The vast majority of video content is nuanced

Netra

SearchPedia: A List of 250+ Search Engines

SearchPedia: una lista de más de 250 motores de búsqueda

Anatomy of a Malware Injection

The long awaited of this series has arrived. It has taken me a significant amount of time longer to put together than I had anticipated. Now the wait is over, here it is… Part 2 of 3 This list contains companies working on artificial intelligence and machine learning products primarily for business use, non-specific to any industry. Industry specific AI will be the final part of this series. Rise of artificial intelligence is rapidly taking over the world, especially countries like Anguilla; a small island in the Eastern Caribbean with the use of their .ai top level domain for companies within the AI and robotics industry. But in all seriousness, AI is without a doubt accelerating much faster than is probably known and much more established within our daily lives than most would realise. Conducting this research has demonstrated sheer scale of the landscape and progress of the artificial intelligence industry. “Already humans and AI are co-evolving and no one has paid attention to this yet…” “When we search on Google, it’s an AI deciding what we should see. When a dating site matches two people together there’s a machine learning algorithm… that is getting people together, that then have babies. And so in effect you have this machine learning algorithm that is breeding humans.”— Sam Altman, discussing AI . here In the case of the companies listed below, business and AI are co-evolving. With so many websites to go through and dissect there may be some descriptions or names that need changing. Please let me know if that’s the case or if you think there are companies I should add to the list. Shoutout to me , and I’ll get on to it! here Artificial Intelligence for businesses (enterprise intelligence): 🔉 Audio — language transcription and recognition — ? — transcribes insights from phone calls, video footage, and online — discover more customer and employee conversations** **— makes sound searchable — critical insights about your customers conversations — building blocks to add messaging, voice, and video to web+apps Capio Clover Intelligence Deepgram Gridspace MindMeld — advanced AI to power conversational interface Nexidia — turns customer interactions into valuable insights Pop Up Archive TalkIQ Twilio 📊 Business Intelligence & Analytics — optimise and autonomously IT and business operations** ** —behavioural AI for IoT — a suite of intelligent applications for enterprise — a range of products to improve enterprise products** ** — automates repetitive tasks within your business — making complex data science easy for enterprise Arago/HIRO Arimo Ayasdi DataRobot Dataminr — discovers events and breaking information before the news — helps you to answer questions about your business — a smarter Salesforce — adds intelligent decision making to web and mobile apps — helps you index, search, visualise and analyse your data — is a framework for temporal pattern recognition and prediction — to transform raw data into useful information automatically — helps you monitor server bugs Electra by Lore Einstein Fuzzy AI Logz.io NXT AI Paxata Poweredby.ai Sundown UBIX Ruths.ai — helps you do more with your data — a marketplace for analytics — captures, categorises, and extracts key information from all your data — fast data insights using search — solves big data better, faster and at less cost — find hidden value in complex human and business ecosystems — helps to optimise pricing Exchange.ai Owl.ai AnswerRocket iSeek.ai Ecosystem.AI Prix 🍎 Core AI — a common API for many algorithms, functions and models — workbench for neural networks — advanced industry specific ML for the enterprise — advanced machine learning for enterprise — advanced machine learning for enterprise — open source machine learning and deep learning platform — advanced machine learning for enterprise — deep learning as computational system — advanced machine learning for enterprise — advanced machine learning for enterprise — range of financial, ecommerce and digital marketing AI products — open-source deep learning and ETL for enterprise on the JVM — advanced machine learning for enterprise — log Analysis for enterprises Algorithmia Arya CognitiveScale Digital Reasoning Fluid AI H2O.ai Loop AI Labs Nervana Petuum Scaled Inference Sentient Skymind Vicarious Loom Systems PipelineAI — solves the problem of production ML and AI at scale — building AI using neuroscience Ogma 📋 Data Capture — marketplace to automate simple processes — automate the discovery of objects at scale — automatically prepare any data for immediate & compliant use — helps structure data from social media and blog — automatically extract web pages as structured data — extract data from almost any website — training data, image annotation and more for enterprise** ** — tools for operations team to automate business processes Amazon Mechanical Turk CrowdAI Datalogue DataSift Diffbot Import.io Playment WorkFusion 🔬 Data Science — single platform for all predictive use cases — training data, image annotation for machine learning teams — data science platform for prototype, deploy, and run at scale — enterprise data science platform for R&D and production** ** — helps you learn, work, and play with machine learning models — makes data science teams more productive — helps DS teams put machine learning models into production — a platform to build, test, and deploy AI algorithms** **— research engine, capable of discovering complex patterns in data — makes data unification of data silos possible — helps put data into useful structures for analysis — allows data scientists to deploy and update predictive models rapidly — automate the writing of reports, websites, emails, articles and more BigML CrowdFlower Dataiku DataScience Domino Data Lab — platform for collaborating, building and deploying — makes DS accessible to analysts with OpenSource algorithms Exploratory Kaggle RapidMiner Seldon SherlockML Spark Tamr Trifacta Yhat Yseop 💻 Development — detects business incidents — develop more adaptive, trusted and programmable AI models — helps predict project