Weisfeiler and Leman Go Neural: Higher-order Graph Neural Network.pptxssuser2624f71
This document summarizes a research paper that analyzes graph neural networks (GNNs) and proposes extensions of GNNs. It finds that GNNs are not more powerful than the 1-Weisfeiler-Leman algorithm for distinguishing graphs, but that higher-order GNNs (k-GNNs) are more powerful. It introduces k-GNNs and hierarchical k-GNNs, and finds through experiments that k-GNNs outperform standard GNNs for graph classification and regression tasks.
NS-CUK Seminar: S.T.Nguyen, Review on "DeepGCNs: Can GCNs Go as Deep as CNNs?...ssuser4b1f48
This document summarizes a research paper presentation on developing deeper graph convolutional networks (GCNs). The presentation discusses how stacking more layers in GCNs can lead to vanishing gradients as in CNNs. It explores adapting residual, dense, and dilated connections from deep CNNs to address this for GCNs. Experiments on 3D point cloud segmentation show residual and dense GCNs with 56 layers outperform previous methods, indicating GCNs can now scale in depth similar to CNNs. The techniques help information flow better in deep GCNs to improve semantic segmentation performance.
Graph neural networks (GNNs) are neural network architectures that operate on graph-structured data. GNNs iteratively update node representations by aggregating neighbor representations and can be used for tasks like node classification. There are many frontiers for GNN research, including graph generation/transformation, dynamic/heterogeneous graphs, and applications in domains that can be modeled with graphs like social networks and drug discovery. Automated machine learning techniques are also being applied to GNNs.
This document summarizes a research paper on hypergraph neural networks. It introduces hypergraphs as a generalization of graphs that allows edges to connect any number of vertices. It then discusses how traditional graph neural networks are limited by only modeling pairwise connections. The paper proposes a hypergraph neural networks framework that uses hypergraph structures to better formulate complex data correlations. Key contributions include a hyperedge convolution operation and experiments showing the framework outperforms traditional graph neural networks on citation network classification and visual object classification tasks by capturing multi-modal data representations.
240325_JW_labseminar[node2vec: Scalable Feature Learning for Networks].pptxthanhdowork
This document describes the node2vec algorithm for feature learning in networks. Node2vec uses random walks to sample the neighborhood of nodes in a network. It learns feature representations that maximize the likelihood of preserving network neighborhoods in a low-dimensional space. The algorithm introduces two parameters, p and q, that allow it to flexibly explore node neighborhoods. Experiments on real-world networks show node2vec produces high quality feature representations that achieve strong performance on tasks like multi-label classification and link prediction.
240401_Thuy_Labseminar[Train Once and Explain Everywhere: Pre-training Interp...thanhdowork
The document describes a graph convolutional network (GCN) model that aims to be interpretable and generalizable across different graph datasets. It utilizes a pre-training process on synthetic graphs to learn universal structural patterns. The model features a structural pattern learning module to capture these patterns and a hypergraph refining module to identify explanations incorporating local structural interactions. It is shown to outperform comparable methods on a graph interpretation task without requiring pre-training.
Weisfeiler and Leman Go Neural: Higher-order Graph Neural Network.pptxssuser2624f71
This document summarizes a research paper that analyzes graph neural networks (GNNs) and proposes extensions of GNNs. It finds that GNNs are not more powerful than the 1-Weisfeiler-Leman algorithm for distinguishing graphs, but that higher-order GNNs (k-GNNs) are more powerful. It introduces k-GNNs and hierarchical k-GNNs, and finds through experiments that k-GNNs outperform standard GNNs for graph classification and regression tasks.
NS-CUK Seminar: S.T.Nguyen, Review on "DeepGCNs: Can GCNs Go as Deep as CNNs?...ssuser4b1f48
This document summarizes a research paper presentation on developing deeper graph convolutional networks (GCNs). The presentation discusses how stacking more layers in GCNs can lead to vanishing gradients as in CNNs. It explores adapting residual, dense, and dilated connections from deep CNNs to address this for GCNs. Experiments on 3D point cloud segmentation show residual and dense GCNs with 56 layers outperform previous methods, indicating GCNs can now scale in depth similar to CNNs. The techniques help information flow better in deep GCNs to improve semantic segmentation performance.
Graph neural networks (GNNs) are neural network architectures that operate on graph-structured data. GNNs iteratively update node representations by aggregating neighbor representations and can be used for tasks like node classification. There are many frontiers for GNN research, including graph generation/transformation, dynamic/heterogeneous graphs, and applications in domains that can be modeled with graphs like social networks and drug discovery. Automated machine learning techniques are also being applied to GNNs.
This document summarizes a research paper on hypergraph neural networks. It introduces hypergraphs as a generalization of graphs that allows edges to connect any number of vertices. It then discusses how traditional graph neural networks are limited by only modeling pairwise connections. The paper proposes a hypergraph neural networks framework that uses hypergraph structures to better formulate complex data correlations. Key contributions include a hyperedge convolution operation and experiments showing the framework outperforms traditional graph neural networks on citation network classification and visual object classification tasks by capturing multi-modal data representations.
