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Found 9 papers, 6 papers with code
Enhancing Hyper-To-Real Space Projections Through Euclidean Norm Meta-Heuristic Optimization
The continuous computational power growth in the last decades has made solving several optimization problems significant to humankind a tractable task; however, tackling some of them remains a challenge due to the overwhelming amount of candidate solutions to be evaluated, even by using sophisticated algorithms.
Improving Pre-Trained Weights Through Meta-Heuristics Fine-Tuning
Machine Learning algorithms have been extensively researched throughout the last decade, leading to unprecedented advances in a broad range of applications, such as image classification and reconstruction, object recognition, and text categorization.
From Actions to Events: A Transfer Learning Approach Using Improved Deep Belief Networks
In the last decade, exponential data growth supplied machine learning-based algorithms' capacity and enabled their usage in daily-life activities.
LiteTransformerSearch: Training-free Neural Architecture Search for Efficient Language Models
Results show that the perplexity of 16-layer GPT-2 and Transformer-XL can be achieved with up to 1. 5x, 2. 5x faster runtime and 1. 2x, 2. 0x lower peak memory utilization.
Speeding Up OPFython with Numba
A graph-inspired classifier, known as Optimum-Path Forest (OPF), has proven to be a state-of-the-art algorithm comparable to Logistic Regressors, Support Vector Machines in a wide variety of tasks.
Energy-based Dropout in Restricted Boltzmann Machines: Why not go random
Deep learning architectures have been widely fostered throughout the last years, being used in a wide range of applications, such as object recognition, image reconstruction, and signal processing.
A Nature-Inspired Feature Selection Approach based on Hypercomplex Information
The essential idea behind it is to find the most suitable subset of features according to some criterion.
Fast Ensemble Learning Using Adversarially-Generated Restricted Boltzmann Machines
Machine Learning has been applied in a wide range of tasks throughout the last years, ranging from image classification to autonomous driving and natural language processing.
Opytimizer: A Nature-Inspired Python Optimizer
Optimization aims at selecting a feasible set of parameters in an attempt to solve a particular problem, being applied in a wide range of applications, such as operations research, machine learning fine-tuning, and control engineering, among others.
