Adversarial Reprogramming has demonstrated success in utilizing pre-trained neural network classifiers for alternative classification tasks without modification to the original network. An adversary in such an attack scenario trains an additive contribution to the inputs to repurpose the neural network for the new clas…
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169,051 papers · 148 categories
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48 results for “classification networks”
Estimates neural network errors for classification problems.
problem Binary and multi-class classification problems.
method Rademacher complexity estimates and direct approximation theorems.
result A priori error estimates for regularized loss functionals.
Graph Prototypical Networks improve few-shot node classification on attributed networks.
problem Few-shot node classification in attributed networks with limited labeled instances.
method Graph Prototypical Networks (GPN) using meta-learning to extract meta-knowledge and identify informative labeled instances.
result GPN achieves superior performance in few-shot node classification.
Capsule networks improve audio classification performance.
problem Improving audio classification accuracy.
method Dynamic routing-by-agreement mechanism for capsule networks.
result Significant improvements in performance compared to baseline models.
BraidNet uses braid theory to optimize neural networks for image classification.
problem Image classification problems
method Procedural optimization of neural networks combining information theory and braid theory
result BraidNet outperforms other networks in learning speed and accuracy
Improved protein structure classification using weighted graphlets and deep neural networks.
problem Protein structure classification for function prediction.
method Developed a weighted network and graphlet-based measure, combined with a deep neural network.
result Significantly improved performance on 36 real datasets compared to existing methods.
New optimization methods for neural networks improve classification accuracy.
problem Optimizing shallow networks for binary classification.
method Introducing a new family of optimization problems not covered by existing methods.
result New training algorithms with stable convergence and improved performance.
Paper reduces neural network complexity for image classification.
problem High computational complexity in deep neural networks.
method Proposes a two-step classification process: coarse-grain and fine-grain.
result Achieves similar accuracy with less computational complexity.
Ladder Networks improve semi-supervised hyperspectral image classification.
problem Semi-supervised hyperspectral image classification with limited labeled data.
method Jointly optimizing a supervised and unsupervised cost in a Ladder Network.
result Convolutional Ladder Network achieves state-of-the-art performance with minimal labeled data.
CRCEN neural network tackles imbalanced classification.
problem Challenges in training conventional classifiers on imbalanced datasets.
method CRCEN neural network with a novel weighted cross entropy loss function.
result CRCEN outperforms baseline models on benchmark datasets.
Paper introduces Deep Node Ranking for efficient node classification and embedding.
problem Efficient node classification and embedding in complex networks.
method Autoencoder-based neural network architecture with node ranking scheme.
result Competitive or better classification performance, higher speed, and lower space requirements.
A new network-based high-level data classification method using betweenness centrality.
problem Traditional data classification techniques focus on physical features, while high-level classification considers semantic meaning.
method Proposes a network-based high-level classification technique using betweenness centrality.
result Competent classification performance in nine real datasets compared to traditional models.
Automates graph convolutional network design for semi-supervised node classification.
problem Designing optimal graph convolutional network architectures for semi-supervised node classification.
method An automatic process to define a problem-specific architecture based on graph structure.
result The proposed method outperforms existing methods in classification performance and network compactness.
A neural network tackles emotion recognition, attribution, and summarization.
problem Sparsity of emotional expressions in videos.
method Bi-stream Emotion Attribution-Classification Network (BEAC-Net) with two networks: attribution and classification.
result Superior performance on emotion attribution, recognition, and summarization tasks.
A new kernel measures brain network similarities, improving disease classification.
problem Lack of edge weight information in existing graph kernels for brain connectivity networks.
method Ordinal pattern kernel for weighted brain connectivity networks.
result The ordinal pattern kernel achieves better classification performance than state-of-the-art graph kernels.
A new network-based method for high-level data classification without normalization.
problem Evaluation and classification of data with hidden attribute interactions.
method A new network-building methodology based on attribute-attribute interactions, avoiding normalization.
result Improvement in accuracy of high-level classification using betweenness centrality.
This study analyzes wireless network data using classification techniques.
problem Identify normal and abnormal traffic in wireless networks.
method Used WEKA software with predefined anomaly classes from the MAC layer.
result Classification algorithms show success in detecting anomalies.
