52 skills found · Page 1 of 2
yueliu1999 / DCRN[AAAI 2022] An official source code for paper Deep Graph Clustering via Dual Correlation Reduction.
ZYangChen / MoCha Stereo[CVPR2024] The official implementation of "MoCha-Stereo: Motif Channel Attention Network for Stereo Matching”. & [arXiv] The official implementation of "Motif Channel Opened in a White-Box: Stereo Matching via Motif Correlation Graph"
zh54321 / SharePointDumperPowerShell SharePoint extraction + auditing tool for red/blue/purple teams. Enumerates all SharePoint sites/drives a user can access via Microsoft Graph, recursively downloads files, and logs every Graph + SharePoint HTTP request for SIEM correlation, detection engineering, and IR testing.
lvguofeng / GNN PPICodes and models for the paper "Learning Unknown from Correlations: Graph Neural Network for Inter-novel-protein Interaction Prediction".
hengruizhang98 / CCA SSGCodes for 'From Canonical Correlation Analysis to Self-supervised Graph Neural Networks'. https://arxiv.org/abs/2106.12484
USC-InfoLab / NeuroGNNNeuroGNN is a state-of-the-art framework for precise seizure detection and classification from EEG data. It employs dynamic Graph Neural Networks (GNNs) to capture intricate spatial, temporal, semantic, and taxonomic correlations between EEG electrode locations and brain regions, resulting in improved accuracy. Presented at PAKDD '24.
MIRALab-USTC / KG TACTThe code of paper Topology-Aware Correlations Between Relations for Inductive Link Prediction in Knowledge Graphs. Jiajun Chen, Huarui He, Feng Wu, Jie Wang. AAAI 2021.
Extreme-classification / ECLAREECLARE: Extreme Classification with Label Graph Correlations
bsrikrishna / Financials Analysis For Financial ModellingExport 15 years P&L and BS data from moneycontrol. Correlation analysis of various heads. Mean & Std. Graphs of YoY changes and projecting 10yr financials in python and exporting the data to Excel. Makes it easy for preparing Financial Models for Indian companies.
GraphAlgoX / GraphMM MasterGraphMM: Graph-based Vehicular Map Matching by Leveraging Trajectory and Road Correlations
reddyprasade / Machine Learning Interview PreparationPrepare to Technical Skills Here are the essential skills that a Machine Learning Engineer needs, as mentioned Read me files. Within each group are topics that you should be familiar with. Study Tip: Copy and paste this list into a document and save to your computer for easy referral. Computer Science Fundamentals and Programming Topics Data structures: Lists, stacks, queues, strings, hash maps, vectors, matrices, classes & objects, trees, graphs, etc. Algorithms: Recursion, searching, sorting, optimization, dynamic programming, etc. Computability and complexity: P vs. NP, NP-complete problems, big-O notation, approximate algorithms, etc. Computer architecture: Memory, cache, bandwidth, threads & processes, deadlocks, etc. Probability and Statistics Topics Basic probability: Conditional probability, Bayes rule, likelihood, independence, etc. Probabilistic models: Bayes Nets, Markov Decision Processes, Hidden Markov Models, etc. Statistical measures: Mean, median, mode, variance, population parameters vs. sample statistics etc. Proximity and error metrics: Cosine similarity, mean-squared error, Manhattan and Euclidean distance, log-loss, etc. Distributions and random sampling: Uniform, normal, binomial, Poisson, etc. Analysis methods: ANOVA, hypothesis testing, factor analysis, etc. Data Modeling and Evaluation Topics Data preprocessing: Munging/wrangling, transforming, aggregating, etc. Pattern recognition: Correlations, clusters, trends, outliers & anomalies, etc. Dimensionality reduction: Eigenvectors, Principal Component Analysis, etc. Prediction: Classification, regression, sequence prediction, etc.; suitable error/accuracy metrics. Evaluation: Training-testing split, sequential vs. randomized cross-validation, etc. Applying Machine Learning Algorithms and Libraries Topics Models: Parametric vs. nonparametric, decision tree, nearest neighbor, neural net, support vector machine, ensemble of multiple models, etc. Learning procedure: Linear regression, gradient descent, genetic algorithms, bagging, boosting, and other model-specific methods; regularization, hyperparameter tuning, etc. Tradeoffs and gotchas: Relative advantages and disadvantages, bias and variance, overfitting and underfitting, vanishing/exploding gradients, missing data, data leakage, etc. Software Engineering and System Design Topics Software interface: Library calls, REST APIs, data collection endpoints, database queries, etc. User interface: Capturing user inputs & application events, displaying results & visualization, etc. Scalability: Map-reduce, distributed processing, etc. Deployment: Cloud hosting, containers & instances, microservices, etc. Move on to the final lesson of this course to find lots of sample practice questions for each topic!
0snap / Graph Alert CorrelationSimple implementation of scientific paper 'GAC: Graph-Based Alert Correlation for the Detection of Distributed Multi-Step Attacks'
MatthewRajan13 / GNN RL Stock PredictorCreating a graph that summarizes correlations between stocks and using a Graph Neural Network to encode that information to be utilized in an RL trading agent
iosonofabio / Lshknnk nearest neighbor (KNN) graphs via Pearson correlation distance and local sensitive hashing (LSH).
RifleZhang / CORD CPDCorrelation-aware Change-point Detection via Graph Neural Networks
RomainLITUD / Multistep Traffic Forecasting By Dynamic Graph ConvolutionMultistep Traffic Forecasting by Dynamic Graph Convolution: Interpretations of Real-Time Spatial Correlations
SalamanderXing / MCSA library for finding the maximum common induced subgraph between two graphs and compute their similarity (correlation).
gs-ai / AMBER ICIAMBER ICI v3: industrial-grade local Ollama command center for multi-model orchestration, live token streaming, graph correlation, investigative file ingestion, agent/chain pipelines, archive search, timeline analysis, OCR extraction, and GPU telemetry.
jiaqima / CopulaGNNCopulaGNN: Towards Integrating Representational and Correlational Roles of Graphs in Graph Neural Networks (ICLR 2021)
queenie88 / A GCNPytorch code for our work: Learning Label Correlations for Multi-Label Image Recognition with Graph Networks
