Search Results for author:
Found 9 papers, 2 papers with code
SAFFRON: tranSfer leArning For Food-disease RelatiOn extractioN
The accelerating growth of big data in the biomedical domain, with an endless amount of electronic health records and more than 30 million citations and abstracts in PubMed, introduces the need for automatic structuring of textual biomedical data.
TransOpt: Transformer-based Representation Learning for Optimization Problem Classification
We propose a representation of optimization problem instances using a transformer-based neural network architecture trained for the task of problem classification of the 24 problem classes from the Black-box Optimization Benchmarking (BBOB) benchmark.
PS-AAS: Portfolio Selection for Automated Algorithm Selection in Black-Box Optimization
Our proposed method creates algorithm behavior meta-representations, constructs a graph from a set of algorithms based on their meta-representation similarity, and applies a graph algorithm to select a final portfolio of diverse, representative, and non-redundant algorithms.
DynamoRep: Trajectory-Based Population Dynamics for Classification of Black-box Optimization Problems
The application of machine learning (ML) models to the analysis of optimization algorithms requires the representation of optimization problems using numerical features.
Assessing the Generalizability of a Performance Predictive Model
A key component of automated algorithm selection and configuration, which in most cases are performed using supervised machine learning (ML) methods is a good-performing predictive model.
Improving Nevergrad's Algorithm Selection Wizard NGOpt through Automated Algorithm Configuration
Algorithm selection wizards are effective and versatile tools that automatically select an optimization algorithm given high-level information about the problem and available computational resources, such as number and type of decision variables, maximal number of evaluations, possibility to parallelize evaluations, etc.
SELECTOR: Selecting a Representative Benchmark Suite for Reproducible Statistical Comparison
Fair algorithm evaluation is conditioned on the existence of high-quality benchmark datasets that are non-redundant and are representative of typical optimization scenarios.
FoodChem: A food-chemical relation extraction model
In this paper, we present FoodChem, a new Relation Extraction (RE) model for identifying chemicals present in the composition of food entities, based on textual information provided in biomedical peer-reviewed scientific literature.
Less is more: Selecting the right benchmarking set of data for time series classification
By observing discrepancy between the empirical results of the bootstrap evaluation and recently adapted practices in TSC literature when introducing novel methods we warn on the potentially harmful effects of tuning the methods on certain parts of the landscape (unless this is an explicit and desired goal of the study).
