Search Results for author:
Found 18 papers, 6 papers with code
Video Action Recognition Collaborative Learning with Dynamics via PSO-ConvNet Transformer
To extend our approach to video, we integrate ConvNets with state-of-the-art temporal methods such as Transformer and Recurrent Neural Networks.
Ranked #75 on
Action Recognition
on UCF101
PSO-Convolutional Neural Networks with Heterogeneous Learning Rate
In such framework, the vector of weights of each ConvNet is typically cast as the position of a particle in phase space whereby PSO collaborative dynamics intertwines with Stochastic Gradient Descent (SGD) in order to boost training performance and generalization.
Ranked #39 on
Image Classification
on CIFAR-10
Action Recognition for American Sign Language
In this research, we present our findings to recognize American Sign Language from series of hand gestures.
Rethinking Recurrent Neural Networks and Other Improvements for Image Classification
In addition, we extend the training strategy so that our model performs comparably to leading models and can even match the state-of-the-art models on several challenging datasets (e. g., SVHN (0. 99), Cifar-100 (0. 9027) and Cifar-10 (0. 9852)).
Ranked #1 on
Image Classification
on Surrey ASL
An Improvement for Capsule Networks using Depthwise Separable Convolution
Capsule Networks face a critical problem in computer vision in the sense that the image background can challenge its performance, although they learn very well on training data.
Incremental Evolution and Development of Deep Artificial Neural Networks
Despite aiding non-expert users to design and train ANNs, the vast majority of NE approaches disregard the knowledge that is gathered when solving other tasks, i. e., evolution starts from scratch for each problem, ultimately delaying the evolutionary process.
Evolution of Scikit-Learn Pipelines with Dynamic Structured Grammatical Evolution
The deployment of Machine Learning (ML) models is a difficult and time-consuming job that comprises a series of sequential and correlated tasks that go from the data pre-processing, and the design and extraction of features, to the choice of the ML algorithm and its parameterisation.
Fast-DENSER++: Evolving Fully-Trained Deep Artificial Neural Networks
This paper proposes a new extension to Deep Evolutionary Network Structured Evolution (DENSER), called Fast-DENSER++ (F-DENSER++).
Offline and Online Deep Learning for Image Recognition
Image recognition using Deep Learning has been evolved for decades though advances in the field through different settings is still a challenge.
Advanced Capsule Networks via Context Awareness
Capsule Networks (CN) offer new architectures for Deep Learning (DL) community.
A Bayesian Additive Model for Understanding Public Transport Usage in Special Events
Public special events, like sports games, concerts and festivals are well known to create disruptions in transportation systems, often catching the operators by surprise.
Learning Supervised Topic Models for Classification and Regression from Crowds
The growing need to analyze large collections of documents has led to great developments in topic modeling.
DENSER: Deep Evolutionary Network Structured Representation
Deep Evolutionary Network Structured Representation (DENSER) is a novel approach to automatically design Artificial Neural Networks (ANNs) using Evolutionary Computation.
A Grassmannian Approach to Zero-Shot Learning for Network Intrusion Detection
This can be overcome with Zero-Shot Learning, a new approach in the field of Computer Vision, which can be described in two stages: the Attribute Learning and the Inference Stage.
Towards the Evolution of Multi-Layered Neural Networks: A Dynamic Structured Grammatical Evolution Approach
On the other, there is no way to evolve networks with more than one output neuron.
Attribute Learning for Network Intrusion Detection
In this paper we propose a new algorithm for the attribute learning stage of ZSL.
Automatic Graphic Logo Detection via Fast Region-based Convolutional Networks
Brand recognition is a very challenging topic with many useful applications in localization recognition, advertisement and marketing.
Mahalanobis Distance Metric Learning Algorithm for Instance-based Data Stream Classification
This approach hybridizes a Mahalanobis distance metric learning algorithm and a k-NN data stream classification algorithm with concept drift detection.
