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Found 25 papers, 4 papers with code
Text-to-Image Cross-Modal Generation: A Systematic Review
The conducted review suggests a significant increase in the number of papers published in the area and highlights research gaps and potential lines of investigation.
One Self-Configurable Model to Solve Many Abstract Visual Reasoning Problems
With the aim of developing universal learning systems in the AVR domain, we propose the unified model for solving Single-Choice Abstract visual Reasoning tasks (SCAR), capable of solving various single-choice AVR tasks, without making any a priori assumptions about the task structure, in particular the number and location of panels.
Coevolutionary Algorithm for Building Robust Decision Trees under Minimax Regret
It outperformed all competing methods on 13 datasets with adversarial accuracy metrics, and on all 20 considered datasets with minimax regret.
AttentionMix: Data augmentation method that relies on BERT attention mechanism
The Mixup method has proven to be a powerful data augmentation technique in Computer Vision, with many successors that perform image mixing in a guided manner.
StatMix: Data augmentation method that relies on image statistics in federated learning
Availability of large amount of annotated data is one of the pillars of deep learning success.
Using adversarial images to improve outcomes of federated learning for non-IID data
One of the important problems in federated learning is how to deal with unbalanced data.
Evolutionary Approach to Security Games with Signaling
Green Security Games have become a popular way to model scenarios involving the protection of natural resources, such as wildlife.
Improving LSHADE by means of a pre-screening mechanism
The paper introduces an extension to the well-known LSHADE algorithm in the form of a pre-screening mechanism (psLSHADE).
A Review of Emerging Research Directions in Abstract Visual Reasoning
One of them refers to the observation that in the machine learning literature different tasks are considered in isolation, which is in the stark contrast with the way the AVR tasks are used to measure human intelligence, where multiple types of problems are combined within a single IQ test.
Deep Learning Methods for Abstract Visual Reasoning: A Survey on Raven's Progressive Matrices
We focus on the most common type of AVR tasks -- the Raven's Progressive Matrices (RPMs) -- and provide a comprehensive review of the learning methods and deep neural models applied to solve RPMs, as well as, the RPM benchmark sets.
Adversarial defenses via a mixture of generators
A canonical set of images is used to generate adversarial examples through potentially multiple attacks.
Prediction of the Facial Growth Direction is Challenging
Facial dysmorphology or malocclusion is frequently associated with abnormal growth of the face.
Audio-to-Image Cross-Modal Generation
Cross-modal representation learning allows to integrate information from different modalities into one representation.
An overview of mixing augmentation methods and augmentation strategies
Deep Convolutional Neural Networks have made an incredible progress in many Computer Vision tasks.
Prediction of the facial growth direction with Machine Learning methods
First attempts of prediction of the facial growth (FG) direction were made over half of a century ago.
Monte Carlo Tree Search: A Review of Recent Modifications and Applications
Monte Carlo Tree Search (MCTS) is a powerful approach to designing game-playing bots or solving sequential decision problems.
Multi-Label Contrastive Learning for Abstract Visual Reasoning
State-of-the-art systems solving RPMs rely on massive pattern-based training and sometimes on exploiting biases in the dataset, whereas humans concentrate on identification of the rules / concepts underlying the RPM (or generally a visual reasoning task) to be solved.
Biologically Plausible Learning of Text Representation with Spiking Neural Networks
This study proposes a novel biologically plausible mechanism for generating low-dimensional spike-based text representation.
Dynamic Vehicle Routing Problem: A Monte Carlo approach
First, that we know a bounding rectangle of the area in which the requests might appear.
A Particle Swarm Optimization hyper-heuristic for the Dynamic Vehicle Routing Problem
The optimization algorithm is chosen on the basis of a prediction made by a linear model trained on that data and the relative results obtained by the optimization algorithms.
A Committee of Convolutional Neural Networks for Image Classication in the Concurrent Presence of Feature and Label Noise
Basing on the MNIST, CIFAR-10 and CIFAR-100 datasets, we experimentally proved that the difference by which committees beat single models increases along with noise level, no matter it is an attribute or label disruption.
Generalized Self-Adapting Particle Swarm Optimization algorithm with archive of samples
In comparison with the original GAPSO formulation it includes the following four features: a global restart management scheme, samples gathering within an R-Tree based index (archive/memory of samples), adaptation of a sampling behavior based on a global particle performance, and a specific approach to local search.
Anchoring Theory in Sequential Stackelberg Games
An underlying assumption of Stackelberg Games (SGs) is perfect rationality of the players.
Double-oracle sampling method for Stackelberg Equilibrium approximation in general-sum extensive-form games
The paper presents a new method for approximating Strong Stackelberg Equilibrium in general-sum sequential games with imperfect information and perfect recall.
Addressing Expensive Multi-objective Games with Postponed Preference Articulation via Memetic Co-evolution
This paper presents algorithmic and empirical contributions demonstrating that the convergence characteristics of a co-evolutionary approach to tackle Multi-Objective Games (MOGs) with postponed preference articulation can often be hampered due to the possible emergence of the so-called Red Queen effect.