A new method combines topological features with graph convolutional networks for improved paper classification.
problem Classifying papers based on their content and structure.
method Combining topological features of nodes with information propagation through Graph Convolutional Networks (GCN).
result The method improves classification accuracy on CiteSeer and Cora datasets, matching or exceeding text-based classification results.
Deep learning improves sparse representation for better classification.
problem Improving classification accuracy using sparse representation.
method A transductive deep learning network combining convolutional autoencoder and fully-connected layers.
result The proposed network achieves better classification results than state-of-the-art SRC methods.
Deep neural network improves hand gesture classification from wearable IMUs.
problem Classifying hand gestures from wearable IMUs using conventional methods.
method Deep Neural Network (DNN) with optimization objective for signal fitting.
result 3-5% improvement in classification accuracy compared to SVM and kNN.
The paper introduces new Bayesian network classifiers for better classification accuracy.
problem Improving supervised classification accuracy using Bayesian network classifiers.
method Developed novel classes of generative classifiers based on staged tree models, extending Bayesian networks.
result Data-driven learning routines enhance the accuracy of the new classifiers.
GMNN combines conditional random fields and graph neural networks for relational data.
problem Semi-supervised object classification in relational data.
method Combines conditional random fields and graph neural networks. Uses variational EM algorithm for training.
result GMNN achieves state-of-the-art results on object classification, link classification, and unsupervised node representation learning.
LNEMLC embeds label network for multi-label classification.
problem Lack of effective adaptation and preservation of generalization abilities for unseen label combinations.
method LNEMLC embeds label network to extend input space for any base multi-label classifier.
result Statistically significant improvements over simple kNN baseline classifier.
LoCEC classifies user relationships in large social networks, addressing sparsity issues.
problem Sparse relationship feature and label data in real social platforms.
method Local Community-based Edge Classification (LoCEC) framework with three-phase processing.
result Effective and efficient classification of user relationships in large-scale networks.
Complex-valued neural networks perform similarly to real-valued models for real-valued classification tasks.
problem Comparing real-valued and complex-valued neural networks for real-valued classification tasks.
method Comparison of neural networks with similar capacity sizes, using various activation functions and weight initialisation strategies.
result Complex-valued neural networks perform equal to or slightly worse than real-valued models for real-valued classification tasks.
A new neural network for text classification reduces parameters with improved accuracy.
problem Reducing the number of parameters in text classification models.
method Compositional coding, capsule network, k-means routing algorithm.
result The proposed method achieves competitive accuracy with significantly fewer parameters.
Proposes a new pretraining strategy for RNNs to improve classification performance.
problem Poor generalization of RNNs due to initial parameter assignment.
method Data-aware layer-wise pretraining strategy to initialize RNN parameters.
result Data-aware strategies positively support the initialization of RNN-based classification models.
Paper proposes a robust time series classification method using ResNet and Recurrence Plots.
problem Classifying time series data is challenging and underexplored.
method Transfer learning in Deep Neural Networks, 2D Recurrence Plots, ResNet architecture, simplified preprocessing.
result First time multi-time series classification using a single network.
Novel multigraph network improves chemical classification tasks.
problem Learning from variable graphs with multiple relationships.
method Proposed a multigraph network using Chebyshev GCNs to handle variable graphs and learned edges.
result Achieved competitive results on chemical classification benchmarks.
New network learns image features inductively for disease classification.
problem Pre-processing image features limits network optimization.
method Inductive end-to-end learning with CNN and graph filters trained jointly.
result Significantly improved classification scores and higher stability.
Machine learning techniques have become increasingly popular in the field of resting state fMRI (functional magnetic resonance imaging) network based classification. However, the application of convolutional networks has been proposed only very recently and has remained largely unexplored. In this paper we describe a c…
Deep neural network for cancer classification using autoencoders.
problem Cancer classification using molecular information.
method Using a Denoising Autoencoder (DAE) as weight initialization for a deep neural network, comparing two approaches: fixed weights and fine-tuning. Embedding strategies included encoding layers and complete autoencoder.
result Best F1 score of 98.04% for identifying thyroid cancer samples.
