These are the slides from the London Creative AI meetup on "Generating audio and images using deep learning" given by Kai Arulkumaran on 23rd November.
Explores the type of structure learned by Convolutional Neural Networks, the applications where they're most valuable and a number of appropriate mental models for understanding deep learning.
The presentation is coverong the convolution neural network (CNN) design.
First,
the main building blocks of CNNs will be introduced. Then we systematically
investigate the impact of a range of recent advances in CNN architectures and
learning methods on the object categorization (ILSVRC) problem. In the
evaluation, the influence of the following choices of the architecture are
tested: non-linearity (ReLU, ELU, maxout, compatibility with batch
normalization), pooling variants (stochastic, max, average, mixed), network
width, classifier design (convolution, fully-connected, SPP), image
pre-processing, and of learning parameters: learning rate, batch size,
cleanliness of the data, etc.
Explores the type of structure learned by Convolutional Neural Networks, the applications where they're most valuable and a number of appropriate mental models for understanding deep learning.
The presentation is coverong the convolution neural network (CNN) design.
First,
the main building blocks of CNNs will be introduced. Then we systematically
investigate the impact of a range of recent advances in CNN architectures and
learning methods on the object categorization (ILSVRC) problem. In the
evaluation, the influence of the following choices of the architecture are
tested: non-linearity (ReLU, ELU, maxout, compatibility with batch
normalization), pooling variants (stochastic, max, average, mixed), network
width, classifier design (convolution, fully-connected, SPP), image
pre-processing, and of learning parameters: learning rate, batch size,
cleanliness of the data, etc.
Modern Convolutional Neural Network techniques for image segmentationGioele Ciaparrone
Recently, Convolutional Neural Networks have been successfully applied to image segmentation tasks. Here we present some of the most recent techniques that increased the accuracy in such tasks. First we describe the Inception architecture and its evolution, which allowed to increase width and depth of the network without increasing the computational burden. We then show how to adapt classification networks into fully convolutional networks, able to perform pixel-wise classification for segmentation tasks. We finally introduce the hypercolumn technique to further improve state-of-the-art on various fine-grained localization tasks.
http://imatge-upc.github.io/telecombcn-2016-dlcv/
Deep learning technologies are at the core of the current revolution in artificial intelligence for multimedia data analysis. The convergence of big annotated data and affordable GPU hardware has allowed the training of neural networks for data analysis tasks which had been addressed until now with hand-crafted features. Architectures such as convolutional neural networks, recurrent neural networks and Q-nets for reinforcement learning have shaped a brand new scenario in signal processing. This course will cover the basic principles and applications of deep learning to computer vision problems, such as image classification, object detection or text captioning.
Machine Learning - Introduction to Convolutional Neural NetworksAndrew Ferlitsch
Abstract: This PDSG workshop introduces basic concepts of convolutional neural networks. Concepts covered are image pixels, image preprocessing, feature detectors, feature maps, convolution, ReLU, pooling and flattening.
Level: Fundamental
Requirements: No prior programming or statistics knowledge required. Some knowledge of neural networks is recommended.
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/
Convolutional Neural Networks : Popular Architecturesananth
In this presentation we look at some of the popular architectures, such as ResNet, that have been successfully used for a variety of applications. Starting from the AlexNet and VGG that showed that the deep learning architectures can deliver unprecedented accuracies for Image classification and localization tasks, we review other recent architectures such as ResNet, GoogleNet (Inception) and the more recent SENet that have won ImageNet competitions.
https://telecombcn-dl.github.io/2017-dlcv/
Deep learning technologies are at the core of the current revolution in artificial intelligence for multimedia data analysis. The convergence of large-scale annotated datasets and affordable GPU hardware has allowed the training of neural networks for data analysis tasks which were previously addressed with hand-crafted features. Architectures such as convolutional neural networks, recurrent neural networks and Q-nets for reinforcement learning have shaped a brand new scenario in signal processing. This course will cover the basic principles and applications of deep learning to computer vision problems, such as image classification, object detection or image captioning.