240325_JW_labseminar[node2vec: Scalable Feature Learning for Networks].pptxthanhdowork
This document describes the node2vec algorithm for feature learning in networks. Node2vec uses random walks to sample the neighborhood of nodes in a network. It learns feature representations that maximize the likelihood of preserving network neighborhoods in a low-dimensional space. The algorithm introduces two parameters, p and q, that allow it to flexibly explore node neighborhoods. Experiments on real-world networks show node2vec produces high quality feature representations that achieve strong performance on tasks like multi-label classification and link prediction.
240401_Thuy_Labseminar[Train Once and Explain Everywhere: Pre-training Interp...thanhdowork
The document describes a graph convolutional network (GCN) model that aims to be interpretable and generalizable across different graph datasets. It utilizes a pre-training process on synthetic graphs to learn universal structural patterns. The model features a structural pattern learning module to capture these patterns and a hypergraph refining module to identify explanations incorporating local structural interactions. It is shown to outperform comparable methods on a graph interpretation task without requiring pre-training.
"Sparse Graph Attention Networks", IEEE Transactions on Knowledge and Data En...ssuser2624f71
This document proposes sparse graph attention networks (SGATs) which integrate a sparse attention mechanism into graph attention networks. SGATs simplify GAT architectures by sharing attention coefficients across heads and layers. SGATs can identify and remove noisy edges from graphs to achieve similar or improved accuracy on classification tasks. The proposed method is tested on several graph datasets and is shown to learn more robust representations compared to GAT, especially on disassortative graphs where GAT fails. Future work involves applying SGATs to edge detection against adversarial attacks and unsupervised domain adaptation.
Graph convolutional neural networks for web-scale recommender systems.pptxssuser2624f71
1. The document discusses the LightGCN model, which was proposed to overcome limitations of the NGCF model for recommender systems.
2. LightGCN simplifies the model design of GCNs by only including the most essential components, without feature transformation or nonlinear activation functions.
3. An experiment comparing LightGCN and NGCF found that LightGCN provided substantial improvements in performance over NGCF.
This document discusses the digital circuit layout problem and approaches to solving it using graph partitioning techniques. It begins by introducing the digital circuit layout problem and how it has become more complex with increasing circuit sizes. It then discusses how the problem can be decomposed into subproblems using graph partitioning to assign geometric coordinates to circuit components. The document reviews several traditional approaches to solve the problem, such as the Kernighan-Lin algorithm, and discusses their limitations for larger circuit sizes. It also discusses more recent approaches using evolutionary algorithms and concludes by analyzing the contributions of various approaches.
This document summarizes research on using graph partitioning techniques to solve digital circuit layout problems. It discusses how the digital circuit layout problem is a constrained optimization problem that is NP-hard. It then reviews previous work on using techniques like min-cut bipartitioning, multi-way partitioning algorithms, and spectral graph partitioning to solve the problem. The document concludes by analyzing evolutionary approaches that have been used, including genetic algorithms, memetic algorithms, ant colony optimization, and particle swarm intelligence. It finds that these approaches are dependent on representation and initialization but can produce quality solutions for small circuits.
Weisfeiler and Leman Go Neural: Higher-order Graph Neural Networks, arXiv e-...ssuser2624f71
This document summarizes research on k-dimensional graph neural networks (k-GNNs), which are a generalization of graph neural networks (GNNs) based on the k-dimensional Weisfeiler-Leman graph isomorphism test. It presents the theoretical basis for k-GNNs, describes the k-GNN model and a hierarchical variant, and reports the results of experimental studies comparing k-GNNs to GNNs and kernel methods on several benchmark datasets. The research found that k-GNNs outperformed GNNs and were able to match the performance of kernel methods, demonstrating their ability to learn graph properties beyond what GNNs can represent.
NS-CUK Seminar: S.T.Nguyen, Review on "Are More Layers Beneficial to Graph Tr...ssuser4b1f48
This document summarizes a research paper on improving the depth of graph transformer models using local attention to graph substructures. The paper proposes adding substructure tokens to the graph and applying local attention between each substructure and its nodes. This addresses limitations in graph transformers' ability to learn substructure features as depth increases. The model achieves state-of-the-art results on graph benchmarks, with performance continuing to improve as depth increases up to 48 layers, demonstrating it alleviates problems of shrinking attention capacity with depth. Ablation studies show local attention and substructure encoding are important for the model's performance, especially on deeper models and datasets where specific substructures are key features.
A Generalization of Transformer Networks to Graphs.pptxssuser2624f71
This document summarizes a research paper on Graph Transformers, which generalizes transformer networks to graph-structured data. It introduces the Graph Transformer model, which addresses two key challenges of applying transformers to graphs: sparsity and positional encodings. The model uses Laplacian eigenvectors to encode node positions and handles sparsity through restricted self-attention. Experiments show the Graph Transformer outperforms GNN baselines on molecular property prediction and node classification tasks. Future work may explore efficient training on large graphs and heterogeneous domains.
