39 skills found · Page 1 of 2
rahulvigneswaran / Intrusion Detection SystemsThis is the repo of the research paper, "Evaluating Shallow and Deep Neural Networks for Network Intrusion Detection Systems in Cyber Security".
giacbrd / ShallowLearnAn experiment about re-implementing supervised learning models based on shallow neural network approaches (e.g. fastText) with some additional exclusive features and nice API. Written in Python and fully compatible with Scikit-learn.
vbalnt / TfeatTFeat descriptor models for BMVC 2016 paper "Learning local feature descriptors with triplets and shallow convolutional neural networks"
mkartik / Shallow UWnetShallow-UWnet, a neural network which maintains performance and has fewer parameters than the state-of-art underwater image enhancement model. Generalization of the model is demonstrated by benchmarking its performance on combination of synthetic and real-world datasets.
SSQ / Coursera Ng Neural Networks And Deep LearningBuild logistic regression, neural network models for classification
LeadingIndiaAI / Fake News Detection Fake news is misinformation or manipulated news that is spread across the social media with an intention to damage a person, agency and organisation. Due to the dissemination of fake news, there is a need for computational methods to detect them. Fake news detection aims to help users to expose varieties of fabricated news. To achieve this goal, first we have taken the datasets which contains both fake and real news and conducted various experiments to organize fake news detector. We used natural processing, machine learning and deep learning techniques to classify the datasets. We yielded a comprehensive audit of detecting fake news by including fake news categorization, existing algorithms from machine learning techniques. In this project, we explored different machine learning models like Naïve Bayes, K nearest neighbors, decision tree, random forest and deep learning networks like Shallow Convolutional Neural Networks (CNN), Deep Convolutional Neural Network (VDCNN), Long Short-Term Memory Network (LSTM), Gated Recurrent Unit Network (GRU), Combination of Convolutional Neural Network with Long Short-Term Memory (CNN-LSTM) and Convolutional Neural Network with Gated Recurrent Unit (CNN-LSTM).
shilpar27 / Label Embeddings In Image ClassificationConvolutional Neural Networks (CNNs) are being widely used for various tasks in Computer Vision. We focus on the task of image classification particularly using CNNs with more focus on the relation or similarity between class labels. The similarity between labels is judged using label word embeddings and incorporated into the loss layer. We propose that shallower networks be learnt with more complex and structured losses, in order to gain from shorter training time and equivalent complexity. We train two variants of CNNs with multiple architectures , all limited to a maximum of ten convolution layers to obtain an accuracy of 93.27% on the Fashion-MNIST dataset and 86.40% on the CIFAR 10 dataset. We further probe the adversarial robustness of the model as well the classspecific behavior by visualizing the class confusion matrix.We also show some preliminary results towards extending a trained variant to zero-shot learning.
stathius / Wave PropagationPredicting wave propagation on shallow water with deep neural networks
zeochoy / Tcga Embeddingusing shallow neural network layer (embedding) to infer gene-gene/sample relationship from gene expression data
jaejun-yoo / Shallow DANN Two Moon DatasetImplementation: shallow Domain Adaptation Neural Network (DANN) with two moon dataset / % reference : https://arxiv.org/pdf/1505.07818v4.pdf
Orang-utan / CNN Sleep Stage Estimation😴 Classifying patient sleep stage (i.e. REM sleep, shallow, etc.) based on Heart Rate Variability data; model is based on time-domain Convolutional Neural Network, implemented in Tensorflow, Python.
open-airlab / GateNetGateNet: A shallow neural network for gate perception in drone racing
sss441803 / QTensorAIA hybrid quantum-classical neural network simulation platform. Quantum simulation uses QTensor, a state-of-the-art tensor network-based simulator that usually has linear complexity in the number of qubits for shallow circuits, instead of exponential complexity. This opens up the possibility to simulate large hybrid models with many qubits. The hybrid model is a PyTorch model, batch-parallelized, GPU compatible and fully differentiable.
PL97 / TTL[BMVC'23 Oral] Offical repository of "Rethinking Transfer Learning for Medical Image Classification"
kyegomez / ShallowFFZeta implemantion of "Rethinking Attention: Exploring Shallow Feed-Forward Neural Networks as an Alternative to Attention Layers in Transformers"
LuizScarlet / AEIC[CVPR 2026] Ultra-Low Bitrate Perceptual Image Compression with Shallow Encoder
Reasat / Cnn ImiThis is a repository for the code developed to produced the results in the paper "Detection of Inferior Myocardial Infarction using Shallow Convolutional Neural Networks" (https://arxiv.org/abs/1710.01115v3)
Abdulk084 / HybridTox2DIn recent times, toxicological classification of chemical compounds is considered to be a grand challenge for pharma-ceutical and environment regulators. Advancement in machine learning techniques enabled efficient toxicity predic-tion pipelines. Random forests (RF), support vector machines (SVM) and deep neural networks (DNN) are often ap-plied to model the toxic effects of chemical compounds. However, complexity-accuracy tradeoff still needs to be ac-counted in order to improve the efficiency and commercial deployment of these methods. In this study, we implement a hybrid framework consists of a shallow neural network and a decision classifier for toxicity prediction of chemicals that interrupt nuclear receptor (NR) and stress response (SR) signaling pathways. A model based on proposed hybrid framework is trained on Tox21 data using 2D chemical descriptors that are less multifarious in nature and easy to calcu-late. Our method achieved the highest accuracy of 0.847 AUC (area under the curve) using a shallow neural network with only one hidden layer consisted of 10 neurons. Furthermore, our hybrid model enabled us to elucidate the inter-pretation of most important descriptors responsible for NR and SR toxicity.
phamdinhthang / Neural EmbedderTensorflow implementation of Categorical Variable encoder, using Shallow Neural Network entity embedding
sukiboo / SgnSource code for the numerical experiments presented in the paper "Greedy Shallow Networks: An Approach for Constructing and Training Neural Networks".
