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Found 22 papers, 6 papers with code
Traditional to Transformers: A Survey on Current Trends and Future Prospects for Hyperspectral Image Classification
Hyperspectral image classification is a challenging task due to the high dimensionality and complex nature of hyperspectral data.
Importance of Disjoint Sampling in Conventional and Transformer Models for Hyperspectral Image Classification
This paper presents an innovative disjoint sampling approach for training SOTA models on Hyperspectral image classification (HSIC) tasks.
Pyramid Hierarchical Transformer for Hyperspectral Image Classification
The traditional Transformer model encounters challenges with variable-length input sequences, particularly in Hyperspectral Image Classification (HSIC), leading to efficiency and scalability concerns.
Attention Mechanism Meets with Hybrid Dense Network for Hyperspectral Image Classification
The resulting \textit{attention-fused hybrid network} (AfNet) is based on three attention-fused parallel hybrid sub-nets with different kernels in each block repeatedly using high-level features to enhance the final ground-truth maps.
3D/2D regularized CNN feature hierarchy for Hyperspectral image classification
Convolutional Neural Networks (CNN) have been rigorously studied for Hyperspectral Image Classification (HSIC) and are known to be effective in exploiting joint spatial-spectral information with the expense of lower generalization performance and learning speed due to the hard labels and non-uniform distribution over labels.
Hyperspectral Image Classification: Artifacts of Dimension Reduction on Hybrid CNN
Convolutional Neural Networks (CNN) has been extensively studied for Hyperspectral Image Classification (HSIC) more specifically, 2D and 3D CNN models have proved highly efficient in exploiting the spatial and spectral information of Hyperspectral Images.
Hyperspectral Image Classification-Traditional to Deep Models: A Survey for Future Prospects
Therefore, this survey discusses some strategies to improve the generalization performance of DL strategies which can provide some future guidelines.
Prediction of Malignant & Benign Breast Cancer: A Data Mining Approach in Healthcare Applications
As a potential contributor to state-of-art technology development, data mining finds a multi-fold application in predicting Brest cancer.
A Conjoint Application of Data Mining Techniques for Analysis of Global Terrorist Attacks -- Prevention and Prediction for Combating Terrorism
Terrorism has become one of the most tedious problems to deal with and a prominent threat to mankind.
Stance Prediction for Russian: Data and Analysis
As well as presenting this openly-available dataset, the first of its kind for Russian, the paper presents a baseline for stance prediction in the language.
Ranked #1 on
Stance Detection
on RuStance
Open source platform Digital Personal Assistant
In this paper we develop an open source DPA and smart home system as a 3-rd party extension to show the functionality of the assistant.
Human-Computer Interaction
Pseudorehearsal in actor-critic agents with neural network function approximation
Catastrophic forgetting has a significant negative impact in reinforcement learning.
Self-adaptive node-based PCA encodings
In this paper we propose an algorithm, Simple Hebbian PCA, and prove that it is able to calculate the principal component analysis (PCA) in a distributed fashion across nodes.
Pseudorehearsal in actor-critic agents
Catastrophic forgetting has a serious impact in reinforcement learning, as the data distribution is generally sparse and non-stationary over time.
Pseudorehearsal in value function approximation
Catastrophic forgetting is of special importance in reinforcement learning, as the data distribution is generally non-stationary over time.
The BioDynaMo Project: a platform for computer simulations of biological dynamics
This paper is a brief update on developments in the BioDynaMo project, a new platform for computer simulations for biological research.
Neuromorphic Robot Dream
During the "day phase" a robotic system stores the inbound information and is controlled by a light-weight rule-based system in real time.
The BioDynaMo Project
Computer simulations have become a very powerful tool for scientific research.
Microservices: yesterday, today, and tomorrow
Microservices is an architectural style inspired by service-oriented computing that has recently started gaining popularity.
Software Engineering
Towards Anthropo-inspired Computational Systems: the $P^3$ Model
This paper proposes a model which aim is providing a more coherent framework for agents design.
A Cognitive Architecture for the Implementation of Emotions in Computing Systems
In this paper we present a new neurobiologically-inspired affective cognitive architecture: NEUCOGAR (NEUromodulating COGnitive ARchitecture).
Robot Dream
In this position paper we present a novel approach to neurobiologically plausible implementation of emotional reactions and behaviors for real-time autonomous robotic systems.