timelines — adds intelligent decision making to web and mobile apps — connecting projects with the right team — augments your coding environment with web available knowledge — deep learning platform for prediction and personalisation — makes developing chatbots for your business easy — make your bot smarter, by helping it self learn — rapidly web and mobile app testing — increase server uptime and predict downtime — project management and chat software that’s easy for teams AnOdot Bonsai Deckard.ai Fuzzy.ai Gigster Kite Layer 6 AI Morph Ozz RainforestQA SignifAI Turtle Improve.ai — automatically optimises app content, design, pricing etc. — gesture recognition for developers — trains deep learning algorithms to learn like human brain — abstracts away the complexity of ML libraries like TensorFlow for more effective management of AI models — helps with decision making for designers, engineers and leaders — helps analyse algorithm Gesture.ai Cognitive Toolkit Bonsai Tangle Imandra 📂 Internal Data — helps you work together, improve productivity, and data indexing** ** — helps employees with tasks and access data using micro apps — better website analytics Alation Cycorp — a range of different AI enterprise products — takes the pain of cluster management away to focus on DS — helps predict project timelines — insight into online trends and other text analytics tools — AI platform for business Databricks Deckard.ai Gavagi IBM Watson Kyndi — helps knowledge workers process vast amounts of information — SaaS AI for risk management and operations — makes your business software smarter oneFactor Probot Sapho Sofia eContext — structure for unstructured data — create intelligent interactions between people, devices, and data — deep insights into customers and internal data —automate decision-making at scale Hayley RelativeInsight Rainbird 🤖 Machine Learning —No-code end-to-end AutoML tool that takes you from data to predictions in minutes* *__ — a range of different AI enterprise products — helps you focus your digital ad on people you want to target — analyse data in the form of images, speech, text and video — now apart of the Uber AI Labs — can do menial task work, saving time for employees — platform to unite siloed data for better recommendations — improves machine learning models 100x faster PI.Exchange — develop more adaptive, trusted and programmable AI models__ Bonsai Cycorp Datacratic deepsense.io Geometric Intelligence HyperScience Nara Logics SigOpt Amazon Machine Learn — ML-as-a-service, amongst other things — import predictive models and scale infinitely to answer existential questions Providence 📡 Sensor (IoT/IIoT) — platform delivers real-time operational insights for industry — learns about shoppers behaviour in your store — helps to unify application development and data science — sensor analytics solution for businesses — machine learning for manufacturing process optimisation — a range of workflow optimisation products for fuel and industry — helps you develop, deploy, and operate industrial apps — helps renewable energy companies utilise their data better — manufacturing analytics — automates data engineering for data science — add a voice assistant to your connected product — platform to manage the development for your IoT applications — a predictive platform for major industries — smart building management for commercial buildings Alluvium Black C3 IoT KONUX Imubit Maana Predix Planet OS Sight Machine Sentenai Snips ThingWorx Uptake Verdigris 📚 Text Analysis/Generation — creates summaries from your text and information in real-time — extract meaning from your text and visuals — text analytics and mining which works without any training** **—automatic knowledge acquisition from text — build profile and understand your users better — scaleable text analytics software — capture, measure, and act on customer feedback — scalable API to automate text classification — interprets your data more useful information — helps you find errors and inaccuracies in documents — free open-source library natural of language processing in Python — automates information extraction, management and analysis — turn text into insights — helps improve how your job ads are written — automate the writing of reports, websites, emails, articles and more Agolo AYLIEN Compreno Cortical.io — advanced language processing fido.ai IntroSpect by Lore Lexalytics Luminoso MonkeyLearn Narrative Science Qeep spaCy Salient Stride Textio Yseop 😎 Vision — add instant text capture functionality to mobile apps and more! — automated drones with vision and diagnosis capabilities — analyses subtle facial expressions to identify human emotions — makes every camera in the world smart — helps cameras to detect analyse the video feed — finds defects in wind turbines — extracts and transforms data from handwritten and typed forms — helps you to organise media libraries — visual analysis for medical and transport industry — image detection for a range of uses and industries — brand and face recognition — makes satellite imagery useful — the first intelligent cloud video platform** ** — computer vision for automative and human detection — matches similar products and helps to suggests combinations — facial recognition — uses facial recognition to help you decided on job candidates — medical data analysis and interpretation — recognises different objects and things — dedicated brand recognition for social networks — satellite image analysis — deep-learning based computer vision platform — planet monitoring and analysis using satellite imagery — satellite image analysis — is an eye and emotion tracking platform — understands and describes video content — image analysis primarily for industrial purposes ABBYY Achron Affectiva Algocian __ __i Angus.a Birds.ai Captricity Clarifai Cortica Deepomatic DeepVision Descartes Labs Flixsense FotoNation GrokStyle Haystack HireVue Lunit Inc. Matroid Netra Orbital Insight Pilot AI Labs Planet Spaceknow Sticky.ai Valossa Vidi CloudSight — high quality understanding of images within seconds — detects foreign and intentionally placed objects for security — a range of intelligent computer vision techniques Irvine Sensors Pilot.ai Originally posted → liamiscool.com