Shift-Robust Node Classification via Graph Adversarial Clustering Neurips 202...ssuser2624f71
This document proposes a framework called SRNC to make graph neural networks robust against distribution shifts in graph-structured data. SRNC uses unlabeled target graph data to facilitate model generalization. It incorporates graph adversarial clustering to identify latent classes in the target data by breaking infrequent edges between potential clusters. This allows SRNC to jointly model the target class probability distribution Pr(x_t, y_t) on the graph. The framework can handle both open-set and close-set shifts, whereas previous work only addressed one or the other. Experiments on three benchmarks show SRNC can detect over 70% of open-set samples.
This document summarizes a research paper on sparse graph attention networks (SGATs). SGATs apply an attention mechanism to only a subset of neighbors for each node to improve the scalability and memory efficiency of graph attention networks. The key ideas are a sparse attention mechanism using techniques like neighbor sampling and a binary gate attached to each edge. SGATs show advantages in scalability, memory usage, and performance on disassortative graphs by removing up to 80% of edges while maintaining classification accuracy. Evaluation on synthetic and real-world graphs demonstrates SGATs can identify and remove noisy edges.
EfficientNet: Rethinking Model Scaling for Convolutional Neural Networks.pptxssuser2624f71
EfficientNet proposes a compound scaling method that uniformly scales the width, depth, and resolution of convolutional neural networks. Prior work scaled these dimensions independently without theoretical guidance. The paper finds that balancing the scaling of width, depth, and resolution yields more efficient models. EfficientNet models outperform existing state-of-the-art models with an order of magnitude fewer parameters and floating point operations while achieving better accuracy on ImageNet and other datasets.
This document provides an overview of graph neural networks for node classification. It discusses supervised graph neural network approaches like graph convolutional networks (GCN) and graph attention networks. It also covers unsupervised approaches like variational graph auto-encoders and deep graph infomax. Additionally, it discusses general frameworks for graph neural networks like neural message passing networks and issues like over-smoothing when GNNs become too deep.
NS-CUK Joint Journal Club: S.T.Nguyen, Review on "Neural Sheaf Diffusion: A ...ssuser4b1f48
NS-CUK Joint Journal Club: S.T.Nguyen, Review on "Neural Sheaf Diffusion: A Topological Perspective on Heterophily and Oversmoothing in GNNs" , NeurIPS 2022
NS-CUK Seminar: V.T.Hoang, Review on "GOAT: A Global Transformer on Large-sca...ssuser4b1f48
This document presents GOAT, a scalable global transformer model for graph-structured data. GOAT uses a novel local attention module to absorb rich local information from node neighborhoods, in addition to a global attention mechanism that allows each node to attend to all other nodes. The document reports that GOAT achieves strong performance on large-scale homophilous and heterophilous node classification benchmarks, demonstrating its ability to leverage both local and global graph information for prediction tasks. Ablation studies on codebook size further indicate GOAT's effectiveness at modeling long-range interactions through its global attention.
"Sparse Graph Attention Networks", IEEE Transactions on Knowledge and Data En...ssuser2624f71
This document proposes sparse graph attention networks (SGATs) which integrate a sparse attention mechanism into graph attention networks. SGATs simplify GAT architectures by sharing attention coefficients across heads and layers. SGATs can identify and remove noisy edges from graphs to achieve similar or improved accuracy on classification tasks. The proposed method is tested on several graph datasets and is shown to learn more robust representations compared to GAT, especially on disassortative graphs where GAT fails. Future work involves applying SGATs to edge detection against adversarial attacks and unsupervised domain adaptation.
Graph convolutional neural networks for web-scale recommender systems.pptxssuser2624f71
1. The document discusses the LightGCN model, which was proposed to overcome limitations of the NGCF model for recommender systems.
2. LightGCN simplifies the model design of GCNs by only including the most essential components, without feature transformation or nonlinear activation functions.
3. An experiment comparing LightGCN and NGCF found that LightGCN provided substantial improvements in performance over NGCF.
This document discusses the digital circuit layout problem and approaches to solving it using graph partitioning techniques. It begins by introducing the digital circuit layout problem and how it has become more complex with increasing circuit sizes. It then discusses how the problem can be decomposed into subproblems using graph partitioning to assign geometric coordinates to circuit components. The document reviews several traditional approaches to solve the problem, such as the Kernighan-Lin algorithm, and discusses their limitations for larger circuit sizes. It also discusses more recent approaches using evolutionary algorithms and concludes by analyzing the contributions of various approaches.
This document summarizes research on using graph partitioning techniques to solve digital circuit layout problems. It discusses how the digital circuit layout problem is a constrained optimization problem that is NP-hard. It then reviews previous work on using techniques like min-cut bipartitioning, multi-way partitioning algorithms, and spectral graph partitioning to solve the problem. The document concludes by analyzing evolutionary approaches that have been used, including genetic algorithms, memetic algorithms, ant colony optimization, and particle swarm intelligence. It finds that these approaches are dependent on representation and initialization but can produce quality solutions for small circuits.
Weisfeiler and Leman Go Neural: Higher-order Graph Neural Networks, arXiv e-...ssuser2624f71
This document summarizes research on k-dimensional graph neural networks (k-GNNs), which are a generalization of graph neural networks (GNNs) based on the k-dimensional Weisfeiler-Leman graph isomorphism test. It presents the theoretical basis for k-GNNs, describes the k-GNN model and a hierarchical variant, and reports the results of experimental studies comparing k-GNNs to GNNs and kernel methods on several benchmark datasets. The research found that k-GNNs outperformed GNNs and were able to match the performance of kernel methods, demonstrating their ability to learn graph properties beyond what GNNs can represent.
