For the full video of this presentation, please visit: https://www.edge-ai-vision.com/2021/10/dnn-training-data-how-to-know-what-you-need-and-how-to-get-it-a-presentation-from-tech-mahindra/
Abhishek Sharma, Practice Head for Engineering AI at Tech Mahindra, presents the “DNN Training Data: How to Know What You Need and How to Get It” tutorial at the May 2021 Embedded Vision Summit.
Successful training of deep neural networks requires the right amounts and types of annotated training data. Collecting, curating and labeling this data is typically one of the most time-consuming aspects of developing a deep-learning-based solution.
In this talk, Sharma discusses approaches useful for situations where insufficient data is available, including transfer learning and data augmentation, including the use of generative adversarial networks (GANs). He also discusses techniques that can be helpful when data is plentiful, such as transforms, data path optimization and approximate computing. He illustrates these techniques and challenges via case studies from the healthcare and manufacturing industries.
PR-297: Training data-efficient image transformers & distillation through att...Jinwon Lee
안녕하세요 TensorFlow Korea 논문 읽기 모임 PR-12의 297번째 리뷰입니다
어느덧 PR-12 시즌 3의 끝까지 논문 3편밖에 남지 않았네요.
시즌 3가 끝나면 바로 시즌 4의 새 멤버 모집이 시작될 예정입니다. 많은 관심과 지원 부탁드립니다~~
(멤버 모집 공지는 Facebook TensorFlow Korea 그룹에 올라올 예정입니다)
오늘 제가 리뷰한 논문은 Facebook의 Training data-efficient image transformers & distillation through attention 입니다.
Google에서 나왔던 ViT논문 이후에 convolution을 전혀 사용하지 않고 오직 attention만을 이용한 computer vision algorithm에 어느때보다 관심이 높아지고 있는데요
이 논문에서 제안한 DeiT 모델은 ViT와 같은 architecture를 사용하면서 ViT가 ImageNet data만으로는 성능이 잘 안나왔던 것에 비해서
Training 방법 개선과 새로운 Knowledge Distillation 방법을 사용하여 mageNet data 만으로 EfficientNet보다 뛰어난 성능을 보여주는 결과를 얻었습니다.
정말 CNN은 이제 서서히 사라지게 되는 것일까요? Attention이 computer vision도 정복하게 될 것인지....
개인적으로는 당분간은 attention 기반의 CV 논문이 쏟아질 거라고 확신하고, 또 여기에서 놀라운 일들이 일어날 수 있을 거라고 생각하고 있습니다
CNN은 10년간 많은 연구를 통해서 발전해왔지만, transformer는 이제 CV에 적용된 지 얼마 안된 시점이라서 더 기대가 크구요,
attention이 inductive bias가 가장 적은 형태의 모델이기 때문에 더 놀라운 이들을 만들 수 있을거라고 생각합니다
얼마 전에 나온 open AI의 DALL-E도 그 대표적인 예라고 할 수 있을 것 같습니다. Transformer의 또하나의 transformation이 궁금하신 분들은 아래 영상을 참고해주세요
영상링크: https://youtu.be/DjEvzeiWBTo
논문링크: https://arxiv.org/abs/2012.12877
For the full video of this presentation, please visit: https://www.edge-ai-vision.com/2021/10/dnn-training-data-how-to-know-what-you-need-and-how-to-get-it-a-presentation-from-tech-mahindra/
Abhishek Sharma, Practice Head for Engineering AI at Tech Mahindra, presents the “DNN Training Data: How to Know What You Need and How to Get It” tutorial at the May 2021 Embedded Vision Summit.
Successful training of deep neural networks requires the right amounts and types of annotated training data. Collecting, curating and labeling this data is typically one of the most time-consuming aspects of developing a deep-learning-based solution.
In this talk, Sharma discusses approaches useful for situations where insufficient data is available, including transfer learning and data augmentation, including the use of generative adversarial networks (GANs). He also discusses techniques that can be helpful when data is plentiful, such as transforms, data path optimization and approximate computing. He illustrates these techniques and challenges via case studies from the healthcare and manufacturing industries.
PR-297: Training data-efficient image transformers & distillation through att...Jinwon Lee
안녕하세요 TensorFlow Korea 논문 읽기 모임 PR-12의 297번째 리뷰입니다
어느덧 PR-12 시즌 3의 끝까지 논문 3편밖에 남지 않았네요.
시즌 3가 끝나면 바로 시즌 4의 새 멤버 모집이 시작될 예정입니다. 많은 관심과 지원 부탁드립니다~~
(멤버 모집 공지는 Facebook TensorFlow Korea 그룹에 올라올 예정입니다)
오늘 제가 리뷰한 논문은 Facebook의 Training data-efficient image transformers & distillation through attention 입니다.
