58 skills found · Page 1 of 2
XiaoxiaoMa-MQ / Awesome Deep Graph Anomaly DetectionAwesome graph anomaly detection techniques built based on deep learning frameworks. Collections of commonly used datasets, papers as well as implementations are listed in this github repository. We also invite researchers interested in anomaly detection, graph representation learning, and graph anomaly detection to join this project as contributors and boost further research in this area.
boostorg / GraphBoost.org graph module
richelbilderbeek / Boost Graph Cookbook 1Boost.Graph Cookbook 1: Basics
mistyreed63849 / Graph LLMGraphLLM: Boosting Graph Reasoning Ability of Large Language Model (IEEE Transactions on Big Data)
dgleich / Matlab BglA graph library for Matlab based on the boost graph library
GemsLab / H2GCNBoost learning for GNNs from the graph structure under challenging heterophily settings. (NeurIPS'20)
Sebastp / Next React Graphql Apollo BoostrapReact + GraphQL + Next.js project architecture that I play with right now
facebookresearch / 3D Vision And TouchWhen told to understand the shape of a new object, the most instinctual approach is to pick it up and inspect it with your hand and eyes in tandem. Here, touch provides high fidelity localized information while vision provides complementary global context. However, in 3D shape reconstruction, the complementary fusion of visual and haptic modalities remains largely unexplored. In this paper, we study this problem and present an effective chart-based approach to fusing vision and touch, which leverages advances in graph convolutional networks. To do so, we introduce a dataset of simulated touch and vision signals from the interaction between a robotic hand and a large array of 3D objects. Our results show that (1) leveraging both vision and touch signals consistently improves single-modality baselines, especially when the object is occluded by the hand touching it; (2) our approach outperforms alternative modality fusion methods and strongly benefits from the proposed chart-based structure; (3) reconstruction quality boosts with the number of grasps provided; and (4) the touch information not only enhances the reconstruction at the touch site but also extrapolates to its local neighborhood.
ulab-uiuc / GoR[ACL'25 Main] Graph of Records: Boosting Retrieval Augmented Generation for Long-context Summarization with Graphs
Balram-1 / Graph GreenerGraph-Greener is a powerful tool designed to enhance your GitHub contribution graph by generating backdated commits. It helps developers maintain an active and visually appealing profile with customizable commit patterns. Easy to use and perfect for boosting your GitHub presence responsibly.
boostorg / Graph ParallelBoost.org graph_parallel module
Yu-Maryland / E BoostBoosted E-Graph Extraction with Adaptive Heuristics and Exact Solving
LechengKong / MAG GNNOfficial implementation of MAG-GNN: an RL-boosted graph learning framework.
skramm / UdgcdUnDirected Graph Cycle Detection: C++ wrapper over Boost Graph Library (BGL)
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!
HKUST-KnowComp / DummyNode4GraphLearningSource Code for ICML 2022 paper "Boosting Graph Structure Learning with Dummy Nodes"
erwinvaneijk / Bgl PythonBoost Graph Library - Python interface. This is the repository with the imported repository from Douglas Gregor
GraphDetec / RF GNNSource code for "RF-GNN: Random Forest Boosted Graph Neural Network for Social Bot Detection"
zzhenggit / Graph Cuts Losscode for ICCVW paper 'Graph Cuts Loss to Boost Model Accuracy and Generalizability for Medical Image Segmentation'.
leonardoarcari / ArlibC++ Alternative Routing Library for Boost.Graph. A configurable, efficient, plug-n-play solution for alternative route planning and k-shortest paths problems.
