This presentation is Part 2 of my September Lisp NYC presentation on Reinforcement Learning and Artificial Neural Nets. We will continue from where we left off by covering Convolutional Neural Nets (CNN) and Recurrent Neural Nets (RNN) in depth.
Time permitting I also plan on having a few slides on each of the following topics:
1. Generative Adversarial Networks (GANs)
2. Differentiable Neural Computers (DNCs)
3. Deep Reinforcement Learning (DRL)
Some code examples will be provided in Clojure.
After a very brief recap of Part 1 (ANN & RL), we will jump right into CNN and their appropriateness for image recognition. We will start by covering the convolution operator. We will then explain feature maps and pooling operations and then explain the LeNet 5 architecture. The MNIST data will be used to illustrate a fully functioning CNN.
Next we cover Recurrent Neural Nets in depth and describe how they have been used in Natural Language Processing. We will explain why gated networks and LSTM are used in practice.
Please note that some exposure or familiarity with Gradient Descent and Backpropagation will be assumed. These are covered in the first part of the talk for which both video and slides are available online.
A lot of material will be drawn from the new Deep Learning book by Goodfellow & Bengio as well as Michael Nielsen's online book on Neural Networks and Deep Learning as well several other online resources.
Bio
Pierre de Lacaze has over 20 years industry experience with AI and Lisp based technologies. He holds a Bachelor of Science in Applied Mathematics and a Master’s Degree in Computer Science.
https://www.linkedin.com/in/pierre-de-lacaze-b11026b/
This presentation is Part 2 of my September Lisp NYC presentation on Reinforcement Learning and Artificial Neural Nets. We will continue from where we left off by covering Convolutional Neural Nets (CNN) and Recurrent Neural Nets (RNN) in depth.
Time permitting I also plan on having a few slides on each of the following topics:
1. Generative Adversarial Networks (GANs)
2. Differentiable Neural Computers (DNCs)
3. Deep Reinforcement Learning (DRL)
Some code examples will be provided in Clojure.
After a very brief recap of Part 1 (ANN & RL), we will jump right into CNN and their appropriateness for image recognition. We will start by covering the convolution operator. We will then explain feature maps and pooling operations and then explain the LeNet 5 architecture. The MNIST data will be used to illustrate a fully functioning CNN.
Next we cover Recurrent Neural Nets in depth and describe how they have been used in Natural Language Processing. We will explain why gated networks and LSTM are used in practice.
Please note that some exposure or familiarity with Gradient Descent and Backpropagation will be assumed. These are covered in the first part of the talk for which both video and slides are available online.
A lot of material will be drawn from the new Deep Learning book by Goodfellow & Bengio as well as Michael Nielsen's online book on Neural Networks and Deep Learning as well several other online resources.
Bio
Pierre de Lacaze has over 20 years industry experience with AI and Lisp based technologies. He holds a Bachelor of Science in Applied Mathematics and a Master’s Degree in Computer Science.
https://www.linkedin.com/in/pierre-de-lacaze-b11026b/
NS-CUK Journal club: H.E.Lee, Review on " A biomedical knowledge graph-based ...ssuser4b1f48
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SAP Sapphire 2024 - ASUG301 building better apps with SAP Fiori.pdfPeter Spielvogel
Building better applications for business users with SAP Fiori.
• What is SAP Fiori and why it matters to you
• How a better user experience drives measurable business benefits
• How to get started with SAP Fiori today
• How SAP Fiori elements accelerates application development
• How SAP Build Code includes SAP Fiori tools and other generative artificial intelligence capabilities
• How SAP Fiori paves the way for using AI in SAP apps
Observability Concepts EVERY Developer Should Know -- DeveloperWeek Europe.pdfPaige Cruz
Monitoring and observability aren’t traditionally found in software curriculums and many of us cobble this knowledge together from whatever vendor or ecosystem we were first introduced to and whatever is a part of your current company’s observability stack.
While the dev and ops silo continues to crumble….many organizations still relegate monitoring & observability as the purview of ops, infra and SRE teams. This is a mistake - achieving a highly observable system requires collaboration up and down the stack.
I, a former op, would like to extend an invitation to all application developers to join the observability party will share these foundational concepts to build on:
Epistemic Interaction - tuning interfaces to provide information for AI supportAlan Dix
Paper presented at SYNERGY workshop at AVI 2024, Genoa, Italy. 3rd June 2024
https://alandix.com/academic/papers/synergy2024-epistemic/
As machine learning integrates deeper into human-computer interactions, the concept of epistemic interaction emerges, aiming to refine these interactions to enhance system adaptability. This approach encourages minor, intentional adjustments in user behaviour to enrich the data available for system learning. This paper introduces epistemic interaction within the context of human-system communication, illustrating how deliberate interaction design can improve system understanding and adaptation. Through concrete examples, we demonstrate the potential of epistemic interaction to significantly advance human-computer interaction by leveraging intuitive human communication strategies to inform system design and functionality, offering a novel pathway for enriching user-system engagements.
The Art of the Pitch: WordPress Relationships and SalesLaura Byrne
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All these questions and more will be explored as we talk about matching clients’ needs with what your agency offers without pulling teeth or pulling your hair out. Practical tips, and strategies for successful relationship building that leads to closing the deal.
Builder.ai Founder Sachin Dev Duggal's Strategic Approach to Create an Innova...Ramesh Iyer
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Elevating Tactical DDD Patterns Through Object CalisthenicsDorra BARTAGUIZ
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Essentials of Automations: Optimizing FME Workflows with ParametersSafe Software
Are you looking to streamline your workflows and boost your projects’ efficiency? Do you find yourself searching for ways to add flexibility and control over your FME workflows? If so, you’re in the right place.