NS-CUK Seminar: S.T.Nguyen, Review on "Are More Layers Beneficial to Graph Tr...ssuser4b1f48
This document summarizes a research paper on improving the depth of graph transformer models using local attention to graph substructures. The paper proposes adding substructure tokens to the graph and applying local attention between each substructure and its nodes. This addresses limitations in graph transformers' ability to learn substructure features as depth increases. The model achieves state-of-the-art results on graph benchmarks, with performance continuing to improve as depth increases up to 48 layers, demonstrating it alleviates problems of shrinking attention capacity with depth. Ablation studies show local attention and substructure encoding are important for the model's performance, especially on deeper models and datasets where specific substructures are key features.
A Generalization of Transformer Networks to Graphs.pptxssuser2624f71
This document summarizes a research paper on Graph Transformers, which generalizes transformer networks to graph-structured data. It introduces the Graph Transformer model, which addresses two key challenges of applying transformers to graphs: sparsity and positional encodings. The model uses Laplacian eigenvectors to encode node positions and handles sparsity through restricted self-attention. Experiments show the Graph Transformer outperforms GNN baselines on molecular property prediction and node classification tasks. Future work may explore efficient training on large graphs and heterogeneous domains.
Shift-Robust Node Classification via Graph Adversarial Clustering Neurips 202...ssuser2624f71
This document proposes a framework called SRNC to make graph neural networks robust against distribution shifts in graph-structured data. SRNC uses unlabeled target graph data to facilitate model generalization. It incorporates graph adversarial clustering to identify latent classes in the target data by breaking infrequent edges between potential clusters. This allows SRNC to jointly model the target class probability distribution Pr(x_t, y_t) on the graph. The framework can handle both open-set and close-set shifts, whereas previous work only addressed one or the other. Experiments on three benchmarks show SRNC can detect over 70% of open-set samples.
This document summarizes a research paper on sparse graph attention networks (SGATs). SGATs apply an attention mechanism to only a subset of neighbors for each node to improve the scalability and memory efficiency of graph attention networks. The key ideas are a sparse attention mechanism using techniques like neighbor sampling and a binary gate attached to each edge. SGATs show advantages in scalability, memory usage, and performance on disassortative graphs by removing up to 80% of edges while maintaining classification accuracy. Evaluation on synthetic and real-world graphs demonstrates SGATs can identify and remove noisy edges.
EfficientNet: Rethinking Model Scaling for Convolutional Neural Networks.pptxssuser2624f71
EfficientNet proposes a compound scaling method that uniformly scales the width, depth, and resolution of convolutional neural networks. Prior work scaled these dimensions independently without theoretical guidance. The paper finds that balancing the scaling of width, depth, and resolution yields more efficient models. EfficientNet models outperform existing state-of-the-art models with an order of magnitude fewer parameters and floating point operations while achieving better accuracy on ImageNet and other datasets.
This document provides an overview of graph neural networks for node classification. It discusses supervised graph neural network approaches like graph convolutional networks (GCN) and graph attention networks. It also covers unsupervised approaches like variational graph auto-encoders and deep graph infomax. Additionally, it discusses general frameworks for graph neural networks like neural message passing networks and issues like over-smoothing when GNNs become too deep.
NS-CUK Joint Journal Club: S.T.Nguyen, Review on "Neural Sheaf Diffusion: A ...ssuser4b1f48
NS-CUK Joint Journal Club: S.T.Nguyen, Review on "Neural Sheaf Diffusion: A Topological Perspective on Heterophily and Oversmoothing in GNNs" , NeurIPS 2022
Similar to NS-CUK Seminar: J.H.Lee, Review on "Rethinking the Expressive Power of GNNs via Graph Biconnectivity", ICLR 2023 (20)
NS-CUK Seminar: V.T.Hoang, Review on "GOAT: A Global Transformer on Large-sca...ssuser4b1f48
This document presents GOAT, a scalable global transformer model for graph-structured data. GOAT uses a novel local attention module to absorb rich local information from node neighborhoods, in addition to a global attention mechanism that allows each node to attend to all other nodes. The document reports that GOAT achieves strong performance on large-scale homophilous and heterophilous node classification benchmarks, demonstrating its ability to leverage both local and global graph information for prediction tasks. Ablation studies on codebook size further indicate GOAT's effectiveness at modeling long-range interactions through its global attention.
NS-CUK Seminar: H.B.Kim, Review on "Cluster-GCN: An Efficient Algorithm for ...ssuser4b1f48
This document summarizes the Cluster-GCN method for training graph convolutional networks (GCNs) in a memory-efficient and scalable way. The key contributions of Cluster-GCN are that it achieves the best memory usage for training GCNs on large graphs, especially deep GCNs, while maintaining training speed comparable to or faster than existing methods. Experimental results demonstrate that Cluster-GCN can efficiently train very deep GCNs on large graphs and achieve state-of-the-art performance.