Introduction to Graph Neural Networks: Basics and Applications - Katsuhiko Is...Preferred Networks
This presentation explains basic ideas of graph neural networks (GNNs) and their common applications. Primary target audiences are students, engineers and researchers who are new to GNNs but interested in using GNNs for their projects. This is a modified version of the course material for a special lecture on Data Science at Nara Institute of Science and Technology (NAIST), given by Preferred Networks researcher Katsuhiko Ishiguro, PhD.
Deep Style: Using Variational Auto-encoders for Image GenerationTJ Torres
This talk is about some work done at Stitch Fix surrounding the use of Variational Auto-encoders to efficiently create distributed representation spaces of style and generative image models for new clothing.
Creative Responses to Artificial IntelligenceLuba Elliott
This presentation was delivered by Murray Shanahan at the Creative AI meetup #3 in London on the 18th January 2017.
Science fiction has long offered a philosophical critique of the prospect of artificial intelligence. But now that AI technologies are increasingly real rather than fictional the wider world of culture and the arts is beginning to respond. I will offer my personal perpective on this based on my experience working with the film Ex Machina, and collaborating with artist collective Random International.
Murray Shanahan is Professor of Cognitive Robotics in the Dept. of Computing at Imperial College London, where he heads the Neurodynamics Group. Educated at Imperial College and Cambridge University (King’s College), he became a full professor in 2006. His publications span artificial intelligence, robotics, logic, dynamical systems, computational neuroscience, and philosophy of mind. He was scientific advisor to the film Ex Machina, and regularly appears in the media to comment on artificial intelligence and robotics. His books include “Embodiment and the Inner Life” (2010), and “The Technological Singularity” (2015).
Modern Convolutional Neural Network techniques for image segmentationGioele Ciaparrone
Recently, Convolutional Neural Networks have been successfully applied to image segmentation tasks. Here we present some of the most recent techniques that increased the accuracy in such tasks. First we describe the Inception architecture and its evolution, which allowed to increase width and depth of the network without increasing the computational burden. We then show how to adapt classification networks into fully convolutional networks, able to perform pixel-wise classification for segmentation tasks. We finally introduce the hypercolumn technique to further improve state-of-the-art on various fine-grained localization tasks.
http://imatge-upc.github.io/telecombcn-2016-dlcv/
Deep learning technologies are at the core of the current revolution in artificial intelligence for multimedia data analysis. The convergence of big annotated data and affordable GPU hardware has allowed the training of neural networks for data analysis tasks which had been addressed until now with hand-crafted features. Architectures such as convolutional neural networks, recurrent neural networks and Q-nets for reinforcement learning have shaped a brand new scenario in signal processing. This course will cover the basic principles and applications of deep learning to computer vision problems, such as image classification, object detection or text captioning.
Machine Learning - Introduction to Convolutional Neural NetworksAndrew Ferlitsch
Abstract: This PDSG workshop introduces basic concepts of convolutional neural networks. Concepts covered are image pixels, image preprocessing, feature detectors, feature maps, convolution, ReLU, pooling and flattening.
Level: Fundamental
Requirements: No prior programming or statistics knowledge required. Some knowledge of neural networks is recommended.
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/
Convolutional Neural Networks : Popular Architecturesananth
In this presentation we look at some of the popular architectures, such as ResNet, that have been successfully used for a variety of applications. Starting from the AlexNet and VGG that showed that the deep learning architectures can deliver unprecedented accuracies for Image classification and localization tasks, we review other recent architectures such as ResNet, GoogleNet (Inception) and the more recent SENet that have won ImageNet competitions.
https://telecombcn-dl.github.io/2017-dlcv/
Deep learning technologies are at the core of the current revolution in artificial intelligence for multimedia data analysis. The convergence of large-scale annotated datasets and affordable GPU hardware has allowed the training of neural networks for data analysis tasks which were previously addressed with hand-crafted features. Architectures such as convolutional neural networks, recurrent neural networks and Q-nets for reinforcement learning have shaped a brand new scenario in signal processing. This course will cover the basic principles and applications of deep learning to computer vision problems, such as image classification, object detection or image captioning.