A list of artificial intelligence tools you can use today — for businesses (2/3)

Mission Generation Using Classic Machine Learning and Recurrent Neural Networks in Zombie State

জম্বি রাজ্যে ক্লাসিক মেশিন লার্নিং এবং পুনরাবৃত্ত নিউরাল নেটওয়ার্ক ব্যবহার করে মিশন জেনারেশন

Missionsgenerierung mit klassischem maschinellem Lernen und rekurrierenden neuronalen Netzwerken im Zombie-Zustand

Generación de misiones utilizando aprendizaje automático clásico y redes neuronales recurrentes en estado zombie

Génération de mission à l'aide de l'apprentissage automatique classique et des réseaux de neurones récurrents dans un état zombie

ज़ोंबी स्टेट में क्लासिक मशीन लर्निंग और रीकरंट न्यूरल नेटवर्क का उपयोग करके मिशन जनरेशन

ゾンビ状態での古典的な機械学習とリカレントニューラルネットワークを使用したミッション生成

좀비 상태에서 고전적인 기계 학습과 순환 신경망을 사용한 미션 생성

Geração de missão usando aprendizado de máquina clássico e redes neurais recorrentes no estado zumbi

Генерация миссий с использованием классического машинного обучения и рекуррентных нейронных сетей в состоянии зомби

Zombie State'te Klasik Makine Öğrenimi ve Tekrarlayan Sinir Ağlarını Kullanarak Görev Oluşturma

Tạo nhiệm vụ bằng cách sử dụng mạng lưới thần kinh định kỳ và học máy cổ điển ở trạng thái Zombie