Google에서 나왔던 ViT논문 이후에 convolution을 전혀 사용하지 않고 오직 attention만을 이용한 computer vision algorithm에 어느때보다 관심이 높아지고 있는데요
이 논문에서 제안한 DeiT 모델은 ViT와 같은 architecture를 사용하면서 ViT가 ImageNet data만으로는 성능이 잘 안나왔던 것에 비해서
Training 방법 개선과 새로운 Knowledge Distillation 방법을 사용하여 mageNet data 만으로 EfficientNet보다 뛰어난 성능을 보여주는 결과를 얻었습니다.
정말 CNN은 이제 서서히 사라지게 되는 것일까요? Attention이 computer vision도 정복하게 될 것인지....
개인적으로는 당분간은 attention 기반의 CV 논문이 쏟아질 거라고 확신하고, 또 여기에서 놀라운 일들이 일어날 수 있을 거라고 생각하고 있습니다
CNN은 10년간 많은 연구를 통해서 발전해왔지만, transformer는 이제 CV에 적용된 지 얼마 안된 시점이라서 더 기대가 크구요,
attention이 inductive bias가 가장 적은 형태의 모델이기 때문에 더 놀라운 이들을 만들 수 있을거라고 생각합니다
얼마 전에 나온 open AI의 DALL-E도 그 대표적인 예라고 할 수 있을 것 같습니다. Transformer의 또하나의 transformation이 궁금하신 분들은 아래 영상을 참고해주세요
영상링크: https://youtu.be/DjEvzeiWBTo
논문링크: https://arxiv.org/abs/2012.12877
For the full video of this presentation, please visit: https://www.edge-ai-vision.com/2021/08/high-fidelity-conversion-of-floating-point-networks-for-low-precision-inference-using-distillation-with-limited-data-a-presentation-from-imagination-technologies/
James Imber, Senior Research Engineer at Imagination Technologies, presents the “High-fidelity Conversion of Floating-point Networks for Low-precision Inference using Distillation with Limited Data” tutorial at the May 2021 Embedded Vision Summit.
When converting floating-point networks to low-precision equivalents for high-performance inference, the primary objective is to maximally compress the network whilst maintaining fidelity to the original, floating-point network. This is made particularly challenging when only a reduced or unlabelled dataset is available. Data may be limited for reasons of a commercial or legal nature: for example, companies may be unwilling to share valuable data and labels that represent a substantial investment of resources; or the collector of the original dataset may not be permitted to share it for data privacy reasons.
Imber presents a method based on distillation that allows high-fidelity, low-precision networks to be produced for a wide range of different network types, using the original trained network in place of a labeled dataset. The proposed approach is directly applicable across multiple domains (e.g. classification, segmentation and style transfer) and can be adapted to numerous network compression techniques.
Super resolution in deep learning era - Jaejun YooJaeJun Yoo
Abstract (Eng/Kor):
Image restoration (IR) is one of the fundamental problems, which includes denoising, deblurring, super-resolution, etc. Among those, in today's talk, I will more focus on the super-resolution task. There are two main streams in the super-resolution studies; a traditional model-based optimization and a discriminative learning method. I will present the pros and cons of both methods and their recent developments in the research field. Finally, I will provide a mathematical view that explains both methods in a single holistic framework, while achieving the best of both worlds. The last slide summarizes the remaining problems that are yet to be solved in the field.
영상 복원(Image restoration, IR)은 low-level vision에서 매우 중요하게 다루는 근본적인 문제 중 하나로서 denoising, deblurring, super-resolution 등의 다양한 영상 처리 문제를 포괄합니다. 오늘 발표에서는 영상 복원 분야 중에서도 super-resolution 문제에 대해 집중적으로 다루겠습니다. 전통적인 model-based optimization 방식과 deep learning을 적용하여 문제를 푸는 방식에 대해, 각각의 장단점과 최신 연구 발전 흐름을 소개하겠습니다. 마지막으로는 이 둘을 하나로 잇는 통일된 관점을 제시하고 관련 연구들 살펴본 후, super-resolution 분야에서 아직 남아있는 문제점들을 정리하겠습니다.
For the full video of this presentation, please visit: https://www.edge-ai-vision.com/2021/10/modern-machine-learning-from-basics-to-advanced-deep-learning-a-presentation-from-deep-netts/
Zoran Sevarac, Associate Professor at the University of Belgrade and Co-founder and CEO of Deep Netts, presents the “Modern Machine Vision from Basics to Advanced Deep Learning” tutorial at the May 2021 Embedded Vision Summit.
In this talk, Sevarac introduces the fundamentals of deep learning for image understanding. He begins by explaining the basics of convolutional neural networks (CNNs), and showing how CNNs are used to perform image classification and object detection. He provides an overview of the recent evolution of CNN topologies for object detection. He also illustrates typical use cases for CNN-based image classification and object detection, and provides a roadmap for getting started with deep learning for image understanding.
Enhance Example-Based Super Resolution to Achieve Fine Magnification of Low ...IJMER
Images with high resolution (HR) often required in most electronic imaging applications.