Introduction to Graph Neural Networks: Basics and Applications - Katsuhiko Is...Preferred Networks
This presentation explains basic ideas of graph neural networks (GNNs) and their common applications. Primary target audiences are students, engineers and researchers who are new to GNNs but interested in using GNNs for their projects. This is a modified version of the course material for a special lecture on Data Science at Nara Institute of Science and Technology (NAIST), given by Preferred Networks researcher Katsuhiko Ishiguro, PhD.
Deep Style: Using Variational Auto-encoders for Image GenerationTJ Torres
This talk is about some work done at Stitch Fix surrounding the use of Variational Auto-encoders to efficiently create distributed representation spaces of style and generative image models for new clothing.
Creative Responses to Artificial IntelligenceLuba Elliott
This presentation was delivered by Murray Shanahan at the Creative AI meetup #3 in London on the 18th January 2017.
Science fiction has long offered a philosophical critique of the prospect of artificial intelligence. But now that AI technologies are increasingly real rather than fictional the wider world of culture and the arts is beginning to respond. I will offer my personal perpective on this based on my experience working with the film Ex Machina, and collaborating with artist collective Random International.
Murray Shanahan is Professor of Cognitive Robotics in the Dept. of Computing at Imperial College London, where he heads the Neurodynamics Group. Educated at Imperial College and Cambridge University (King’s College), he became a full professor in 2006. His publications span artificial intelligence, robotics, logic, dynamical systems, computational neuroscience, and philosophy of mind. He was scientific advisor to the film Ex Machina, and regularly appears in the media to comment on artificial intelligence and robotics. His books include “Embodiment and the Inner Life” (2010), and “The Technological Singularity” (2015).
Debating with teenagers to enhance Critical Thinking SkillsMaria Laura Damelli
DEBATING WITH TEENAGERS TO ENHANCE CRITICAL THINKING SKILLS. ->This presentation aims at sharing our experience of working with debates with teenagers in an EFL classroom. It focuses on the following issues: What a debate is, why it is a good idea to implement debates and how it is implemented
This presentation is for my Seminar Course at the University of Tehran. in this presentation, I will introduce some of the newest and also exciting developments in Generative Adversarial Networks.
https://telecombcn-dl.github.io/dlai-2020/
Deep learning technologies are at the core of the current revolution in artificial intelligence for multimedia data analysis. The convergence of large-scale annotated datasets and affordable GPU hardware has allowed the training of neural networks for data analysis tasks which were previously addressed with hand-crafted features. Architectures such as convolutional neural networks, recurrent neural networks or Q-nets for reinforcement learning have shaped a brand new scenario in signal processing. This course will cover the basic principles of deep learning from both an algorithmic and computational perspectives.
The field of Artificial Intelligence (AI) has been revitalized in this decade, primarily due to the large-scale application of Deep Learning (DL) and other Machine Learning (ML) algorithms. This has been most evident in applications like computer vision, natural language processing, and game bots. However, extraordinary successes within a short period of time have also had the unintended consequence of causing a sharp difference of opinion in research and industrial communities regarding the capabilities and limitations of deep learning. A few questions you might have heard being asked (or asked yourself) include:
a. We don’t know how Deep Neural Networks make decisions, so can we trust them?
b. Can Deep Learning deal with highly non-linear continuous systems with millions of variables?
c. Can Deep Learning solve the Artificial General Intelligence problem?
The goal of this seminar is to provide a 1000-feet view of Deep Learning and hopefully answer the questions above. The seminar will touch upon the evolution, current state of the art, and peculiarities of Deep Learning, and share thoughts on using Deep Learning as a tool for developing power system solutions.
Abstract: Generative models, and in particular adversarial ones, are becoming prevalent in computer vision as they enable enhancing artistic creation, inspire designers, prove usefulness in semi-supervised learning or robotics applications.
We will see how to develop the abilities of Generative Adversarial Networks (GANs) to
deviate from training examples to generate more original images of fashion designs. As a limitation of GANs is the production of raw images of low resolution, we also present solutions to produce vectorized results, and show how the developed method may be useful for image editing.