在僵尸状态下使用经典机器学习和循环神经网络生成任务

Coding has just become simpler, thanks to Neurahub. This all-in-one generative AI platform provides easy-to-use tools for beginner and expert developers. Neurahub is an up-and-coming platform allowing users to harness the limitless power of artificial intelligence to learn, create, and earn. Among its numerous AI tools is an AI code generator that can save you time and resources, helping your project get off the ground successfully. AI is the latest technological breakthrough that benefits developers and programmers, and Neurahub helps you take advantage of it. Here's how it works. Coding with Neurahub Neurahub offers state-of-the-art generative AI tools that instantly produce text, code, and images. Among these tools is an AI code generator that helps programmers write code on the go and overcome project hurdles. Anyone, including beginner developers, can use this tool to generate code lines for applications and digital products. Moreover, the platform has an intuitive dashboard with easy-to-understand features and commands. Programmers can easily tweak their inputs to make the AI code generator write the required code. Seasoned developers can use it to reduce time and other resources in the development process. Meanwhile, newbies can consider coding with Neurahub a learning experience that gets the job done. How Neurahub Works Neurahub simplifies coding and covers other areas if the project includes other elements, such as text and images. The platform requires minimal user input to produce compelling text and stunning images within seconds. Thus, creators can rely on Neurahub's AI tools to lead their projects from the initial concept to the final stages. Neurahub is easy to use. After subscribing to the platform, you merely need to choose your tool, explain your content, and adjust settings according to your needs. Next, you add keywords and basic information to help the AI algorithms transform your ideas into reality. Lastly, you can view, edit, and export the result with only a few clicks. It’s worth noting that Neurahub operates as a rapidly expanding Web3 ecosystem on the Ethereum blockchain. The project has a native token, $NEURA, with the following tokenomics: 100 million tokens (max supply)
75% LP
10% Marketing
10% Ecosystem and Listings
5% Team (locked for a year with linear unlock system) A 5% tax will be applied to transactions on Uniswap to fund marketing and development costs. Participants on the platform can purchase the $NEURA token to gain access to new revenue streams and get more from coding with generative AI software. Where to Start with Neurahub The quickest way to start using Neurahub is with the Neurahub free trial. This option lets you use some Neurahub features for a limited time and test the platform’s potential. However, if you wish to make the best of its powerful AI tools, you can opt for one of the monthly plans, which vary in features, benefits, and costs: Neura One: $20/month for 20,000 word tokens and 200 image tokens
Neura Boost: $50/month for 50,000 word tokens and 500 image tokens
Neura Omni: $250/month for 5 team seats, 250,000 word tokens, and 2,500 image tokens Alternatively, you can choose one of the pre-paid plans: Neura Play: $10/month for 10,000 word tokens and 100 image tokens.
Neura Pilot: $50/month for 50,000 word tokens and 500 image tokens. If you consider starting with the free trial plan, you will only be entitled to 1,000-word tokens and 10 image tokens. Neurahub aims to become the go-to Web3 platform for generative AI tools with revolutionary results. You can learn more about the platform and its goals at these links: Website | X (Twitter) | Telegram This story was distributed under HackerNoon’s Business Blogging Program. Learn more about the program here: https://business.hackernoon.com/ Coding has just become simpler, thanks to Neurahub . This all-in-one generative AI platform provides easy-to-use tools for beginner and expert developers. Neurahub Neurahub Neurahub is an up-and-coming platform allowing users to harness the limitless power of artificial intelligence to learn, create, and earn. Among its numerous AI tools is an AI code generator that can save you time and resources, helping your project get off the ground successfully. AI is the latest technological breakthrough that benefits developers and programmers, and Neurahub helps you take advantage of it. Here's how it works. Coding with Neurahub Neurahub offers