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Found 11 papers, 3 papers with code
Online Multi-Task Learning with Recursive Least Squares and Recursive Kernel Methods
This paper introduces two novel approaches for Online Multi-Task Learning (MTL) Regression Problems.
OFA$^2$: A Multi-Objective Perspective for the Once-for-All Neural Architecture Search
This could be achieved by using any multi-objective evolutionary algorithm during the search stage, such as NSGA-II and SMS-EMOA.
New advances in enumerative biclustering algorithms with online partitioning
This paper further extends RIn-Close_CVC, a biclustering algorithm capable of performing an efficient, complete, correct and non-redundant enumeration of maximal biclusters with constant values on columns in numerical datasets.
RIn-Close_CVC2: an even more efficient enumerative algorithm for biclustering of numerical datasets
Despite RIn-Close_CVC has all these outstanding properties, it has a high computational cost in terms of memory usage because it must keep a symbol table in memory to prevent a maximal bicluster to be found more than once.
Spatial Projection of Multiple Climate Variables using Hierarchical Multitask Learning
While IPCC has traditionally used a simple model output average, recent work has illustrated potential advantages of using a multitask learning (MTL) framework for projections of individual climate variables.
An Extremal Optimization approach to parallel resonance constrained capacitor placement problem
In this work we propose a parsimonious method to deal with the capacitor placement problem that incorporates resonance constraints, ensuring that every allocated capacitor will not act as a harmonic source.
Single-Solution Hypervolume Maximization and its use for Improving Generalization of Neural Networks
The relationship between the two problems is proved through bounds on the cost function when an optimal solution to one of the problems is evaluated on the other, with a hyperparameter to control the similarity between the two problems.
Reducing the Training Time of Neural Networks by Partitioning
This paper presents a new method for pre-training neural networks that can decrease the total training time for a neural network while maintaining the final performance, which motivates its use on deep neural networks.
Online Social Network Analysis: A Survey of Research Applications in Computer Science
The emergence and popularization of online social networks suddenly made available a large amount of data from social organization, interaction and human behavior.
Social and Information Networks Physics and Society
Asymmetric Distributions from Constrained Mixtures
This paper introduces constrained mixtures for continuous distributions, characterized by a mixture of distributions where each distribution has a shape similar to the base distribution and disjoint domains.
Multi-task Sparse Structure Learning
We illustrate the effectiveness of the proposed model on a variety of synthetic and benchmark datasets for regression and classification.