This document summarizes a research paper on Gate Graph Sequence Neural Networks (GGSNN). GGSNN is a model that incorporates time dependencies and higher-order relationships in graphs using GRU-based methods. It generates an output sequence to allow for graph-level analysis. The model can be used for a wide range of tasks involving logical formulas. It uses GRU to compute slopes via backpropagation over time, allowing it to capture long-term dependencies between output time steps. Node representations in GGSNN can be updated over time using label data, unlike previous graph neural networks.
NS-CUK Journal club: H.E.Lee, Review on " A biomedical knowledge graph-based ...ssuser4b1f48
1) The document proposes a deep learning framework called DeepLGF to predict drug-drug interactions by combining local and global feature extraction from biomedical knowledge graphs.
2) DeepLGF uses graph neural networks and knowledge graph embedding methods to extract local drug features from chemical structures and biological functions, and global features from the relationships between drugs and other biological entities.
3) Experimental results on prediction tasks using several drug interaction datasets demonstrate that DeepLGF outperforms other state-of-the-art models and has promising applications in drug development and clinical use.
NS-CUK Seminar: H.B.Kim, Review on "Inductive Representation Learning on Lar...ssuser4b1f48
1. The document summarizes the GraphSAGE framework for inductive node embedding proposed by Hamilton et al.
2. GraphSAGE leverages node features to learn an embedding function that generalizes to unseen nodes using a sample and aggregate approach.
3. Across citation, Reddit, and other datasets, GraphSAGE improves classification F1-scores by 51% on average compared to using node features alone and outperforms strong baselines.
NS-CUK Seminar: J.H.Lee, Review on "Relational Self-Supervised Learning on Gr...ssuser4b1f48
This document proposes a new self-supervised learning framework called Relational Graph Representation Learning (RGRL). RGRL aims to learn node representations that preserve relationships between nodes even after augmentation. It does this by focusing training on low-degree nodes and using both global and local contexts to sample anchor nodes. Experiments on 14 real-world datasets show RGRL outperforms previous methods on tasks like node classification and link prediction.
NS-CUK Seminar: H.E.Lee, Review on "Structural Deep Embedding for Hyper-Netw...ssuser4b1f48
This document presents a Deep Hyper-Network Embedding (DHNE) model to learn low-dimensional representations of hypernetworks. The DHNE model uses an autoencoder and fully connected layer to preserve both local and global proximity in the embedding space. The DHNE model is tested on four datasets and outperforms other network embedding methods on tasks like network reconstruction, link prediction, and classification. The DHNE model can learn embeddings of hypernetworks while preserving the decomposability property of hyperedges using a nonlinear tuple similarity function.
Threats to mobile devices are more prevalent and increasing in scope and complexity. Users of mobile devices desire to take full advantage of the features
available on those devices, but many of the features provide convenience and capability but sacrifice security. This best practices guide outlines steps the users can take to better protect personal devices and information.
AI 101: An Introduction to the Basics and Impact of Artificial IntelligenceIndexBug
Imagine a world where machines not only perform tasks but also learn, adapt, and make decisions. This is the promise of Artificial Intelligence (AI), a technology that's not just enhancing our lives but revolutionizing entire industries.
In the rapidly evolving landscape of technologies, XML continues to play a vital role in structuring, storing, and transporting data across diverse systems. The recent advancements in artificial intelligence (AI) present new methodologies for enhancing XML development workflows, introducing efficiency, automation, and intelligent capabilities. This presentation will outline the scope and perspective of utilizing AI in XML development. The potential benefits and the possible pitfalls will be highlighted, providing a balanced view of the subject.
We will explore the capabilities of AI in understanding XML markup languages and autonomously creating structured XML content. Additionally, we will examine the capacity of AI to enrich plain text with appropriate XML markup. Practical examples and methodological guidelines will be provided to elucidate how AI can be effectively prompted to interpret and generate accurate XML markup.
Further emphasis will be placed on the role of AI in developing XSLT, or schemas such as XSD and Schematron. We will address the techniques and strategies adopted to create prompts for generating code, explaining code, or refactoring the code, and the results achieved.
The discussion will extend to how AI can be used to transform XML content. In particular, the focus will be on the use of AI XPath extension functions in XSLT, Schematron, Schematron Quick Fixes, or for XML content refactoring.
The presentation aims to deliver a comprehensive overview of AI usage in XML development, providing attendees with the necessary knowledge to make informed decisions. Whether you’re at the early stages of adopting AI or considering integrating it in advanced XML development, this presentation will cover all levels of expertise.
By highlighting the potential advantages and challenges of integrating AI with XML development tools and languages, the presentation seeks to inspire thoughtful conversation around the future of XML development. We’ll not only delve into the technical aspects of AI-powered XML development but also discuss practical implications and possible future directions.
Taking AI to the Next Level in Manufacturing.pdfssuserfac0301
Read Taking AI to the Next Level in Manufacturing to gain insights on AI adoption in the manufacturing industry, such as:
1. How quickly AI is being implemented in manufacturing.
2. Which barriers stand in the way of AI adoption.
3. How data quality and governance form the backbone of AI.
4. Organizational processes and structures that may inhibit effective AI adoption.
6. Ideas and approaches to help build your organization's AI strategy.
HCL Notes and Domino License Cost Reduction in the World of DLAUpanagenda
Webinar Recording: https://www.panagenda.com/webinars/hcl-notes-and-domino-license-cost-reduction-in-the-world-of-dlau/
The introduction of DLAU and the CCB & CCX licensing model caused quite a stir in the HCL community. As a Notes and Domino customer, you may have faced challenges with unexpected user counts and license costs. You probably have questions on how this new licensing approach works and how to benefit from it. Most importantly, you likely have budget constraints and want to save money where possible. Don’t worry, we can help with all of this!
