Results 41 to 50 of about 9,899,096 (370)
Robust deep learning based protein sequence design using ProteinMPNN
bioRxiv, 2022 While deep learning has revolutionized protein structure prediction, almost all experimentally characterized de novo protein designs have been generated using physically based approaches such as Rosetta.
J. Dauparas +21 more
semanticscholar +1 more source
Building Program Vector Representations for Deep Learning [PDF]
, 2014 Deep learning has made significant breakthroughs in various fields of artificial intelligence. Advantages of deep learning include the ability to capture highly complicated features, weak involvement of human engineering, etc.
Jin, Zhi +6 more
core +1 more source
ESANN 2021 proceedings, 2021 Deep learning for graphs encompasses all those neural models endowed with multiple layers of computation operating on data represented as graphs. The most common building blocks of these models are graph encoding layers, which compute a vector embedding for each node in a graph using message-passing operators.
Bacciu, Davide +3 more
openaire +3 more sources
Distributed Deep Reinforcement Learning: A Survey and A Multi-Player Multi-Agent Learning Toolbox [PDF]
Machine Intelligence Research, 2024
(https://link.springer.com/article/10.1007/s11633-023-1454-4), 2022 With the breakthrough of AlphaGo, deep reinforcement learning becomes a recognized technique for solving sequential decision-making problems. Despite its reputation, data inefficiency caused by its trial and error learning mechanism makes deep reinforcement learning hard to be practical in a wide range of areas.
arxiv +1 more source
Deep learning for time series classification: a review [PDF]
Data mining and knowledge discovery, 2018 Time Series Classification (TSC) is an important and challenging problem in data mining. With the increase of time series data availability, hundreds of TSC algorithms have been proposed.
Hassan Ismail Fawaz +4 more
semanticscholar +1 more source
Scaling deep learning for materials discovery
Nature, 2023 Novel functional materials enable fundamental breakthroughs across technological applications from clean energy to information processing^ 1 – 11 . From microchips to batteries and photovoltaics, discovery of inorganic crystals has been bottlenecked by ...
Amil Merchant +5 more
semanticscholar +1 more source
Deep Learning for Person Re-Identification: A Survey and Outlook [PDF]
IEEE Transactions on Pattern Analysis and Machine Intelligence, 2020 Person re-identification (Re-ID) aims at retrieving a person of interest across multiple non-overlapping cameras. With the advancement of deep neural networks and increasing demand of intelligent video surveillance, it has gained significantly increased ...
Mang Ye +5 more
semanticscholar +1 more source
Question Classification in Question Answering System using Combination of Ensemble Classification and Feature Selection [PDF]
Journal of Artificial Intelligence and Data Mining, 2022 A Question Answering System (QAS) is a special form of information retrieval which consists of three parts: question processing, information retrieval, and answer selection. Determining the type of question is the most important part of QAS as it affects
Sh. Golzari +4 more
doaj +1 more source
Energy and Policy Considerations for Deep Learning in NLP [PDF]
Annual Meeting of the Association for Computational Linguistics, 2019 Recent progress in hardware and methodology for training neural networks has ushered in a new generation of large networks trained on abundant data. These models have obtained notable gains in accuracy across many NLP tasks.
Emma Strubell +2 more
semanticscholar +1 more source
De Computis, 2023 In recent years, deep learning (DL) has been the most popular computational approach in the field of machine learning (ML), achieving exceptional results on a variety of complex cognitive tasks, matching or even surpassing human performance.
Mohammad Mustafa Taye
semanticscholar +1 more source