Generating images from a text description is as challenging as it is interesting. The Adversarial network
performs in a competitive fashion where the networks are the rivalry of each other. With the introduction of
Generative Adversarial Network, lots of development is happening in the field of Computer Vision. With
generative adversarial networks as the baseline model, studied Stack GAN consisting of two-stage GANS
step-by-step in this paper that could be easily understood. This paper presents visual comparative study of
other models attempting to generate image conditioned on the text description. One sentence can be related
to many images. And to achieve this multi-modal characteristic, conditioning augmentation is also
performed. The performance of Stack-GAN is better in generating images from captions due to its unique
architecture. As it consists of two GANS instead of one, it first draws a rough sketch and then corrects the
defects yielding a high-resolution image.
Generating images from a text description is as challenging as it is interesting. The Adversarial network
performs in a competitive fashion where the networks are the rivalry of each other. With the introduction of
Generative Adversarial Network, lots of development is happening in the field of Computer Vision. With
generative adversarial networks as the baseline model, studied Stack GAN consisting of two-stage GANS
step-by-step in this paper that could be easily understood. This paper presents visual comparative study of
other models attempting to generate image conditioned on the text description. One sentence can be related
to many images. And to achieve this multi-modal characteristic, conditioning augmentation is also
performed. The performance of Stack-GAN is better in generating images from captions due to its unique
architecture. As it consists of two GANS instead of one, it first draws a rough sketch and then corrects the
defects yielding a high-resolution image.
With the explosive growth of online information, recommender system has been an effective tool to overcome information overload and promote sales. In recent years, deep learning's revolutionary advances in speech recognition, image analysis and natural language processing have gained significant attention. Meanwhile, recent studies also demonstrate its efficacy in coping with information retrieval and recommendation tasks. Applying deep learning techniques into recommender system has been gaining momentum due to its state-of-the-art performance. In this talk, I will present recent development of deep learning based recommender models and highlight some future challenges and open issues of this research field.
This presentation focuses on Deep Learning (DL) concepts, such as neural networks, backprop, activation functions, and Convolutional Neural Networks. You'll also learn how to incorporate Deep Learning in Android applications. Basic knowledge of matrices is helpful for this session, which is targeted primarily to beginners.
This is a deep learning presentation based on Deep Neural Network. It reviews the deep learning concept, related works and specific application areas.It describes a use case scenario of deep learning and highlights the current trends and research issues of deep learning
Luba Elliott - AI art - ICCV ConferenceLuba Elliott
This talk was given as part of the ICCV Workshop on Computer Vision for Fashion, Art and Design on the 2nd November in Seoul. See the workshop computer vision art gallery at computervisionart.com.
AI Art Gallery Overview - Luba Elliott - NeurIPS Creativity WorkshopLuba Elliott
This talk on 'AI Art Gallery Overview' was given by Luba Elliott at the NeurIPS Creativity Workshop on the 8th December in Montreal, Canada. The AI art gallery can be found at www.aiartonline.com.
Creativity is Intelligence - Kenneth Stanley - NeurIPS Creativity WorkshopLuba Elliott
This invited talk on 'Creativity is Intelligence' was given by Kenneth Stanley at the 2018 NeurIPS Workshop on Machine Learning for Creativity and Design in Montreal, Canada on the 8th December.
Seen by machine: Computational Spectatorship in the BBC ArchiveLuba Elliott
This talk on 'Seen by machine: Computational Spectatorship in the BBC Archive' was given by Daniel Chávez Heras as part of the Creative AI meetup on the 15th November at the Goethe Institute in London.
Natasha Jaques - Learning via Social Awareness - Creative AI meetupLuba Elliott
This talk by Natasha Jaques from MIT Media Lab on "Learning via Social Awareness: Improving a deep generative sketching model with facial feedback" was presented on 10th September 2018 at IDEA London as part of the Creative AI meetup.
Sander Dieleman - Generating music in the raw audio domain - Creative AI meetupLuba Elliott
This talk by Sander Dieleman from DeepMind on "Generating music in the raw audio domain" was presented on 10th September 2018 at IDEA London as part of the Creative AI meetup.