There are two types of resolution first is high resolution and other one is low resolution. Now high
resolution means pixel density with in an image is high and low resolution means pixel density with in
an image is low. Therefore high resolution image can offer more detail compare to low resolution
image that may be critical in many application. Super resolution is the process to obtain high
resolution image from one or more low resolution images. Here in paper explain such robust methods
of image super resolution. This paper describes the learning-based SR technique that utilizes an
example-based algorithm. This technique divides a large volume of training images into small
rectangular pieces called patches and patch pairs of low-resolution and high-resolution images are
stored in dictionary. After that there are low resolution patch is extracted from the input images. The
most alike patch pair is searched in the dictionary to synthesize high resolution image using the
searched high resolution patch in the pair.
NIT Silchar ML Hackathon 2019 Session on Computer Vision with Deep Learning.
Targeted Audience: Pre-requisite: Basic knowledge on Machine Learning and Deep Learning
To get this project in ONLINE or through TRAINING Sessions,
Contact:JP INFOTECH, Old No.31, New No.86, 1st Floor, 1st Avenue, Ashok Pillar, Chennai -83. Landmark: Next to Kotak Mahendra Bank. Pondicherry Office: JP INFOTECH, #45, Kamaraj Salai, Thattanchavady, Puducherry -9. Landmark: Next to VVP Nagar Arch. Mobile: (0) 9952649690 , Email: jpinfotechprojects@gmail.com, web: www.jpinfotech.org Blog: www.jpinfotech.blogspot.com
SINGLE IMAGE SUPER RESOLUTION: A COMPARATIVE STUDYcsandit
The majority of applications requiring high resolution images to derive and analyze data
accurately and easily. Image super resolution is playing an effective role in those applications.
Image super resolution is the process of producing high resolution image from low resolution
image. In this paper, we study various image super resolution techniques with respect to the
quality of results and processing time. This comparative study introduces a comparison between
four algorithms of single image super-resolution. For fair comparison, the compared algorithms
are tested on the same dataset and same platform to show the major advantages of one over the
others.
Face Recognition: From Scratch To Hatch / Эдуард Тянтов (Mail.ru Group)Ontico
HighLoad++ 2017
Зал «Найроби+Касабланка», 7 ноября, 15:00
Тезисы:
http://www.highload.ru/2017/abstracts/3044.html
Мы разработали технологию по детекту и распознаванию лиц для продуктов компании Mail.ru, которая показывает высокие результаты на известных тестах. Технология на данный момент используется в Мобильном Облаке@Mail.ru для кластеризации фотографий по людям, а также во внутренних сервисах компании.
...
[SOCRS2013]Differential Context Modeling in Collaborative FilteringYONG ZHENG
Abstract: Context-aware recommender systems (CARS) try to adapt their recommendations to users’ specific contextual situations. In many recommender systems, particularly those based on collaborative filtering (CF), the additional contextual constraints may lead to increased sparsity in the user preference data, thus fewer matches between the current user context and previous situations. Our earlier work proposed two approaches to deal with this problem – differential context relaxation (DCR) and differential context weighting (DCW) and we have successfully examined them using user-based collaborative filtering (UBCF). In this paper, we put DCR and DCW into one framework called differential context modeling (DCM). As a general framework, DCM is able to be applied to other recommendation algorithms other than UBCF. We expand the application of DCM to the other two CF approaches: item-based CF and slope one recommender. Predictive performances are evaluated based on two real-world data sets and experimental results demonstrate that applying DCM to those two algorithms is able to improve predictive accuracy compared with our baselines: context-free CF algorithms and contextual pre-filtering algorithms.
For the full video of this presentation, please visit: https://www.edge-ai-vision.com/2021/08/high-fidelity-conversion-of-floating-point-networks-for-low-precision-inference-using-distillation-with-limited-data-a-presentation-from-imagination-technologies/
James Imber, Senior Research Engineer at Imagination Technologies, presents the “High-fidelity Conversion of Floating-point Networks for Low-precision Inference using Distillation with Limited Data” tutorial at the May 2021 Embedded Vision Summit.
When converting floating-point networks to low-precision equivalents for high-performance inference, the primary objective is to maximally compress the network whilst maintaining fidelity to the original, floating-point network. This is made particularly challenging when only a reduced or unlabelled dataset is available. Data may be limited for reasons of a commercial or legal nature: for example, companies may be unwilling to share valuable data and labels that represent a substantial investment of resources; or the collector of the original dataset may not be permitted to share it for data privacy reasons.
Imber presents a method based on distillation that allows high-fidelity, low-precision networks to be produced for a wide range of different network types, using the original trained network in place of a labeled dataset. The proposed approach is directly applicable across multiple domains (e.g. classification, segmentation and style transfer) and can be adapted to numerous network compression techniques.
Super resolution in deep learning era - Jaejun YooJaeJun Yoo
Abstract (Eng/Kor):
Image restoration (IR) is one of the fundamental problems, which includes denoising, deblurring, super-resolution, etc. Among those, in today's talk, I will more focus on the super-resolution task. There are two main streams in the super-resolution studies; a traditional model-based optimization and a discriminative learning method. I will present the pros and cons of both methods and their recent developments in the research field. Finally, I will provide a mathematical view that explains both methods in a single holistic framework, while achieving the best of both worlds. The last slide summarizes the remaining problems that are yet to be solved in the field.
