkd.in/eNC7BK5

#DeepLearning #NeuralNetworks #RecurrentNeuralNetworks #RNN


❇️ @AI_Python_EN
🗣 @AI_Python_arXiv
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Predicting Aircraft Trajectories: A Deep Generative Convolutional Recurrent Neural Networks Approach.
http://arxiv.org/abs/1812.11670
#RNN #ML

✴️ @AI_Python_EN
🗣 @AI_Python_Arxiv

Fundamentals of Recurrent Neural Network (RNN) and Long Short-Term Memory (LSTM) Network

Paper by Alex Sherstinsky: https://lnkd.in/enTMCDH

#RecurrentNeuralNetwork #RNN #LongShortTermMemory #LSTM #NeuralNetworks

🗣 @AI_Python_Arxiv
✴️ @AI_Python_EN

In a paper published two days ago in Nature, a group of scientists designed a recurrent neural network that decoded cortical signals to speech signals.
This problem considered much harder than decoding muscle movement from brain signal as the signals that responsible for spoken words are much difficult to decode.
Nature (paywall): https://lnkd.in/fM8EsuE
direct link to pdf: https://lnkd.in/ftrEbe5
#ai #neuralnetwork #science #rnn #neuroscience

✴️ @AI_Python_EN

Speech-to-Text using Convolutional Neural Networks
#CNN
Deep Learning beginners quickly learn that Recurrent Neural Network ( #RNN s) are for building models for sequential data tasks (such as language translation) whereas Convolutional Neural Networks (CNNs) are for image and video related tasks. This is a pretty good thumb rule - but recent work at Facebook has shown some great results for sequential data just by using CNNs.

✴️ @AI_Python_EN

Building a #Conversational #AI #Agent for medical and healthcare services is one of the products in our pipeline in the coming months.

Here is how a typical chatbot recirculation recurrent #pipeline looks like

#CNN #RNN #GAN #DeepLearning #NLP

✴️ @AI_Python_EN

Walkthrough - When and How to Use MLP, CNN, and RNN Neural Networks - Jason Brownlee

To follow posts: https://lnkd.in/ev9S2hh

#machinelearning #artificialintelligence #datascience #ml #ai #deeplearning #MLP #CNN #RNN #neuralnetworks

✴️ @AI_Python_EN

A Pattern-Based Method for Medical Entity Recognition From Chinese Diagnostic Imaging Text

The identification of medical entities and relations from electronic medical records is a fundamental research issue for medical informatics. However, the task of extracting valuable knowledge from these records is challenging due to its high complexity.

The method proves to be stable and robust with different amounts of testing data. It achieves a comparatively high performance in the CHIP 2018 open challenge, demonstrating its effectiveness in extracting tumor-related entities from Chinese diagnostic imaging text.

Paper: https://lnkd.in/g3uezmR
#LSTM #RNN #deeplearning #healthcare #algorithm

✴️ @AI_Python_EN

Really awesome #deeplearning #rnn paper 👏🏻 explaining an increase in predicted risk for clinical alerts.

Explanations in a temporal setting where a stateful dynamical model produces a sequence of risk estimates given an input at each time step.

The goal here is to alert a clinician when a patient’s risk of deterioration rises. The clinician then has to decide whether to intervene and adjust the treatment.

Given a potentially long sequence of new events since she last saw the patient, a concise explanation helps her to quickly triage the alert.

Authors developed methods to lift static attribution techniques to the dynamical setting, where they identified and addressed challenges specific to dynamics.

They then experimentally assess the utility of different explanations of clinical alerts through expert evaluation.

Here’s full paper: https://lnkd.in/duMQYyW
#healthcare #diagnostics #clinical
#prediction
✴️ @AI_Python_EN

Have you heard of "R-Transformer?", a Recurrent Neural Network Enhanced Transformer

Recurrent Neural Networks have long been the dominating choice for sequence modeling. However, it severely suffers from two issues: impotent in capturing very long-term dependencies and unable to parallelize the sequential computation procedure.

Therefore, many non-recurrent sequence models that are built on convolution and attention operations have been proposed recently.

Here the authors propose the R-Transformer which enjoys the advantages of both RNNs and the multi-head attention mechanism while avoids their respective drawbacks.

The proposed model can effectively capture both local structures and global long-term dependencies in sequences without any use of position embeddings. They evaluated R-Transformer through extensive experiments with data from a wide range of domains and the empirical results show that R-Transformer outperforms the state-of-the-art methods by a large margin in most of the tasks.

Github code: https://lnkd.in/dpFckix

#research #algorithms #machinelearning #deeplearning #rnn

✴️ @AI_Python_EN

Abnormal respiratory patterns classifier may contribute to large-scale screening of people infected with COVID-19 in an accurate and unobtrusive manner.

abs: https://arxiv.org/abs/2002.05534v1

#rnn #machinelearning #ArtificialIntelligence #DeepLearning #

❇️ @AI_Python_EN