Marco Marchesi - Practical uses of style transfer in the creative industryLuba Elliott
This talk by Marco Marchesi from Happy Finish on "Can you make this image more neoclassical? Practical uses of Style Transfer in the creative industry" was presented at the Style Transfer event on 18th April at TechHub as part of the Creative AI meetup.
Hooman Shayani - CAD/CAM in the Age of AI: Designers’ Journey from Earth to SkyLuba Elliott
This talk by Hooman Shayani from Autodesk on "CAD/CAM in the Age of AI: Designers’ Journey from Earth to Sky" was presented at the Design and Manufacturing in the Age of AI event on 24th October at UCL as part of the Creative AI meetup.
Lucas Theis - Compressing Images with Neural Networks - Creative AI meetupLuba Elliott
This talk by Lucas Theis from Twitter/Magic Pony on "Compressing Images with Neural Networks" was presented at the Learning Image Representations event on 30th August at Twitter as part of the Creative AI meetup.
Emily Denton - Unsupervised Learning of Disentangled Representations from Vid...Luba Elliott
This talk by Emily Denton from New York University on "Unsupervised Learning of Disentangled Representations from Video" was presented at the Learning Image Representations event on 30th August at Twitter as part of the Creative AI meetup.
Georgia Ward Dyer - O Time thy pyramids - Creative AI meetupLuba Elliott
This talk by Georgia Ward Dyer from Royal College of Art on "O Time thy pyramids" was presented at the Calligraphic Traces event on 31st July at Thoughtworks as part of the Creative AI meetup. The upload consists of slides followed by Georgia's notes from the talk.
Daniel Berio - Graffiti synthesis, a motion centric approach - Creative AI me...Luba Elliott
This talk by Daniel Berio from Goldsmiths University on "Graffiti synthesis, a motion centric approach" was presented at the Calligraphic Traces event on 31st July at Thoughtworks as part of the Creative AI meetup.
Ali Eslami - Artificial Intelligence and Computer Aided Design - Creative AI ...Luba Elliott
This talk by Ali Eslami on "Artificial Intelligence and Computer Aided Design" was presented at the AI & Architecture event on the 21st June held at the Digital Catapult. It was part of the Creative AI meetup series and the London Festival of Architecture.
Daghan Cam - Adaptive Autonomous Manufacturing with AI - Creative AI meetupLuba Elliott
This talk by Daghan Cam from AI Build on "Adaptive Autonomous Manufacturing with AI" was presented at the AI & Architecture event on the 21st June held at the Digital Catapult. It was part of the Creative AI meetup series and the London Festival of Architecture.
Why You Should Replace Windows 11 with Nitrux Linux 3.5.0 for enhanced perfor...SOFTTECHHUB
The choice of an operating system plays a pivotal role in shaping our computing experience. For decades, Microsoft's Windows has dominated the market, offering a familiar and widely adopted platform for personal and professional use. However, as technological advancements continue to push the boundaries of innovation, alternative operating systems have emerged, challenging the status quo and offering users a fresh perspective on computing.
One such alternative that has garnered significant attention and acclaim is Nitrux Linux 3.5.0, a sleek, powerful, and user-friendly Linux distribution that promises to redefine the way we interact with our devices. With its focus on performance, security, and customization, Nitrux Linux presents a compelling case for those seeking to break free from the constraints of proprietary software and embrace the freedom and flexibility of open-source computing.
State of ICS and IoT Cyber Threat Landscape Report 2024 previewPrayukth K V
The IoT and OT threat landscape report has been prepared by the Threat Research Team at Sectrio using data from Sectrio, cyber threat intelligence farming facilities spread across over 85 cities around the world. In addition, Sectrio also runs AI-based advanced threat and payload engagement facilities that serve as sinks to attract and engage sophisticated threat actors, and newer malware including new variants and latent threats that are at an earlier stage of development.