영상 복원(Image restoration, IR)은 low-level vision에서 매우 중요하게 다루는 근본적인 문제 중 하나로서 denoising, deblurring, super-resolution 등의 다양한 영상 처리 문제를 포괄합니다. 오늘 발표에서는 영상 복원 분야 중에서도 super-resolution 문제에 대해 집중적으로 다루겠습니다. 전통적인 model-based optimization 방식과 deep learning을 적용하여 문제를 푸는 방식에 대해, 각각의 장단점과 최신 연구 발전 흐름을 소개하겠습니다. 마지막으로는 이 둘을 하나로 잇는 통일된 관점을 제시하고 관련 연구들 살펴본 후, super-resolution 분야에서 아직 남아있는 문제점들을 정리하겠습니다.
For the full video of this presentation, please visit: https://www.edge-ai-vision.com/2021/10/modern-machine-learning-from-basics-to-advanced-deep-learning-a-presentation-from-deep-netts/
Zoran Sevarac, Associate Professor at the University of Belgrade and Co-founder and CEO of Deep Netts, presents the “Modern Machine Vision from Basics to Advanced Deep Learning” tutorial at the May 2021 Embedded Vision Summit.
In this talk, Sevarac introduces the fundamentals of deep learning for image understanding. He begins by explaining the basics of convolutional neural networks (CNNs), and showing how CNNs are used to perform image classification and object detection. He provides an overview of the recent evolution of CNN topologies for object detection. He also illustrates typical use cases for CNN-based image classification and object detection, and provides a roadmap for getting started with deep learning for image understanding.
Enhance Example-Based Super Resolution to Achieve Fine Magnification of Low ...IJMER
Images with high resolution (HR) often required in most electronic imaging applications.
There are two types of resolution first is high resolution and other one is low resolution. Now high
resolution means pixel density with in an image is high and low resolution means pixel density with in
an image is low. Therefore high resolution image can offer more detail compare to low resolution
image that may be critical in many application. Super resolution is the process to obtain high
resolution image from one or more low resolution images. Here in paper explain such robust methods
of image super resolution. This paper describes the learning-based SR technique that utilizes an
example-based algorithm. This technique divides a large volume of training images into small
rectangular pieces called patches and patch pairs of low-resolution and high-resolution images are
stored in dictionary. After that there are low resolution patch is extracted from the input images. The
most alike patch pair is searched in the dictionary to synthesize high resolution image using the
searched high resolution patch in the pair.
NIT Silchar ML Hackathon 2019 Session on Computer Vision with Deep Learning.
Targeted Audience: Pre-requisite: Basic knowledge on Machine Learning and Deep Learning
To get this project in ONLINE or through TRAINING Sessions,
Contact:JP INFOTECH, Old No.31, New No.86, 1st Floor, 1st Avenue, Ashok Pillar, Chennai -83. Landmark: Next to Kotak Mahendra Bank. Pondicherry Office: JP INFOTECH, #45, Kamaraj Salai, Thattanchavady, Puducherry -9. Landmark: Next to VVP Nagar Arch. Mobile: (0) 9952649690 , Email: jpinfotechprojects@gmail.com, web: www.jpinfotech.org Blog: www.jpinfotech.blogspot.com
SINGLE IMAGE SUPER RESOLUTION: A COMPARATIVE STUDYcsandit
The majority of applications requiring high resolution images to derive and analyze data
accurately and easily. Image super resolution is playing an effective role in those applications.
Image super resolution is the process of producing high resolution image from low resolution
image. In this paper, we study various image super resolution techniques with respect to the
quality of results and processing time. This comparative study introduces a comparison between
four algorithms of single image super-resolution. For fair comparison, the compared algorithms
are tested on the same dataset and same platform to show the major advantages of one over the
others.
Face Recognition: From Scratch To Hatch / Эдуард Тянтов (Mail.ru Group)Ontico
HighLoad++ 2017
Зал «Найроби+Касабланка», 7 ноября, 15:00
Тезисы:
http://www.highload.ru/2017/abstracts/3044.html
Мы разработали технологию по детекту и распознаванию лиц для продуктов компании Mail.ru, которая показывает высокие результаты на известных тестах. Технология на данный момент используется в Мобильном Облаке@Mail.ru для кластеризации фотографий по людям, а также во внутренних сервисах компании.
...
[SOCRS2013]Differential Context Modeling in Collaborative FilteringYONG ZHENG
Abstract: Context-aware recommender systems (CARS) try to adapt their recommendations to users’ specific contextual situations. In many recommender systems, particularly those based on collaborative filtering (CF), the additional contextual constraints may lead to increased sparsity in the user preference data, thus fewer matches between the current user context and previous situations. Our earlier work proposed two approaches to deal with this problem – differential context relaxation (DCR) and differential context weighting (DCW) and we have successfully examined them using user-based collaborative filtering (UBCF). In this paper, we put DCR and DCW into one framework called differential context modeling (DCM). As a general framework, DCM is able to be applied to other recommendation algorithms other than UBCF. We expand the application of DCM to the other two CF approaches: item-based CF and slope one recommender. Predictive performances are evaluated based on two real-world data sets and experimental results demonstrate that applying DCM to those two algorithms is able to improve predictive accuracy compared with our baselines: context-free CF algorithms and contextual pre-filtering algorithms.
