AIMC Topic: Neural Networks, Computer

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Robust asynchronous control of ERP-Based brain-Computer interfaces using deep learning.

Computer methods and programs in biomedicine
BACKGROUND AND OBJECTIVE: Brain-computer interfaces (BCI) based on event-related potentials (ERP) are a promising technology for alternative and augmented communication in an assistive context. However, most approaches to date are synchronous, requir...

Deep CNN with Hybrid Binary Local Search and Particle Swarm Optimizer for Exudates Classification from Fundus Images.

Journal of digital imaging
Diabetic retinopathy is a chronic condition that causes vision loss if not detected early. In the early stage, it can be diagnosed with the aid of exudates which are called lesions. However, it is arduous to detect the exudate lesion due to the avail...

Deep Constraint-Based Propagation in Graph Neural Networks.

IEEE transactions on pattern analysis and machine intelligence
The popularity of deep learning techniques renewed the interest in neural architectures able to process complex structures that can be represented using graphs, inspired by Graph Neural Networks (GNNs). We focus our attention on the originally propos...

On Inductive-Transductive Learning With Graph Neural Networks.

IEEE transactions on pattern analysis and machine intelligence
Many real-world domains involve information naturally represented by graphs, where nodes denote basic patterns while edges stand for relationships among them. The graph neural network (GNN) is a machine learning model capable of directly managing gra...

GeoNet++: Iterative Geometric Neural Network with Edge-Aware Refinement for Joint Depth and Surface Normal Estimation.

IEEE transactions on pattern analysis and machine intelligence
In this paper, we propose a geometric neural network with edge-aware refinement (GeoNet++) to jointly predict both depth and surface normal maps from a single image. Building on top of two-stream CNNs, GeoNet++ captures the geometric relationships be...

ManifoldNet: A Deep Neural Network for Manifold-Valued Data With Applications.

IEEE transactions on pattern analysis and machine intelligence
Geometric deep learning is a relatively nascent field that has attracted significant attention in the past few years. This is partly due to the availability of data acquired from non-euclidean domains or features extracted from euclidean-space data t...

Dynamic Facial Expression Generation on Hilbert Hypersphere With Conditional Wasserstein Generative Adversarial Nets.

IEEE transactions on pattern analysis and machine intelligence
In this work, we propose a novel approach for generating videos of the six basic facial expressions given a neutral face image. We propose to exploit the face geometry by modeling the facial landmarks motion as curves encoded as points on a hypersphe...

P-CNN: Part-Based Convolutional Neural Networks for Fine-Grained Visual Categorization.

IEEE transactions on pattern analysis and machine intelligence
This paper proposes an end-to-end fine-grained visual categorization system, termed Part-based Convolutional Neural Network (P-CNN), which consists of three modules. The first module is a Squeeze-and-Excitation (SE) block, which learns to recalibrate...

Coherence Constrained Graph LSTM for Group Activity Recognition.

IEEE transactions on pattern analysis and machine intelligence
This work aims to address the group activity recognition problem by exploring human motion characteristics. Traditional methods hold that the motions of all persons contribute equally to the group activity, which suppresses the contributions of some ...

Application of Neuromorphic Olfactory Approach for High-Accuracy Classification of Malts.

Sensors (Basel, Switzerland)
Current developments in artificial olfactory systems, also known as electronic nose (e-nose) systems, have benefited from advanced machine learning techniques that have significantly improved the conditioning and processing of multivariate feature-ri...