The latest edition of the OT/ICS and IoT security Threat Landscape Report 2024 also covers:
State of global ICS asset and network exposure
Sectoral targets and attacks as well as the cost of ransom
Global APT activity, AI usage, actor and tactic profiles, and implications
Rise in volumes of AI-powered cyberattacks
Major cyber events in 2024
Malware and malicious payload trends
Cyberattack types and targets
Vulnerability exploit attempts on CVEs
Attacks on counties – USA
Expansion of bot farms – how, where, and why
In-depth analysis of the cyber threat landscape across North America, South America, Europe, APAC, and the Middle East
Why are attacks on smart factories rising?
Cyber risk predictions
Axis of attacks – Europe
Systemic attacks in the Middle East
Download the full report from here:
https://sectrio.com/resources/ot-threat-landscape-reports/sectrio-releases-ot-ics-and-iot-security-threat-landscape-report-2024/
GraphRAG is All You need? LLM & Knowledge GraphGuy Korland
Guy Korland, CEO and Co-founder of FalkorDB, will review two articles on the integration of language models with knowledge graphs.
1. Unifying Large Language Models and Knowledge Graphs: A Roadmap.
https://arxiv.org/abs/2306.08302
2. Microsoft Research's GraphRAG paper and a review paper on various uses of knowledge graphs:
https://www.microsoft.com/en-us/research/blog/graphrag-unlocking-llm-discovery-on-narrative-private-data/
Generative AI Deep Dive: Advancing from Proof of Concept to ProductionAggregage
Join Maher Hanafi, VP of Engineering at Betterworks, in this new session where he'll share a practical framework to transform Gen AI prototypes into impactful products! He'll delve into the complexities of data collection and management, model selection and optimization, and ensuring security, scalability, and responsible use.
Removing Uninteresting Bytes in Software FuzzingAftab Hussain
Imagine a world where software fuzzing, the process of mutating bytes in test seeds to uncover hidden and erroneous program behaviors, becomes faster and more effective. A lot depends on the initial seeds, which can significantly dictate the trajectory of a fuzzing campaign, particularly in terms of how long it takes to uncover interesting behaviour in your code. We introduce DIAR, a technique designed to speedup fuzzing campaigns by pinpointing and eliminating those uninteresting bytes in the seeds. Picture this: instead of wasting valuable resources on meaningless mutations in large, bloated seeds, DIAR removes the unnecessary bytes, streamlining the entire process.
In this work, we equipped AFL, a popular fuzzer, with DIAR and examined two critical Linux libraries -- Libxml's xmllint, a tool for parsing xml documents, and Binutil's readelf, an essential debugging and security analysis command-line tool used to display detailed information about ELF (Executable and Linkable Format). Our preliminary results show that AFL+DIAR does not only discover new paths more quickly but also achieves higher coverage overall. This work thus showcases how starting with lean and optimized seeds can lead to faster, more comprehensive fuzzing campaigns -- and DIAR helps you find such seeds.
- These are slides of the talk given at IEEE International Conference on Software Testing Verification and Validation Workshop, ICSTW 2022.
Pushing the limits of ePRTC: 100ns holdover for 100 daysAdtran
At WSTS 2024, Alon Stern explored the topic of parametric holdover and explained how recent research findings can be implemented in real-world PNT networks to achieve 100 nanoseconds of accuracy for up to 100 days.
Accelerate your Kubernetes clusters with Varnish CachingThijs Feryn
A presentation about the usage and availability of Varnish on Kubernetes. This talk explores the capabilities of Varnish caching and shows how to use the Varnish Helm chart to deploy it to Kubernetes.
This presentation was delivered at K8SUG Singapore. See https://feryn.eu/presentations/accelerate-your-kubernetes-clusters-with-varnish-caching-k8sug-singapore-28-2024 for more details.
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.
Dev Dives: Train smarter, not harder – active learning and UiPath LLMs for do...UiPathCommunity
💥 Speed, accuracy, and scaling – discover the superpowers of GenAI in action with UiPath Document Understanding and Communications Mining™:
See how to accelerate model training and optimize model performance with active learning
Learn about the latest enhancements to out-of-the-box document processing – with little to no training required
Get an exclusive demo of the new family of UiPath LLMs – GenAI models specialized for processing different types of documents and messages
This is a hands-on session specifically designed for automation developers and AI enthusiasts seeking to enhance their knowledge in leveraging the latest intelligent document processing capabilities offered by UiPath.
