Graph neural network - PowerPoint PPT Presentation


Graph Machine Learning Overview: Traditional ML to Graph Neural Networks

Explore the evolution of Machine Learning in Graphs, from traditional ML tasks to advanced Graph Neural Networks (GNNs). Discover key concepts like feature engineering, tools like PyG, and types of ML tasks in graphs. Uncover insights into node-level, graph-level, and community-level predictions, an

3 views • 87 slides


Rainfall-Runoff Modelling Using Artificial Neural Network: A Case Study of Purna Sub-catchment, India

Rainfall-runoff modeling is crucial in understanding the relationship between rainfall and runoff. This study focuses on developing a rainfall-runoff model for the Upper Tapi basin in India using Artificial Neural Networks (ANNs). ANNs mimic the human brain's capabilities and have been widely used i

0 views • 26 slides



Localised Adaptive Spatial-Temporal Graph Neural Network

This paper introduces the Localised Adaptive Spatial-Temporal Graph Neural Network model, focusing on the importance of spatial-temporal data modeling in graph structures. The challenges of balancing spatial and temporal dependencies for accurate inference are addressed, along with the use of distri

3 views • 19 slides


Graph Neural Networks

Graph Neural Networks (GNNs) are a versatile form of neural networks that encompass various network architectures like NNs, CNNs, and RNNs, as well as unsupervised learning models such as RBM and DBNs. They find applications in diverse fields such as object detection, machine translation, and drug d

2 views • 48 slides


Understanding Neo4j Graph Database Fundamentals

This comprehensive presentation delves into the fundamentals of Neo4j graph database, covering topics such as the definition of graph databases, reasons for their usage, insights into Neo4j and Cypher, practical applications like data flow analysis, and hands-on instructions on creating and querying

0 views • 20 slides


Understanding Keras Functional API for Neural Networks

Explore the Keras Functional API for building complex neural network models that go beyond sequential structures. Learn how to create computational graphs, handle non-sequential models, and understand the directed graph of computations involved in deep learning. Discover the flexibility and power of

1 views • 12 slides


Exploring Graph-Based Data Science: Opportunities, Challenges, and Techniques

Graph-based data science offers a powerful approach to analyzing data by leveraging graph structures. This involves using graph representation, analysis algorithms, ML/AI techniques, kernels, embeddings, and neural networks. Real-world examples show the utility of data graphs in various domains like

3 views • 37 slides


Understanding Artificial Neural Networks From Scratch

Learn how to build artificial neural networks from scratch, focusing on multi-level feedforward networks like multi-level perceptrons. Discover how neural networks function, including training large networks in parallel and distributed systems, and grasp concepts such as learning non-linear function

1 views • 33 slides


A Deep Dive into Neural Network Units and Language Models

Explore the fundamentals of neural network units in language models, discussing computation, weights, biases, and activations. Understand the essence of weighted sums in neural networks and the application of non-linear activation functions like sigmoid, tanh, and ReLU. Dive into the heart of neural

0 views • 81 slides


Assistive Speech System for Individuals with Speech Impediments Using Neural Networks

Individuals with speech impediments face challenges with speech-to-text software, and this paper introduces a system leveraging Artificial Neural Networks to assist. The technology showcases state-of-the-art performance in various applications, including speech recognition. The system utilizes featu

1 views • 19 slides


Exploring Neural Quantum States and Symmetries in Quantum Mechanics

This article delves into the intricacies of anti-symmetrized neural quantum states and the application of neural networks in solving for the ground-state wave function of atomic nuclei. It discusses the setup using the Rayleigh-Ritz variational principle, neural quantum states (NQSs), variational pa

1 views • 15 slides


Understanding Spiking Neurons and Spiking Neural Networks

Spiking neural networks (SNNs) are a new approach modeled after the brain's operations, aiming for low-power neurons, billions of connections, and high accuracy training algorithms. Spiking neurons have unique features and are more energy-efficient than traditional artificial neural networks. Explor

5 views • 23 slides


Exploring Deep Graph Theory: Philosophical Implications and Misconceptions

Delve into the realm of Deep Graph Theory where graph theory statements are analyzed beyond their conventional scope to uncover philosophical insights and correct misunderstandings. Discover the essence of trees, forests, and the unique relationship where every tree is regarded as a forest. Addition

