The experimentalist's guide to machine learning for small molecule design
Initially part of the field of artificial intelligence, machine learning (ML) has become a
booming research area since branching out into its own field in the 1990s. After three …
booming research area since branching out into its own field in the 1990s. After three …
Do RNN and LSTM have long memory?
The LSTM network was proposed to overcome the difficulty in learning long-term
dependence, and has made significant advancements in applications. With its success and …
dependence, and has made significant advancements in applications. With its success and …
Automated labelling of radiology reports using natural language processing: Comparison of traditional and newer methods
SY Chng, PJW Tern, MRX Kan… - Health Care …, 2023 - Wiley Online Library
Automated labelling of radiology reports using natural language processing allows for the
labelling of ground truth for large datasets of radiological studies that are required for …
labelling of ground truth for large datasets of radiological studies that are required for …
Can sgd learn recurrent neural networks with provable generalization?
Z Allen-Zhu, Y Li - Advances in Neural Information …, 2019 - proceedings.neurips.cc
Abstract Recurrent Neural Networks (RNNs) are among the most popular models in
sequential data analysis. Yet, in the foundational PAC learning language, what concept …
sequential data analysis. Yet, in the foundational PAC learning language, what concept …
[HTML][HTML] Splice-site identification for exon prediction using bidirectional LSTM-RNN approach
N Singh, R Nath, DB Singh - Biochemistry and Biophysics Reports, 2022 - Elsevier
Abstract Machine learning methods played a major role in improving the accuracy of
predictions and classification of DNA (Deoxyribonucleic Acid) and protein sequences. In …
predictions and classification of DNA (Deoxyribonucleic Acid) and protein sequences. In …
Understanding the property of long term memory for the LSTM with attention mechanism
Recent trends of incorporating LSTM network with different attention mechanisms in time
series forecasting have led researchers to consider the attention module as an essential …
series forecasting have led researchers to consider the attention module as an essential …
State-regularized recurrent neural networks
Recurrent neural networks are a widely used class of neural architectures with two
shortcomings. First, it is difficult to understand what exactly they learn. Second, they tend to …
shortcomings. First, it is difficult to understand what exactly they learn. Second, they tend to …
Deep-sdm: A unified computational framework for sequential data modeling using deep learning models
Deep-SDM is a unified layer framework built on TensorFlow/Keras and written in Python
3.12. The framework aligns with the modular engineering principles for the design and …
3.12. The framework aligns with the modular engineering principles for the design and …
Amrl: Aggregated memory for reinforcement learning
In many partially observable scenarios, Reinforcement Learning (RL) agents must rely on
long-term memory in order to learn an optimal policy. We demonstrate that using techniques …
long-term memory in order to learn an optimal policy. We demonstrate that using techniques …
Aggregating frame-level features for large-scale video classification
This paper introduces the system we developed for the Google Cloud & YouTube-8M Video
Understanding Challenge, which can be considered as a multi-label classification problem …
Understanding Challenge, which can be considered as a multi-label classification problem …