Système de recommandations de produits sur un site marchand par Koby KARP, Data Scientist (Equancy) & Hervé MIGNOT, Partner at Equancy
La recommandation reste un outil clé pour la personnalisation des sites marchands et le sujet est loin d’être épuisé. La prise en compte de la particularité d’un marché peut nécessité d’adapter le traitement et les algorithmes utilisés. Après une revue des techniques de recommandations, nous présenterons la démarche spécifique que nous avons adopté. Le système a été développé sous Spark pour la préparation des données et le calcul des modèles de recommandations. Une API simple et son service ont été développé pour délivrer les recommandations aux applications clientes.
Visualizing Model Selection with Scikit-Yellowbrick: An Introduction to Devel...Benjamin Bengfort
This is an overview of the goals and roadmap for the Yellowbrick model visualization library (www.scikit-yb.org). If you're interested in contributing to Yellowbrick or writing visualizers, this is a good place to get started.
In the presentation we discuss the expected workflow of data scientists interacting with the model selection triple and Scikit-Learn. We describe the Yellowbrick API and it's relationship to the Scikit-Learn API. We introduce our primary object: the Visualizer, an estimator that learns from data and displays it visually. Finally we describe the requirements for developing for Yellowbrick, the tools and utilities in place and how to get started.
Yellowbrick is a suite of visual diagnostic tools called "Visualizers" that extend the Scikit-Learn API to allow human steering of the model selection process. In a nutshell, Yellowbrick combines Scikit-Learn with Matplotlib in the best tradition of the Scikit-Learn documentation, but to produce visualizations for your models!
This presentation was given during the opening session of the 2017 Spring DDL Research Labs.
In the world of recommendation systems, there are various theories and algorithms that work together to give the best results. Among these, the core recommendation algorithm is crucial. This paper will provide an introduction to some fundamental algorithms used in recommendation systems. These algorithms are like building blocks that help make recommendations more effective.
basic Function and Terminology of Recommendation Systems. Some Algorithmic Implementation with some sample Dataset for Understanding. It contains all the Layers of RS Framework well explained.
Certification Study Group - Professional ML Engineer Session 3 (Machine Learn...gdgsurrey
Dive into the essentials of ML model development, processes, and techniques to combat underfitting and overfitting, explore distributed training approaches, and understand model explainability. Enhance your skills with practical insights from a seasoned expert.
PredictionIO - Building Applications That Predict User Behavior Through Big D...predictionio
Building Applications That Predict User Behavior Through Big Data Using Open-Source Technologies
Presented by PredictionIO at Big Data TechCon (Oct 17, 2013)
Silhouette analysis based action recognition via exploiting human posesAVVENIRE TECHNOLOGIES
We propose a novel scheme for human action recognition that combines the advantages of both local and global representations.
We explore human silhouettes for human action representation by taking into account the correlation between sequential poses in an action.
Movie recommendation Engine using Artificial IntelligenceHarivamshi D
My Academic Major Project Movie Recommendation using Artificial Intelligence. We also developed a website named movie engine for the recommendation of movies.
Saudi Arabia stands as a titan in the global energy landscape, renowned for its abundant oil and gas resources. It's the largest exporter of petroleum and holds some of the world's most significant reserves. Let's delve into the top 10 oil and gas projects shaping Saudi Arabia's energy future in 2024.
Final project report on grocery store management system..pdfKamal Acharya
In today’s fast-changing business environment, it’s extremely important to be able to respond to client needs in the most effective and timely manner. If your customers wish to see your business online and have instant access to your products or services.
Online Grocery Store is an e-commerce website, which retails various grocery products. This project allows viewing various products available enables registered users to purchase desired products instantly using Paytm, UPI payment processor (Instant Pay) and also can place order by using Cash on Delivery (Pay Later) option. This project provides an easy access to Administrators and Managers to view orders placed using Pay Later and Instant Pay options.
In order to develop an e-commerce website, a number of Technologies must be studied and understood. These include multi-tiered architecture, server and client-side scripting techniques, implementation technologies, programming language (such as PHP, HTML, CSS, JavaScript) and MySQL relational databases. This is a project with the objective to develop a basic website where a consumer is provided with a shopping cart website and also to know about the technologies used to develop such a website.
This document will discuss each of the underlying technologies to create and implement an e- commerce website.
NO1 Uk best vashikaran specialist in delhi vashikaran baba near me online vas...Amil Baba Dawood bangali
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Industrial Training at Shahjalal Fertilizer Company Limited (SFCL)MdTanvirMahtab2
This presentation is about the working procedure of Shahjalal Fertilizer Company Limited (SFCL). A Govt. owned Company of Bangladesh Chemical Industries Corporation under Ministry of Industries.