We’ll show you how to fix common misconfigurations that cause higher-than-expected user counts, and how to identify accounts which you can deactivate to save money. There are also frequent patterns that can cause unnecessary cost, like using a person document instead of a mail-in for shared mailboxes. We’ll provide examples and solutions for those as well. And naturally we’ll explain the new licensing model.
Join HCL Ambassador Marc Thomas in this webinar with a special guest appearance from Franz Walder. It will give you the tools and know-how to stay on top of what is going on with Domino licensing. You will be able lower your cost through an optimized configuration and keep it low going forward.
These topics will be covered
- Reducing license cost by finding and fixing misconfigurations and superfluous accounts
- How do CCB and CCX licenses really work?
- Understanding the DLAU tool and how to best utilize it
- Tips for common problem areas, like team mailboxes, functional/test users, etc
- Practical examples and best practices to implement right away
Ivanti’s Patch Tuesday breakdown goes beyond patching your applications and brings you the intelligence and guidance needed to prioritize where to focus your attention first. Catch early analysis on our Ivanti blog, then join industry expert Chris Goettl for the Patch Tuesday Webinar Event. There we’ll do a deep dive into each of the bulletins and give guidance on the risks associated with the newly-identified vulnerabilities.
Driving Business Innovation: Latest Generative AI Advancements & Success StorySafe Software
Are you ready to revolutionize how you handle data? Join us for a webinar where we’ll bring you up to speed with the latest advancements in Generative AI technology and discover how leveraging FME with tools from giants like Google Gemini, Amazon, and Microsoft OpenAI can supercharge your workflow efficiency.
During the hour, we’ll take you through:
Guest Speaker Segment with Hannah Barrington: Dive into the world of dynamic real estate marketing with Hannah, the Marketing Manager at Workspace Group. Hear firsthand how their team generates engaging descriptions for thousands of office units by integrating diverse data sources—from PDF floorplans to web pages—using FME transformers, like OpenAIVisionConnector and AnthropicVisionConnector. This use case will show you how GenAI can streamline content creation for marketing across the board.
Ollama Use Case: Learn how Scenario Specialist Dmitri Bagh has utilized Ollama within FME to input data, create custom models, and enhance security protocols. This segment will include demos to illustrate the full capabilities of FME in AI-driven processes.
Custom AI Models: Discover how to leverage FME to build personalized AI models using your data. Whether it’s populating a model with local data for added security or integrating public AI tools, find out how FME facilitates a versatile and secure approach to AI.
We’ll wrap up with a live Q&A session where you can engage with our experts on your specific use cases, and learn more about optimizing your data workflows with AI.
This webinar is ideal for professionals seeking to harness the power of AI within their data management systems while ensuring high levels of customization and security. Whether you're a novice or an expert, gain actionable insights and strategies to elevate your data processes. Join us to see how FME and AI can revolutionize how you work with data!
UiPath Test Automation using UiPath Test Suite series, part 6DianaGray10
Welcome to UiPath Test Automation using UiPath Test Suite series part 6. In this session, we will cover Test Automation with generative AI and Open AI.
UiPath Test Automation with generative AI and Open AI webinar offers an in-depth exploration of leveraging cutting-edge technologies for test automation within the UiPath platform. Attendees will delve into the integration of generative AI, a test automation solution, with Open AI advanced natural language processing capabilities.
Throughout the session, participants will discover how this synergy empowers testers to automate repetitive tasks, enhance testing accuracy, and expedite the software testing life cycle. Topics covered include the seamless integration process, practical use cases, and the benefits of harnessing AI-driven automation for UiPath testing initiatives. By attending this webinar, testers, and automation professionals can gain valuable insights into harnessing the power of AI to optimize their test automation workflows within the UiPath ecosystem, ultimately driving efficiency and quality in software development processes.
What will you get from this session?
1. Insights into integrating generative AI.
2. Understanding how this integration enhances test automation within the UiPath platform
3. Practical demonstrations
4. Exploration of real-world use cases illustrating the benefits of AI-driven test automation for UiPath
Topics covered:
What is generative AI
Test Automation with generative AI and Open AI.
UiPath integration with generative AI
Speaker:
Deepak Rai, Automation Practice Lead, Boundaryless Group and UiPath MVP
In his public lecture, Christian Timmerer provides insights into the fascinating history of video streaming, starting from its humble beginnings before YouTube to the groundbreaking technologies that now dominate platforms like Netflix and ORF ON. Timmerer also presents provocative contributions of his own that have significantly influenced the industry. He concludes by looking at future challenges and invites the audience to join in a discussion.
