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Found 29 papers, 12 papers with code
Semi-Supervised Segmentation based on Error-Correcting Supervision
Apart from the supervised training on the labeled data, the segmentation network is judged by an additional network.
BioNeRF: Biologically Plausible Neural Radiance Fields for View Synthesis
This paper presents BioNeRF, a biologically plausible architecture that models scenes in a 3D representation and synthesizes new views through radiance fields.
Introducing Bode: A Fine-Tuned Large Language Model for Portuguese Prompt-Based Task
Large Language Models (LLMs) are increasingly bringing advances to Natural Language Processing.
Feature Selection and Hyperparameter Fine-tuning in Artificial Neural Networks for Wood Quality Classification
The predictive performance of the model was compared against five baseline methods as well as a random search, performing either ANN hyperparameter tuning and feature selection.
Facial Point Graphs for Amyotrophic Lateral Sclerosis Identification
Identifying Amyotrophic Lateral Sclerosis (ALS) in its early stages is essential for establishing the beginning of treatment, enriching the outlook, and enhancing the overall well-being of those affected individuals.
Extractive Text Summarization Using Generalized Additive Models with Interactions for Sentence Selection
Automatic Text Summarization (ATS) is becoming relevant with the growth of textual data; however, with the popularization of public large-scale datasets, some recent machine learning approaches have focused on dense models and architectures that, despite producing notable results, usually turn out in models difficult to interpret.
A survey on text generation using generative adversarial networks
This work presents a thorough review concerning recent studies and text generation advancements using Generative Adversarial Networks.
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.
PL-kNN: A Parameterless Nearest Neighbors Classifier
Demands for minimum parameter setup in machine learning models are desirable to avoid time-consuming optimization processes.
Canonical Cortical Graph Neural Networks and its Application for Speech Enhancement in Audio-Visual Hearing Aids
Despite the recent success of machine learning algorithms, most models face drawbacks when considering more complex tasks requiring interaction between different sources, such as multimodal input data and logical time sequences.
A fuzzy distance-based ensemble of deep models for cervical cancer detection
This article proposes a fuzzy distance-based ensemble approach composed of deep learning models for cervical cancer detection in PaP smear images.
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Handling Imbalanced Datasets Through Optimum-Path Forest
Such a behavior yields considerable influence on the machine learning model's performance since it becomes biased on the more frequent data it receives.
A Review of Deep Learning-based Approaches for Deepfake Content Detection
Recent advancements in deep learning generative models have raised concerns as they can create highly convincing counterfeit images and videos.
Multimodal Audio-Visual Information Fusion using Canonical-Correlated Graph Neural Network for Energy-Efficient Speech Enhancement
Our proposed AV CCA-GNN model deals with multimodal representation learning context.
Comparative Study Between Distance Measures On Supervised Optimum-Path Forest Classification
Machine Learning has attracted considerable attention throughout the past decade due to its potential to solve far-reaching tasks, such as image classification, object recognition, anomaly detection, and data forecasting.
Avoiding Overfitting: A Survey on Regularization Methods for Convolutional Neural Networks
The works are classified into three main areas: the first one is called "data augmentation", where all the techniques focus on performing changes in the input data.
Gait Recognition Based on Deep Learning: A Survey
In general, biometry-based control systems may not rely on individual expected behavior or cooperation to operate appropriately.
Modeling Implicit Bias with Fuzzy Cognitive Maps
This paper presents a Fuzzy Cognitive Map model to quantify implicit bias in structured datasets where features can be numeric or discrete.
Improving Transferability of Domain Adaptation Networks Through Domain Alignment Layers
Deep learning (DL) has been the primary approach used in various computer vision tasks due to its relevant results achieved on many tasks.
Multi-Source Unsupervised Domain Adaptation
Unsupervised Domain Adaptation
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.
A Layer-Wise Information Reinforcement Approach to Improve Learning in Deep Belief Networks
With the advent of deep learning, the number of works proposing new methods or improving existent ones has grown exponentially in the last years.
Intestinal Parasites Classification Using Deep Belief Networks
Currently, approximately $4$ billion people are infected by intestinal parasites worldwide.
A Metaheuristic-Driven Approach to Fine-Tune Deep Boltzmann Machines
Deep learning techniques, such as Deep Boltzmann Machines (DBMs), have received considerable attention over the past years due to the outstanding results concerning a variable range of domains.
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.
MaxDropout: Deep Neural Network Regularization Based on Maximum Output Values
Different techniques have emerged in the deep learning scenario, such as Convolutional Neural Networks, Deep Belief Networks, and Long Short-Term Memory Networks, to cite a few.
Learnergy: Energy-based Machine Learners
Throughout the last years, machine learning techniques have been broadly encouraged in the context of deep learning architectures.
OPFython: A Python-Inspired Optimum-Path Forest Classifier
Machine learning techniques have been paramount throughout the last years, being applied in a wide range of tasks, such as classification, object recognition, person identification, and image segmentation.
On the Learning of Deep Local Features for Robust Face Spoofing Detection
However, these methods do not consider the importance of learning deep local features from each facial region, even though it is known from face recognition that each facial region presents different visual aspects, which can also be exploited for face spoofing detection.
