Learning molecular dynamics with simple language model built upon long short-term memory neural network
نویسندگان
چکیده
منابع مشابه
Multi-stream long short-term memory neural network language model
Long Short-Term Memory (LSTM) neural networks are recurrent neural networks that contain memory units that can store contextual information from past inputs for arbitrary amounts of time. A typical LSTM neural network language model is trained by feeding an input sequence. i.e., a stream of words, to the input layer of the network and the output layer predicts the probability of the next word g...
متن کاملReinforcement Learning with Long Short-Term Memory
This paper presents reinforcement learning with a Long ShortTerm Memory recurrent neural network: RL-LSTM. Model-free RL-LSTM using Advantage( ) learning and directed exploration can solve non-Markovian tasks with long-term dependencies between relevant events. This is demonstrated in a T-maze task, as well as in a di cult variation of the pole balancing task.
متن کاملPrediction of Covid-19 Prevalence and Fatality Rates in Iran Using Long Short-Term Memory Neural Network
Introduction: The rapid spread of COVID-19 has become a critical threat to the world. So far, millions of people worldwide have been infected with the disease. The Covid-19 pandemic has had significant effects on various aspects of human life. Currently, prediction of the virus's spread is essential in order to be safe and make necessary arrangements. It can help control the rate of its outbrea...
متن کاملPrediction of Covid-19 Prevalence and Fatality Rates in Iran Using Long Short-Term Memory Neural Network
Introduction: The rapid spread of COVID-19 has become a critical threat to the world. So far, millions of people worldwide have been infected with the disease. The Covid-19 pandemic has had significant effects on various aspects of human life. Currently, prediction of the virus's spread is essential in order to be safe and make necessary arrangements. It can help control the rate of its outbrea...
متن کاملFrom Recurrent Neural Network to Long Short Term Memory Architecture
Despite more than 30 years of handwriting recognition research, Recognizing the unconstrained sequence is still a challenge task. The difficulty of segmenting cursive script has led to the low recognition rate. Hidden Markov Models (HMMs) are considered as state-of-theart methods for performing non-constrained handwriting recognition. However, HMMs have several well-known drawbacks. One of thes...
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: Nature Communications
سال: 2020
ISSN: 2041-1723
DOI: 10.1038/s41467-020-18959-8