HCL Notes und Domino Lizenzkostenreduzierung in der Welt von DLAUpanagenda
Webinar Recording: https://www.panagenda.com/webinars/hcl-notes-und-domino-lizenzkostenreduzierung-in-der-welt-von-dlau/
DLAU und die Lizenzen nach dem CCB- und CCX-Modell sind für viele in der HCL-Community seit letztem Jahr ein heißes Thema. Als Notes- oder Domino-Kunde haben Sie vielleicht mit unerwartet hohen Benutzerzahlen und Lizenzgebühren zu kämpfen. Sie fragen sich vielleicht, wie diese neue Art der Lizenzierung funktioniert und welchen Nutzen sie Ihnen bringt. Vor allem wollen Sie sicherlich Ihr Budget einhalten und Kosten sparen, wo immer möglich. Das verstehen wir und wir möchten Ihnen dabei helfen!
Wir erklären Ihnen, wie Sie häufige Konfigurationsprobleme lösen können, die dazu führen können, dass mehr Benutzer gezählt werden als nötig, und wie Sie überflüssige oder ungenutzte Konten identifizieren und entfernen können, um Geld zu sparen. Es gibt auch einige Ansätze, die zu unnötigen Ausgaben führen können, z. B. wenn ein Personendokument anstelle eines Mail-Ins für geteilte Mailboxen verwendet wird. Wir zeigen Ihnen solche Fälle und deren Lösungen. Und natürlich erklären wir Ihnen das neue Lizenzmodell.
Nehmen Sie an diesem Webinar teil, bei dem HCL-Ambassador Marc Thomas und Gastredner Franz Walder Ihnen diese neue Welt näherbringen. Es vermittelt Ihnen die Tools und das Know-how, um den Überblick zu bewahren. Sie werden in der Lage sein, Ihre Kosten durch eine optimierte Domino-Konfiguration zu reduzieren und auch in Zukunft gering zu halten.
Diese Themen werden behandelt
- Reduzierung der Lizenzkosten durch Auffinden und Beheben von Fehlkonfigurationen und überflüssigen Konten
- Wie funktionieren CCB- und CCX-Lizenzen wirklich?
- Verstehen des DLAU-Tools und wie man es am besten nutzt
- Tipps für häufige Problembereiche, wie z. B. Team-Postfächer, Funktions-/Testbenutzer usw.
- Praxisbeispiele und Best Practices zum sofortigen Umsetzen
Fueling AI with Great Data with Airbyte WebinarZilliz
This talk will focus on how to collect data from a variety of sources, leveraging this data for RAG and other GenAI use cases, and finally charting your course to productionalization.
Building Production Ready Search Pipelines with Spark and MilvusZilliz
Spark is the widely used ETL tool for processing, indexing and ingesting data to serving stack for search. Milvus is the production-ready open-source vector database. In this talk we will show how to use Spark to process unstructured data to extract vector representations, and push the vectors to Milvus vector database for search serving.
Monitoring and Managing Anomaly Detection on OpenShift.pdfTosin Akinosho
Monitoring and Managing Anomaly Detection on OpenShift
Overview
Dive into the world of anomaly detection on edge devices with our comprehensive hands-on tutorial. This SlideShare presentation will guide you through the entire process, from data collection and model training to edge deployment and real-time monitoring. Perfect for those looking to implement robust anomaly detection systems on resource-constrained IoT/edge devices.
Key Topics Covered
1. Introduction to Anomaly Detection
- Understand the fundamentals of anomaly detection and its importance in identifying unusual behavior or failures in systems.
2. Understanding Edge (IoT)
- Learn about edge computing and IoT, and how they enable real-time data processing and decision-making at the source.
3. What is ArgoCD?
- Discover ArgoCD, a declarative, GitOps continuous delivery tool for Kubernetes, and its role in deploying applications on edge devices.
4. Deployment Using ArgoCD for Edge Devices
- Step-by-step guide on deploying anomaly detection models on edge devices using ArgoCD.
5. Introduction to Apache Kafka and S3
- Explore Apache Kafka for real-time data streaming and Amazon S3 for scalable storage solutions.
6. Viewing Kafka Messages in the Data Lake
- Learn how to view and analyze Kafka messages stored in a data lake for better insights.
7. What is Prometheus?
- Get to know Prometheus, an open-source monitoring and alerting toolkit, and its application in monitoring edge devices.
8. Monitoring Application Metrics with Prometheus
- Detailed instructions on setting up Prometheus to monitor the performance and health of your anomaly detection system.
9. What is Camel K?
- Introduction to Camel K, a lightweight integration framework built on Apache Camel, designed for Kubernetes.
10. Configuring Camel K Integrations for Data Pipelines
- Learn how to configure Camel K for seamless data pipeline integrations in your anomaly detection workflow.
11. What is a Jupyter Notebook?
- Overview of Jupyter Notebooks, an open-source web application for creating and sharing documents with live code, equations, visualizations, and narrative text.
12. Jupyter Notebooks with Code Examples
- Hands-on examples and code snippets in Jupyter Notebooks to help you implement and test anomaly detection models.
AI-Powered Food Delivery Transforming App Development in Saudi Arabia.pdfTechgropse Pvt.Ltd.