Hierarchical Digital Twin of a Naval Power SystemKerry Sado
A hierarchical digital twin of a Naval DC power system has been developed and experimentally verified. Similar to other state-of-the-art digital twins, this technology creates a digital replica of the physical system executed in real-time or faster, which can modify hardware controls. However, its advantage stems from distributing computational efforts by utilizing a hierarchical structure composed of lower-level digital twin blocks and a higher-level system digital twin. Each digital twin block is associated with a physical subsystem of the hardware and communicates with a singular system digital twin, which creates a system-level response. By extracting information from each level of the hierarchy, power system controls of the hardware were reconfigured autonomously. This hierarchical digital twin development offers several advantages over other digital twins, particularly in the field of naval power systems. The hierarchical structure allows for greater computational efficiency and scalability while the ability to autonomously reconfigure hardware controls offers increased flexibility and responsiveness. The hierarchical decomposition and models utilized were well aligned with the physical twin, as indicated by the maximum deviations between the developed digital twin hierarchy and the hardware.
Overview of the fundamental roles in Hydropower generation and the components involved in wider Electrical Engineering.
This paper presents the design and construction of hydroelectric dams from the hydrologist’s survey of the valley before construction, all aspects and involved disciplines, fluid dynamics, structural engineering, generation and mains frequency regulation to the very transmission of power through the network in the United Kingdom.
Author: Robbie Edward Sayers
Collaborators and co editors: Charlie Sims and Connor Healey.
(C) 2024 Robbie E. Sayers
About
Indigenized remote control interface card suitable for MAFI system CCR equipment. Compatible for IDM8000 CCR. Backplane mounted serial and TCP/Ethernet communication module for CCR remote access. IDM 8000 CCR remote control on serial and TCP protocol.
• Remote control: Parallel or serial interface.
• Compatible with MAFI CCR system.
• Compatible with IDM8000 CCR.
• Compatible with Backplane mount serial communication.
• Compatible with commercial and Defence aviation CCR system.
• Remote control system for accessing CCR and allied system over serial or TCP.
• Indigenized local Support/presence in India.
• Easy in configuration using DIP switches.
Technical Specifications
Indigenized remote control interface card suitable for MAFI system CCR equipment. Compatible for IDM8000 CCR. Backplane mounted serial and TCP/Ethernet communication module for CCR remote access. IDM 8000 CCR remote control on serial and TCP protocol.
Key Features
Indigenized remote control interface card suitable for MAFI system CCR equipment. Compatible for IDM8000 CCR. Backplane mounted serial and TCP/Ethernet communication module for CCR remote access. IDM 8000 CCR remote control on serial and TCP protocol.
• Remote control: Parallel or serial interface
• Compatible with MAFI CCR system
• Copatiable with IDM8000 CCR
• Compatible with Backplane mount serial communication.
• Compatible with commercial and Defence aviation CCR system.
• Remote control system for accessing CCR and allied system over serial or TCP.
• Indigenized local Support/presence in India.
Application
• Remote control: Parallel or serial interface.
• Compatible with MAFI CCR system.
• Compatible with IDM8000 CCR.
• Compatible with Backplane mount serial communication.
• Compatible with commercial and Defence aviation CCR system.
• Remote control system for accessing CCR and allied system over serial or TCP.
• Indigenized local Support/presence in India.
• Easy in configuration using DIP switches.
Reward constrained interactive recommendation with natural language feedback noani
1. Reward-Constrained Interactive Recommendation
with Natural Language Feedback
2020. 02. 24.
Jeong-Gwan Lee
1
"Text-Based Interactive Recommendation via Constraint-Augmented Reinforcement Learning." NeurIPS 2019
(Duke University, Samsung Research America, University at Buffalo)
2. 2
Table of contents
● Visual Item Interactive Recommendation
● Non-Natural Language Feedback
● Natural Language Feedback
● Dataset and Setup
● MDP & Constrained MDP
● Recommendation as MDP
● Reward Constrained Recommender Model
● Model Detail(Feature Extractor, Discriminator, Recommender)
● Reward function
● Recommendation as Constrained MDP
● Model Training
● Evaluation
● Conclusion
3. 3
Visual Item Interactive Recommendation
Recommender system has sought to interact with users,
to adapt to user preferences over time.
• Non-Natural Language Feedback
• Clicking Data
• Updated Rating
They provide little information to reflect complex user attitude.
……Round 1
Round 2
……Round 1
Round 2
0.2 0.2 0.6 0.8
4. 4
Visual Item Interactive Recommendation
Text-based recommendation provides richer user feedback.
• Natural Language Feedback (Not dialogue-based)
This paper targets this setting.
Recommender
Seeker
5. 5
Visual Item Recommendation
with Natural Language Feedback Setting
UT-Zappos50K
• A shoe dataset consisting of 50,025 shoe images.
