Bijay Gaudel's repositories
GraphSAGELite
A general framework that leverages node feature information (e.g., text attributes) to efficiently generate node embeddings for previously unseen data. Instead of training individual embeddings for each node, GraphSAGE learn a function that generates embeddings by sampling and aggregating features from a node’s local neighborhood. Here, the implementation of GraphSAGE is based on transductive training
struc2vec
Structural identity is a concept of symmetry in which network nodes are identified according to the network structure and their relationship to other nodes. Structural identity has been studied in theory and practice over the past decades, but only recently has it been adressed with representation learning techniques. Node2vec is a flexible framework for learning latent representation for the structural identity of nodes. Struc2vec uses a hierarchy to measure node similarity at different scales, and constructs a multilayer graph to encode structural similarities and generate structural context for nodes.
faceDetectionUsingWebcam
Face detection using webcame
graphembedding-LINE
Line is a graph embedding technique, suitable for arbitrary types of information networks: undirected, directed, and/or weighted. The method optimizes a carefully designed objective function that preserves both the local and global network structures.
workshop2021
Basic of Deep Learning, Neural Networks, TensorFlow, and Machine Vision
autoencoders
Pytorch Implementation of different types of AutoEncoders
diffusion-defender
A diffusion-based denoising approach to mitigate online adversarial image attacks and an FFT-based detector
graph-similarity
Local and Global similarity calculation
NepaliAutoCompleteAndLM
NepaliAutoCompleteML is a deep learning framework (using Pytorch) for suggesting relevant words during writing. We used Recurrent Neural Network with layer GRU or LSTM, and KL divergence as loss function to train our model.
django_site
Simple django blog post site.
gaudelbijay
Config files for my GitHub profile.
landingGearDetection-YOLOV4
landing gear detection training using YOLOV4.
SDNE
Structural Deep Network Embedding (SDNE) is a semi-supervised deep model for graph embedding. Which has multiple layers of non-linear functions, thereby being able to capture the highly non-linear network structure.
sensor-api
Simple Flask API and Flutter App to track sensors data
simple_NN_visualizer
Simple neural network visualiser from coursera project