Results 141 to 150 of about 8,974 (165)

Recovering time-varying networks from single-cell data. [PDF]

Bioinformatics
Hasanaj E, Póczos B, Bar-Joseph Z.
europepmc   +1 more source

Explainable Multitask Burnout Prediction Using Adaptive Deep Learning (EMBRACE) for Resident Physicians: Algorithm Development and Validation Study. [PDF]

JMIR AI
Alam S, Alam MAU.
europepmc   +1 more source

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Triplet MAML for Few-Shot Classification Problems

2023
In this study, we propose a TripletMAML algorithm as an extension to Model-Agnostic Meta-Learning (MAML) which is the most widely-used optimization-based meta-learning algorithm. We approach MAML from a metric-learning perspective and train it using meta-learning tasks composed of triplets of images.
Ayla Gülcü, İsmail Taha Samed Özkan, Zeki Kuş, Osman Furkan Karakuş   +3 more
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Investigating Parallelization of MAML

2020
We propose a meta-learning framework to distribute Model-Agnostic Meta-Learning (DMAML), a widely used meta-learning algorithm, over multiple workers running in parallel. DMAML enables us to use multiple servers for learning and might be crucial if we want to tackle more challenging problems that often require more CPU time for simulation. In this work,
Jan Bollenbacher, Florian Soulier, Beate Rhein, Laurenz Wiskott   +3 more
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Business apps with MAML

Proceedings of the Symposium on Applied Computing, 2017
Business apps support the digitalization of business operations by utilizing the potential of ubiquitous mobile devices. Whereas many frameworks for programming cross-platform apps exist, few modeling approaches focus on platform-agnostic representations of mobile apps.
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Automated Trading Experiments with Maml

2002
MAML (Multi-Agent Modeling Language) is a macro-language for Swarm. Its aim is to ease the creation of the most common set of agent-based models by providing a couple of high level constructs and structures in the form of specialized keywords. In this paper we introduce the concepts of MAML through an extension of Chris Preist’s auction model on ...
László Gulyás, Tibor Vincze
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Investigating MAML and ProtoNet Algorithms for Few-shot Learning Problems

2020
Deep neural networks have proven to be very effective for image-related problems. However, their success is mainly attributed to the large-scale annotated datasets that have been used to train them. Convolutional neural networks which are special type of neural networks have achieved very good results for visual recognition problems and therefore have ...
GÜLCÜ, Ayla, ALKAN, Muhammet
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Few-shot Learning for NER using MAML

VCU Theses and ...
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