Join us for an insightful dive into the world of FME parameters, a critical element in optimizing workflow efficiency. This webinar marks the beginning of our three-part “Essentials of Automation” series. This first webinar is designed to equip you with the knowledge and skills to utilize parameters effectively: enhancing the flexibility, maintainability, and user control of your FME projects.
Here’s what you’ll gain:
- Essentials of FME Parameters: Understand the pivotal role of parameters, including Reader/Writer, Transformer, User, and FME Flow categories. Discover how they are the key to unlocking automation and optimization within your workflows.
- Practical Applications in FME Form: Delve into key user parameter types including choice, connections, and file URLs. Allow users to control how a workflow runs, making your workflows more reusable. Learn to import values and deliver the best user experience for your workflows while enhancing accuracy.
- Optimization Strategies in FME Flow: Explore the creation and strategic deployment of parameters in FME Flow, including the use of deployment and geometry parameters, to maximize workflow efficiency.
- Pro Tips for Success: Gain insights on parameterizing connections and leveraging new features like Conditional Visibility for clarity and simplicity.
We’ll wrap up with a glimpse into future webinars, followed by a Q&A session to address your specific questions surrounding this topic.
Don’t miss this opportunity to elevate your FME expertise and drive your projects to new heights of efficiency.
Encryption in Microsoft 365 - ExpertsLive Netherlands 2024Albert Hoitingh
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GDG Cloud Southlake #33: Boule & Rebala: Effective AppSec in SDLC using Deplo...James Anderson
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The software team must secure its software delivery process to avoid vulnerability and security breaches. This needs to be achieved with existing tool chains and without extensive rework of the delivery processes. This talk will present strategies and techniques for providing visibility into the true risk of the existing vulnerabilities, preventing the introduction of security issues in the software, resolving vulnerabilities in production environments quickly, and capturing the deployment bill of materials (DBOM).
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Bob Boule
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Gopinath Rebala
Gopinath Rebala is the CTO of OpsMx, where he has overall responsibility for the machine learning and data processing architectures for Secure Software Delivery. Gopi also has a strong connection with our customers, leading design and architecture for strategic implementations. Gopi is a frequent speaker and well-known leader in continuous delivery and integrating security into software delivery.
Generative AI Deep Dive: Advancing from Proof of Concept to ProductionAggregage
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3. 2
1. Introduction
Background
• Graph structures are used in a variety of domains and applications
• Thus, it is important to develop machine learning techniques
that can utilize the information embedded in the graph structure and feature information of nodes and edges
• Recently, methods using graph kernel-based and graph neural network algorithms were proposed
4. 3
1. Introduction
Background
• The kernel approach uses a fixed set of predefined features
• Weisfeiler-Leman subtree kernel
: A method based on a
1-WL graph isomorphism heuristic
• Methods to effectively summarize graph structures
• However,
it does not adapt to the given data distribution
and cannot analyze data with continuous node and edge labels in the domain
5. 4
1. Introduction
Background
• Graph neural network methods solve the limitations of graph kernel methods
using a machine learning framework
• Aggregate using a neural network with node neighbors using feature vectors
• A method that neuralizes the 1-WL algorithm
• GNN framework has the characteristics of message passing in terms of aggregating and forwarding local
neighborhood information.
• So it can be trained in an end-to-end method, allowing for adaptability and better generalization
6. 5
1. Introduction
Present Work
• Theoretical study of the relationship between GNNs and kernels
• GNNs cannot be more powerful than 1-WL in distinguishing between nonisomorphic graphs
→ but can have the same level of representation power with proper parameter initialization
7. 6
1. Introduction
Propose
• Propose K-GNN, a generalized version of GNN based on theoretical relationships
• Based on the K- WL algorithm for neural architectures
• Performs direct message passing between subgraph structures (rather than nodes individually)
8. 7
1. Introduction
Summary
• Show that GNNs cannot be more powerful than 1-WL in distinguishing between isomorphic (sub)graphs
(which was not clear in theory)
, and that GNNs have the same ability as 1-WL, assuming proper parameter initialization
• Propose k-GNNs and "1-k-GNNs", a hierarchical version of k-GNNs
, a model that can capture the fine and continuous structure of graphs and the relationship
• Experimentally prove
that higher-order graph characterization is important for graph classification and regression tasks
9. 8
2. Related work
Kernel Methods
• Mapping a Graph to Hilbert Space
• One of the most common methods used in supervised learning
• Important early research: random walk-based kernels, shortest path-based kernels
• Recent research focuses on scalability and avoiding gram matrix computation
10. 9
2. Related work
Kernel Methods
• Graphlet Counting Based Kernels
• Utilizing small subgraphs to represent specific structures in graph data
• Effectively capture local features
11. 10
2. Related work
Kernel Methods
• Higher-order variants kernels
• Learning methods using larger, more complex subgraphs to account for
high-order and non-regular patterns
• Recent work has focused on allocation-based, spectral, and graph decomposition approaches
12. 11
2. Related work
GNN Methods
• Methods for counterfactuals using neural network frameworks with vector representations
• Neural Fingerprints
• Creating a graph representing interatomic bonds to learn with MLP
• Gated Graph Neural Networks, GraphSAGE
13. 12
2. Related work
GNN Methods
• SplineCNN
• Use curves and higher-order polynomials to process and extract information from graph data
• Effective with unstructured data
Editor's Notes
- But s, GNNs have focused on empirical evaluations and analyses, and the theoretical benefits are not clear.
Can capture more structural information at the node level
Can capture more structural information at the node level