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Found 7 papers, 0 papers with code
A Survey on Multi-Objective based Parameter Optimization for Deep Learning
Multi-objective optimization problems form one of the alternatives yet useful options for parameter optimization.
IV-GNN : Interval Valued Data Handling Using Graph Neural Network
Graph Neural Network (GNN) is a powerful tool to perform standard machine learning on graphs.
Predicting potential drug targets and repurposable drugs for COVID-19 via a deep generative model for graphs
Here, we combine three networks, two of which are year-long curated, and one of which, on SARS-CoV-2-human host-virus protein interactions, was published only most recently (30th of April 2020), raising a novel network that puts drugs, human and virus proteins into mutual context.
Decomposition in Decision and Objective Space for Multi-Modal Multi-Objective Optimization
Prevalent multi-objective evolutionary algorithms are not purely designed to search for multiple solution subsets, whereas, algorithms designed for MMMOPs demonstrate degraded performance in the objective space.
A Many Objective Optimization Approach for Transfer Learning in EEG Classification
This work aims at knowledge transfer to classify EEG of the target subject using a classifier trained with the EEG of another unit source subject.
conLSH: Context based Locality Sensitive Hashing for Mapping of noisy SMRT Reads
To deal with the high error probability of SMRT data, a novel contextual Locality Sensitive Hashing (conLSH) based algorithm is proposed in this article, which can effectively align the noisy SMRT reads to the reference genome.
An Improved Video Analysis using Context based Extension of LSH
In this article, a novel scheme of Context based Locality Sensitive Hashing (conLSH) has been introduced, in which points are hashed together not only based on their closeness, but also because of similar context.