• Samples
• Labels
6. 6
Visual Item Recommendation
with Natural Language Feedback Setting
UT-Zappos50K
• A shoe dataset consisting of 50,025 shoe images.
• Rich attribute data
1. shoes category(4) = {Shoes, Boots, Sandals, Slippers}
2. shoes subcategory(21) = {Oxfords, MidCalf, Heel, Ankle,…}
3. heel height(7) = {flat, Under 1inch, 1~2inch, 2~3inch,…}
4. closure(18) = {leather, padded, removable,…}
5. gender(8) = {men, women, boys, girls,…}
6. toe style(17) = {Capped, Round, Square,…}
7. 7
Dataset and Setup
User simulator
• Unfortunately, Zappos50K didn’t collect the user’s comments relevant
to attributes with ground truth.
1. Given pairs of recommended item and desired item, (10,000 pairs)
the real-world sentences are collected from annotators.
2. From above, the authors derive several sentence templates and
synthesize 20,000 labeled sentence by filling these templates
with the attribute label.
3. They train a Seq2seq based user simulator.
(input : the difference on one attribute value between two items,
output: a sentence describing the visual attribute difference)
Template
recommended desired
Show me more shoes with round toe.
Gender : Men Gender : Women
I prefer shoes for women.
8. 8
Reward Constrained Recommendation
They propose Reward Constrained Recommendation(RCR),
which sequentially incorporates constraints from previous
feedback.
• A constraint-augmented RL problem setting
• A learnable discriminator to detect violations of user
preferences in an adversarial manner
10. 10
Recommendation as MDP
We can model the recommendation-feedback loop as an MDP,
abstractly.
Recommender
Seeker
𝒔 𝟏
𝒂 𝟏
𝒙 𝟏
𝒓 𝟏?
𝒔 𝟐
𝒂 𝟐
𝒙 𝟐
𝒓 𝟐?
𝒔 𝟑
𝒂 𝟑
𝒙 𝟑
𝒓 𝟑?
𝒔 𝟒
𝒓 𝟒?
𝒂 𝟒
𝒙 𝟒
11. 11
Remind of dataset
UT-Zappos50K
• A shoe dataset consisting of 50,025 shoe images.
• Rich attribute data (shoes category(4), shoes subcategory(21), heel
height(7), closure(18), gender(8) and toe style(17))
• Samples
• Labels
12. 12
Reward Constrained Recommender Model
Feature Extractor (extract features of feedback, recommended items)
Recommender (predict attributes, match, and recommend)
Discriminator (prevent constraint violation)
13. 13
Feature Extractor
Visual Encoder = ResNet50[1] + AttrNet (pretrained)
Textual Encoder = Embedding + LSTM + FC
[1] He, Kaiming, et al. "Deep residual learning for image recognition." Proceedings of the IEEE
conference on computer vision and pattern recognition. 2016.
14. 14
Feature Extractor
Visual Encoder = ResNet50[1] + AttrNet (pretrained)
Textual Encoder = Embedding + LSTM + FC
[1] He, Kaiming, et al. "Deep residual learning for image recognition." Proceedings of the IEEE
conference on computer vision and pattern recognition. 2016.
Cat : Shoes
SubCat : Dress shoes
HeelHei. : X
Closure : …
Attributes (at training time)
15. 15
Feature Extractor
Visual Encoder = ResNet50[1] + AttrNet (pretrained)
Textual Encoder = Embedding + LSTM + FC
[1] He, Kaiming, et al. "Deep residual learning for image recognition." Proceedings of the IEEE
conference on computer vision and pattern recognition. 2016.
ResNet50
AttrNet
Concat
Visual Encoder
Cat : Shoes
SubCat : Dress shoes
HeelHei. : X
Closure : …
Attributes (at training time)
16. 16
Feature Extractor
Visual Encoder = ResNet50[1] + AttrNet (pretrained)
Textual Encoder = Embedding + LSTM + FC
[1] He, Kaiming, et al. "Deep residual learning for image recognition." Proceedings of the IEEE
conference on computer vision and pattern recognition. 2016.
ResNet50
AttrNet
Concat
Visual Encoder
Cat : Shoes
SubCat : Dress shoes
HeelHei. : X
Closure : …
Attributes (at training time)
Category(4)
SubCategory(21)
Heel Height(7)
AttrNet
…
ResNet
Features
Attribute Net
17. 17
Recommender
Policy 𝝅 𝜽 selects the closest to the sampled attribute values under
Euclidean distance in the visual attribute space.
Feature Representation
18. 18
Recommender
Policy 𝝅 𝜽 selects the closest to the sampled attribute values under
Euclidean distance in the visual attribute space.
Categorical
Sampling!
FCs
FCs
…
Policy 𝝅 𝜽 with multi-discrete action space
Softmax
Softmax
FCs Softmax
Category(4)
SubCategory(21)
Heel Height(7)…
Feature Representation
19. 19
Recommender
Policy 𝝅 𝜽 selects the closest to the sampled attribute values under
Euclidean distance in the visual attribute space.