Joint Binary Neural Network for Multi-label Learning with Applications to Emotion Classificationcs.LG
Recently the deep learning techniques have achieved success in multi-label classification due to its automatic representation learning ability and the end-to-end learning framework. Existing deep neural networks in multi-label classification can be divided into two kinds: binary relevance neural network (BRNN) and thre…
Combines BERT and GCN for better text classification.
problem Limited global information capture by BERT.
method Integrates BERT with VGCN for improved text classification.
result VGCN-BERT outperforms BERT and GCN alone.
Siegel Neural Networksstat.ML
Proposes Siegel neural networks for improved classification tasks.
problem Classification on Siegel spaces is underexplored.
method Uses quotient structure and vector-valued distance notation.
result Demonstrates state-of-the-art performance in radar and node classification.
Study shows how to approximate and estimate high-dimensional classification functions without the curse of dimensionality.
problem Approximating and estimating classification functions in high-dimensional spaces.
method Modified existing results to show that functions can be approximated by neural networks with bounded weights. Proved the existence of a neural network with bounded weights approximating a classification function. Leveraged these bounds to quantify estimation rates.
result Neural networks can approximate functions without the curse of dimensionality, leading to efficient estimation rates.
A new framework for neural network classification using vector quantization.
problem Learning a neural network classifier under the IB principle.
method Aggregated Learning framework, combining vector quantization and variational techniques.
result The effectiveness of Aggregated Learning verified through experiments.
Neural networks improve functional data classification.
problem Lack of effective classification methods for functional data.
method Extended deep learning methodologies to functional data for classification.
result Demonstrated superior performance in spectrographic data classification.
Proposes a spiking neural network for efficient classification.
problem High computational and power costs of ANNs.
method Random Neural Network (RNN) with spiking neurons.
result Matches classification power of ANNs but with lower energy consumption.
A deep network classifies images by scattering and dictionary learning.
problem Classifying images with high accuracy using deep learning.
method Sparse scattering transform followed by dictionary learning in a deep convolutional network.
result Higher classification accuracy than AlexNet on ImageNet dataset.
The paper classifies vehicle shapes and colors using deep neural networks.
problem Vehicle reidentification and classification challenges.
method Used deeper neural networks for classification accuracy.
result Good classification accuracy on make/model and color.
Paper interprets ResNets via gate-network controls and deep-layer classifications.
problem Understanding the performance mechanism of ResNets.
method Constructs typical solutions using gate-network controls and deep-layer classifications.
result Proves the universal-approximation capability of ResNets.
LC-GNN improves GNNs for node classification by incorporating label consistency.
problem Limited performance of GNNs due to label consistency assumption not always holding.
method LC-GNN uses node pairs with the same label but unconnected to expand GNN's receptive field.
result LC-GNN outperforms traditional GNNs in semi-supervised node classification.
Improved DNN calibration without sacrificing accuracy.
problem Poor calibration of over-parametrized DNNs in safety-critical applications.
method Decoupling feature extraction and classification layers, and applying Gaussian priors.
result Significant improvement in model calibration with minimal training cost.
Deep neural networks classify unbounded Gaussian mixture data without dimensionality issues.
problem Binary classification of unbounded Gaussian mixture data.
method Deep ReLU neural networks with non-asymptotic upper bounds and convergence rates.
result Deep ReLU networks can classify unbounded Gaussian mixture data without dimensionality constraints.
Novel neural network approximates exact distance for robust classification.
problem Adversarial attacks on neural networks in safety-critical systems.
method Signed Distance Classifiers (SDCs) and Unitary-Gradient Neural Network.
result Approximates exact distance from classification boundary for certifiable predictions.
Dilated CNN improves multivariate time series classification.
problem Multivariate time series classification.
method Transformed multivariate time series into image-like style, applied dilated and strided convolutions.
result Automatic features extracted by dilated CNN are as effective as hand-crafted features.
A neural network-based two-sample test improves classification accuracy.
problem Differentiating between two sub-exponential densities.
method Difference of logit function from trained classification neural network.
result Network complexity scales with intrinsic dimensionality for low-dimensional manifolds.