In this blog post, we'll delve into the intersection of AI and app development in Saudi Arabia, focusing on the food delivery sector. We'll explore how AI is revolutionizing the way Saudi consumers order food, how restaurants manage their operations, and how delivery partners navigate the bustling streets of cities like Riyadh, Jeddah, and Dammam. Through real-world case studies, we'll showcase how leading Saudi food delivery apps are leveraging AI to redefine convenience, personalization, and efficiency.
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Boost your website's visibility with proven SEO techniques! Our latest blog dives into essential strategies to enhance your online presence, increase traffic, and rank higher on search engines. From keyword optimization to quality content creation, learn how to make your site stand out in the crowded digital landscape. Discover actionable tips and expert insights to elevate your SEO game.
Best 20 SEO Techniques To Improve Website Visibility In SERP
NS-CUK Seminar: J.H.Lee, Review on "Rethinking the Expressive Power of GNNs via Graph Biconnectivity", ICLR 2023
1. Joo-Ho Lee
School of Computer Science and Information Engineering,
The Catholic University of Korea
E-mail: jooho414@gmail.com
2023-05-05
2. 1
Introduction
Problem Statement
• Most of these works mainly justify their expressiveness by giving toy examples where WL algorithms fail to
distinguish
On the theoretical side, it is quite unclear what additional power they can systematically and provably gain
• There is still a lack of principled and convincing metrics beyond the WL hierarchy to formally measure the
expressive power and to guide the design of provably better GNN architectures
3. 2
Introduction
Problem Statement
• Biconnectivity provides a structural explanation by breaking down the intrinsic structure of the graph, connecting
it, and making it into a tree structure
• Problems related to biconnectivity can be solved efficiently through classical algorithms, and it is expected that
there will be GNNs that can solve these problems
However, in this paper, contrary to these expectations, a deep analysis of four representative GNN
structures found that none of them solved the biconnectivity problem
4. 3
Introduction
Contribution
• They systematically study the problem of designing expressive GNNs from a novel perspective of graph
biconnectivity
• They analyze the new GNN structure, Equivariant Subgraph Aggregation Network (ESAN), and demonstrate
that the DSS-WL algorithm can accurately identify cut vertices and cut edges
• Through this, they have expanded understanding of the expressive power of the DSS-WL algorithm and recent
extensions, as well as providing a fine-grained analysis of key factors such as graph generation policies and
aggregation methods
• The main contribution in this paper is then to give a principled and efficient way to design GNNs that are
expressive for biconnectivity problems
6. 5
Methodology
Generalized Distance Weisfeiler-Lehman Test
• SPD-WL for edge-biconnectivity
SPD-WL is a more complex algorithm that determines the color of each node by aggregating the colors of all
nodes within the k-distance as well as neighboring nodes
7. 6
Methodology
Generalized Distance Weisfeiler-Lehman Test
• RD-WL for vertex-biconnectivity
It shows that there is not enough expression for the vertex-biconnectivity problem.
To overcome this, this paper proposes a new distance measurement method called Resistance Distance (RD)
12. 11
Conclusion
• In this paper, they systematically investigate the expressive power of GNNs via the perspective of graph
biconnectivity
• They then introduce the principled GD-WL framework that is fully expressive for all biconnectivity metrics
• They further design the Graphormer-GD architecture that is provably powerful while enjoying practical efficiency
and parallelizability
• Experiments on both synthetic and real-world datasets demonstrate the effectiveness of Graphormer-GD
13. 12
Conclusion
1. it remains an important open problem whether biconnectivity can be solved more efficiently in 𝑂(𝑛2) time using
equivariant GNNs
2. a deep understanding of GD-WL is generally lacking.
3. it may be interesting to further investigate more expressive distance (structural) encoding schemes beyond RD-
WL and explore how to encode them in Graph Transformers
4. Finally, one can extend biconnectivity to a hierarchy of higher-order variants (e.g., tri-connectivity), which
provides a completely different view parallel to the WL hierarchy to study the expressive power and guide
designing provably powerful GNNs architectures
There are still many promising directions that have not yet been explored
Editor's Notes
이미 이전에 propagation을 활용하여 rumor detection 하는 논문들을 모두 봤었다.
이미 이전에 propagation을 활용하여 rumor detection 하는 논문들을 모두 봤었다.
이미 이전에 propagation을 활용하여 rumor detection 하는 논문들을 모두 봤었다.
이미 이전에 propagation을 활용하여 rumor detection 하는 논문들을 모두 봤었다.
이미 이전에 propagation을 활용하여 rumor detection 하는 논문들을 모두 봤었다.
이미 이전에 propagation을 활용하여 rumor detection 하는 논문들을 모두 봤었다.
이미 이전에 propagation을 활용하여 rumor detection 하는 논문들을 모두 봤었다.
이미 이전에 propagation을 활용하여 rumor detection 하는 논문들을 모두 봤었다.
이미 이전에 propagation을 활용하여 rumor detection 하는 논문들을 모두 봤었다.
이미 이전에 propagation을 활용하여 rumor detection 하는 논문들을 모두 봤었다.
이미 이전에 propagation을 활용하여 rumor detection 하는 논문들을 모두 봤었다.
이미 이전에 propagation을 활용하여 rumor detection 하는 논문들을 모두 봤었다.