ResNet50 AttrNet
Visual Encoder
Categorical
Sampling!
FCs
FCs
…
Policy 𝝅 𝜽 with multi-discrete action space
Softmax
Softmax
FCs Softmax
Category(4)
SubCategory(21)
Heel Height(7)…
Feature Representation
20. 20
Recommender
Policy 𝝅 𝜽 selects the closest to the sampled attribute values under
Euclidean distance in the visual attribute space.
ResNet50 AttrNet
Visual Encoder
Categorical
Sampling!
FCs
FCs
…
Policy 𝝅 𝜽 with multi-discrete action space
Softmax
Softmax
FCs Softmax
Category(4)
SubCategory(21)
Heel Height(7)…
Feature Representation
Category = shoes
SubCat = heel
Heel.H = 3 inch.
[1,0,0,0]
[0,0,0,1,….]
[0,0,1,0,….]
Categorical Sampling Results
…
Euclidean
distance
Distance-based Matching
21. 21
Reward function
Reward : the visual and attribute similarity between the
recommended and desired items.
• It is desired that the recommended one becomes more similar to the
desired one with more interaction
• We want to minimize visual and attribute difference.
• to ensure the scales of the two distances are similar
• If the system can’t find the desired item before 50 iterations,
the system will receive an extra reward -3 (as a penalty)
22. Recommender
Seeker
22
Why explicitly constraints need?
RL algorithms which doesn’t consider constraints easily violate
preference from past feedback, since it needs to explore new items
for further improvement.
• Success case
• Failure case
Recommender
Seeker
23. 23
Discriminator
Discriminator 𝐶" outputs whether the recommended item
violates the user comment.
𝑥!"# : I prefer leather.
𝑥! : I prefer high heel.
…
Feedback History
28. 28
Discriminator
A discriminator is defined as a constraint function.
• Discriminator training
• 𝐶" 𝒔, 𝒂 is induced to 1, if violation.
• 𝐶" 𝒔, 𝒂 is induced to 0, if non-violation.
violation pair non-violation pair
29. 29
Collecting (non-)violation distribution
Discriminator is updated after each user session.
It can’t be pretrained.
• To judge violations or not, we need sequential feedbacks.
• But the dataset doesn’t have sequential feedback.
(only user simulator)
One user session
User session finish!
30. 30
Remind: Reward Constrained Recommender Model
Feature Extractor (extract features of feedback, rec. items)
Discriminator (prevent constraint violation)
Recommender (predict attributes, match, and recommend)
𝑪 𝝓(𝒔, 𝒂)
𝝅 𝜽(𝒂|𝐬)
31. 31
Recommendation as Constrained MDP
Directly solving the constrained-optimization is difficult,
Lagrange relaxation transforms the objective to dual problem.
• Primal problem
• Dual problem(refer to Appendix: Lagrange Relaxation)
• Lagrangian function
• Relaxed objective
Lagrange multiplier
32. 32
Recommendation as Constrained MDP
The goal is to find a saddle point,
can be achieved by alternating gradient descent/ascent
approximately.
Reward function with constraints penalizes the policy for violation.
𝜆 is also optimized to ensure the constraints.
1) If violations happen, 𝜆 will increase to penalize the policy.
2) If there is no violation, 𝜆 will decrease to give the policy more reward
Reward function with Constraints
33. 33
Model Training
Reward Constrained Recommendation Process
• Alternatively training the discriminator 𝐶& and the recommender 𝜋'
: a projection operator, which
keeps the stability as the parameters
are updated within a trust region[1]
: projects 𝜆 into the range [0, 𝜆()*]
[1] Schulman, John, et al. "Trust region policy optimization." International conference on machine
learning. 2015.
One user session
34. 34
Evaluation
SR@K : Success Rate after K interactions
NI : Number of user Interactions before success
NV : Number of Violated attributes compared with the desired
attributes of users
𝜆 increases at early stage
(since violation ↑),
𝜆 becomes stable more.
𝜆 ≈ 0.04 is automatically learned
discriminator weight.
35. 35
Evaluation
RL baseline : ignoring the constraints.
RL + Naive constraints : Fixed the lagrange multiplier 𝜆
• All models are trained for 100,000 iterations (user sessions)
• Seen : training data
• Unseen : test data
• Averaged over 100 sessions with standard error
The learned constraint (discriminator) has better generalization.
36. 36
Conclusion
They propose Reward Constrained Recommendation(RCR), which
sequentially incorporates constraints from previous feedback.
• A constraint-augmented RL problem setting
• A learnable discriminator to detect violations of user preferences in an
adversarial manner
The proposed method can be extended to other applications,
such as,
1. vision-and-dialogue navigation
2. Interactive Recommendation with user’s prior information
3. Dialogue-based Recommendation
39. 39
Appendix: Hyperparameter setting
In reinforcement learning, they use Adam as the optimizer.
They set ,
• 𝛼 : threshold of constraints (refer to page 15)
• 𝜆()* : projection boundary of 𝜆