0 views • 13 slides


Understanding Word2Vec: Creating Dense Vectors for Neural Networks

Word2Vec is a technique used to create dense vectors to represent words in neural networks. By distinguishing target and context words, the network input and output layers are defined. Through training, the neural network predicts target words and minimizes loss. The hidden layer's neuron count dete

7 views • 12 slides


Introduction to Neural Networks in IBM SPSS Modeler 14.2

This presentation provides an introduction to neural networks in IBM SPSS Modeler 14.2. It covers the concepts of directed data mining using neural networks, the structure of neural networks, terms associated with neural networks, and the process of inputs and outputs in neural network models. The d

0 views • 18 slides


Understanding Graph Theory Fundamentals

Delve into the basics of graph theory with topics like graph embeddings, graph plotting, Kuratowski's theorem, planar graphs, Euler characteristic, trees, and more. Explore the principles behind graphs, their properties, and key theorems that define their structure and connectivity.

0 views • 17 slides


Association Rules with Graph Patterns: Exploring Relationships in Data

Dive into the world of association rules with graph patterns, where relationships and connections are analyzed through nodes and edges. Discover how to define association rules, identify customers, and uncover interesting patterns using graph-based techniques. Explore traditional and graph-pattern a

2 views • 18 slides


Solving the Professors to Coffee Lounge Problem: A Graph Theory Approach

An intriguing mathematical problem is presented where new faculty members at TIMS must be assigned to coffee lounge alcoves in a way that ensures no two new members meet after the first day. By constructing a graph based on meet-up timings, analyzing clashes, and determining intervals, this scenario

1 views • 19 slides


Exploring the Impact of Randomness on Planted 3-Coloring Models

In this study by Uriel Feige and Roee David from the Weizmann Institute, the effect of randomness on planted 3-coloring models is investigated. The research delves into the NP-hard nature of 3-coloring problems, introducing a hosted coloring framework that involves choices like the host graph and th

0 views • 55 slides


Managing Large Graphs on Multi-Cores with Graph Awareness

This research discusses the challenges in managing large graphs on multi-core systems and introduces Grace, an in-memory graph management and processing system with optimizations for graph-specific and multi-core-specific operations. The system keeps the entire graph in memory in smaller parts and p

0 views • 14 slides


Maria's Bike Journey Graph Analysis

Maria's bike journey graph depicts her distance from home as she rode to meet friends and run errands before returning home. The graph shows her stops for errands, changes in direction, and her path back home. By interpreting the key features of the graph, such as intercepts and intervals, we can an

0 views • 15 slides


Understanding Advanced Classifiers and Neural Networks

This content explores the concept of advanced classifiers like Neural Networks which compose complex relationships through combining perceptrons. It delves into the workings of the classic perceptron and how modern neural networks use more complex decision functions. The visuals provided offer a cle

0 views • 26 slides


Balanced Graph Edge Partition and Its Practical Applications

Balanced graph edge partitioning is a crucial problem in graph computation, machine learning, and graph databases. It involves partitioning a graph's vertices or edges into balanced components while minimizing cut costs. This process is essential for various real-world applications such as iterative

0 views • 17 slides


Understanding Neural Processing and the Endocrine System

Explore the intricate communication network of the nervous system, from nerve cells transmitting messages to the role of dendrites and axons in neural transmission. Learn about the importance of insulation in neuron communication, the speed of neural impulses, and the processes involved in triggerin

0 views • 24 slides


Exploring Compartmental Models and Adding Detail in Neural Network Biological Modeling

Week 4 delves into compartmental models and the addition of synaptic and cable equation details in biological modeling of neural networks. The content is presented by Wulfram Gerstner from EPFL, Lausanne, Switzerland, providing insights into reducing and adding complexity for a comprehensive underst

0 views • 55 slides


Understanding Neural Network Learning and Perceptrons

Explore the world of neural network learning, including topics like support vector machines, unsupervised learning, and the use of feed-forward perceptrons. Dive into the concepts of gradient descent and how it helps in minimizing errors in neural networks. Visualize the process through graphical ex

0 views • 54 slides


Neural Network for Car-Passenger Matching in Ride-Hailing Services by Karim Akhnoukh