Speakers:
👨🏫 Andras Palfi, Senior Product Manager, UiPath
👩🏫 Lenka Dulovicova, Product Program Manager, UiPath
2. Foreword
Deep learning is a great creative tool
We can generate novel media in unexpected ways
(e.g. DeepDream/Inceptionism [1])
We can remix media (e.g. style transfer [2])
We can directly use deep generative models
The following applies to more than just images
4. Generation
Let's create an image using a starting value
Speci cally, some random noise,
maybe sampled from a Gaussian: z ∼ (0, 1)
Create a transformation model that takes
and returns an image
f z
x
Images from space are generated from a value ∼ P(Z)
5. Networks as Functions
Arti cial neural networks are powerful function approximators
Approximate (many) continuous functions in
(universal approximation theorem) [9]
ℝ
n
Learn network parameters, , to satisfy a criterionθ
7. Discriminator Function
Train a discriminator function, , to label images:D(x; ϕ) y = D(x)
Learn to distinguish real images: when
Learn to distinguish fake images: when
(y = 1) x ∼ p(X)
(y = 0) x = G(z)
Adjust to maximise both criterionsϕ
8. Minimax Game
Train using the minimax rule from game theory [3]G
[log(D(x))] + [1 − log(D(G(z)))]minθ maxϕ x∼p(X) z∼p(Z)
never sees real images, but learns to create images
that would fool
G
D
GANs turn density estimation into an easier problem - classi cation
14. Inference
Impose more meaning on latent space
Observation is generated by a latent variablex z
Inference tries to retrieve which was responsible for whichz x
Probabilistically, generation is
and inference is
x ∼ P(x|z)
z ∼ P(z|x)
Autoencoders learn both together
for "true" distributions, for model distributionsP Q
15. Autoencoders
Neural network encoder, , with encodinge z = e(x)
Decoder, , with decodingd x = d(z)
learns , learnse Q(z|x; θ) d Q(x|z; θ)
Compose networks, , and train jointlyd ∘ e
Criterion is minimising distance between real input
and reconstruction
x
d(e(x))
Mean square error/cross entropy criterions correspond to
maximising likelihood of reconstruction
16. Generative Autoencoders
Constrain encodings to follow a prior probability distribution, P(Z)
Idea 1: Directly sample from stochastic neurons
Optimisation requires estimating gradient over expectation,
naively requiring (Monte Carlo) sampling
Idea 2: Reparameterise to a deterministic function + noise source [4]
Encoder outputs parameters for a probability distribution
Criterion penalises di erence between
desired distribution parameters and encoder outputs
Stochastic samples via the reparameterisation trick
23. Independence Assumption
So far, pixels were created independently of each other,
given the penultimate layer
Autoregressive networks generate pixels one at a time,
conditional on the previous [6-8]
24. Conclusion
Deep generative models have improved a lot in a few years
Images are intuitively interpretable for qualitative evaluation
Generative models are hard to evaluate quantitatively [21]
Potential uses, e.g. procedural content generation
For more depth, see Building Machines that Imagine and Reason
25. Figures
1.
2.
3.
4.
5.
6.
7.
8.
Google Research Blog: Inceptionism: Going Deeper into Neural Networks
Neural Networks, Manifolds, and Topology -- colah's blog
Newmu/dcgan_code - GitHub
Pattern Recognition and Machine Learning | Christopher Bishop | Springer
[1602.03220] Discriminative Regularization for Generative Models
[1610.09296] Improving Sampling from Generative Autoencoders with Markov Chains
DRAW: A Recurrent Neural Network For Image Generation by Google DeepMind - YouTube
[1511.02793] Generating Images from Captions with Attention
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27. Thanks
Friends on Twitter for posts and discussions
Toni Creswell, equal contributor on [16]
Colleagues at BICV and Computational Neurodynamics