In his M.Sc. thesis, Karim Akhnoukh explores the use of a neural network for car-passenger matching in ride-hailing services. The research delves into solving complex optimization problems related to vehicle routing and passenger matching using innovative algorithms. The study showcases the applicat

0 views • 33 slides


Neural Network Control for Seismometer Temperature Stabilization

Utilizing neural networks, this project aims to enhance seismometer temperature stabilization by implementing nonlinear control to address system nonlinearities. The goal is to improve control performance, decrease overshoot, and allow adaptability to unpredictable parameters. The implementation of

0 views • 24 slides


Vertex-Centric Programming for Graph Neural Networks

Seastar presents a vertex-centric programming approach for Graph Neural Networks, showcasing better performance in graph analytic tasks compared to traditional methods. The research introduces the SEAStar computation pattern and discusses GNN programming abstractions, execution, and limitations. Dee

0 views • 17 slides


Graph Property Testing and Algorithms Overview

Explore testable bounded degree graph properties, sparse graphs, d-bounded degree graphs, hyperfinite graphs, arboricity, maximum matching algorithms, and sublinear time approximation algorithms in graph data streams. Learn about various graph models and properties with examples, showcasing the impo

0 views • 53 slides


Machine Learning and Artificial Neural Networks for Face Verification: Overview and Applications

In the realm of computer vision, the integration of machine learning and artificial neural networks has enabled significant advancements in face verification tasks. Leveraging the brain's inherent pattern recognition capabilities, AI systems can analyze vast amounts of data to enhance face detection

0 views • 13 slides


Understanding Graph Modeling and DFS Applications

Explore the world of graph modeling and DFS applications through lectures on graph vocabulary, edge classification in directed graphs, and the use of DFS to find cycles. Discover the significance of tree edges, back edges, forward edges, and cross edges in graph traversal. Learn how DFS can be utili

0 views • 32 slides


Exploring Efficient Hardware Architectures for Deep Neural Network Processing

Discover new hardware architectures designed for efficient deep neural network processing, including SCNN accelerators for compressed-sparse Convolutional Neural Networks. Learn about convolution operations, memory size versus access energy, dataflow decisions for reuse, and Planar Tiled-Input Stati

0 views • 23 slides


Solving Train Track Problems Using Interval Graphs and Graph Coloring

Presented by Manvitha Nellore, this content addresses real-world train track problems in busy cities by proposing solutions through interval graphs and graph theory. The approach involves allotting tracks to trains by scheduling with time intervals to avoid conflicts. An interval graph is defined, a

0 views • 15 slides


Understanding Neural Network Training and Structure

This text delves into training a neural network, covering concepts such as weight space symmetries, error back-propagation, and ways to improve convergence. It also discusses the layer structures and notation of a neural network, emphasizing the importance of finding optimal sets of weights and offs

0 views • 31 slides


Exploring Variability and Noise in Neural Networks

Understanding the variability of spike trains and sources of variability in neural networks, dissecting if variability is equivalent to noise. Delving into the Poisson model, stochastic spike arrival, and firing, and biological modeling of neural networks. Examining variability in different brain re

0 views • 71 slides


Understanding Neural Network Watermarking Technologies

Neural networks are being deployed in various domains like autonomous systems, but protecting their integrity is crucial due to the costly nature of machine learning. Watermarking provides a solution to ensure traceability, integrity, and functionality of neural networks by allowing imperceptible da

0 views • 15 slides


Exploration of Cosmic Neutrinos with Hyper-Kamiokande and Graph Analysis

Delve into the study of cosmic neutrinos with the Hyper-Kamiokande detector at Ecole Polytechnique. Enzo Forestier, a motivated student, combines his passion for physics, space science, and meteorology with Japanese language skills. Through research projects ranging from rocket engines to satellite

0 views • 14 slides


Fruit Image Recognition Using Neural Network by Ekin Yagis & Zain Fuad

Explore the process of fruit image recognition using neural networks, including error functions, data pre-processing, neural network structures, results, and the best networks identified. The research delves into techniques like standardizing data and optimizing network architectures.

0 views • 7 slides


Understanding Deep Generative Bayesian Networks in Machine Learning

Exploring the differences between Neural Networks and Bayesian Neural Networks, the advantages of the latter including robustness and adaptation capabilities, the Bayesian theory behind these networks, and insights into the comparison with regular neural network theory. Dive into the complexities, u

0 views • 22 slides