state-of-the-art generative AI tools that instantly produce text, code, and images. Among these tools is an AI code generator that helps programmers write code on the go and overcome project hurdles. Anyone, including beginner developers, can use this tool to generate code lines for applications and digital products. Moreover, the platform has an intuitive dashboard with easy-to-understand features and commands. Programmers can easily tweak their inputs to make the AI code generator write the required code. Seasoned developers can use it to reduce time and other resources in the development process. Meanwhile, newbies can consider coding with Neurahub a learning experience that gets the job done. How Neurahub Works Neurahub simplifies coding and covers other areas if the project includes other elements, such as text and images. The platform requires minimal user input to produce compelling text and stunning images within seconds. Thus, creators can rely on Neurahub's AI tools to lead their projects from the initial concept to the final stages. Neurahub is easy to use. After subscribing to the platform, you merely need to choose your tool, explain your content, and adjust settings according to your needs. Next, you add keywords and basic information to help the AI algorithms transform your ideas into reality. Lastly, you can view, edit, and export the result with only a few clicks. It’s worth noting that Neurahub operates as a rapidly expanding Web3 ecosystem on the Ethereum blockchain. The project has a native token, $NEURA , with the following tokenomics: $NEURA 100 million tokens (max supply) 75% LP 10% Marketing 10% Ecosystem and Listings 5% Team (locked for a year with linear unlock system) 100 million tokens (max supply) 75% LP 10% Marketing 10% Ecosystem and Listings 5% Team (locked for a year with linear unlock system) A 5% tax will be applied to transactions on Uniswap to fund marketing and development costs. Participants on the platform can purchase the $NEURA token to gain access to new revenue streams and get more from coding with generative AI software. Where to Start with Neurahub The quickest way to start using Neurahub is with the Neurahub free trial . This option lets you use some Neurahub features for a limited time and test the platform’s potential. However, if you wish to make the best of its powerful AI tools, you can opt for one of the monthly plans, which vary in features, benefits, and costs: Neurahub free trial Neurahub free trial Neura One: $20/month for 20,000 word tokens and 200 image tokens Neura Boost: $50/month for 50,000 word tokens and 500 image tokens Neura Omni: $250/month for 5 team seats, 250,000 word tokens, and 2,500 image tokens Neura One: $20/month for 20,000 word tokens and 200 image tokens Neura Boost: $50/month for 50,000 word tokens and 500 image tokens Neura Omni: $250/month for 5 team seats, 250,000 word tokens, and 2,500 image tokens Alternatively, you can choose one of the pre-paid plans: Neura Play: $10/month for 10,000 word tokens and 100 image tokens. Neura Pilot: $50/month for 50,000 word tokens and 500 image tokens. Neura Play: $10/month for 10,000 word tokens and 100 image tokens. Neura Pilot: $50/month for 50,000 word tokens and 500 image tokens. If you consider starting with the free trial plan, you will only be entitled to 1,000-word tokens and 10 image tokens. Neurahub aims to become the go-to Web3 platform for generative AI tools with revolutionary results. You can learn more about the platform and its goals at these links: Website | X (Twitter) | Telegram Website Website X (Twitter) X (Twitter) Telegram Telegram This story was distributed under HackerNoon’s Business Blogging Program. Learn more about the program here: https://business.hackernoon.com/ This story was distributed under HackerNoon’s Business Blogging Program. Learn more about the program here: https://business.hackernoon.com/ https://business.hackernoon.com/

Meet Neurahub - The Perfect Coding Buddy to Help You Learn and Create

Neurahub-এর সাথে দেখা করুন - আপনাকে শিখতে এবং তৈরি করতে সাহায্য করার জন্য নিখুঁত কোডিং বন্ধু

Lernen Sie Neurahub kennen – den perfekten Programmier-Kumpel, der Ihnen beim Lernen und Gestalten hilft

Conozca a Neurahub: el compañero de programación perfecto para ayudarle a aprender y crear

Rencontrez Neurahub - Le compagnon de codage idéal pour vous aider à apprendre et à créer

न्यूराहब से मिलिए - सीखने और सृजन में आपकी मदद करने वाला एक आदर्श कोडिंग साथी

Neurahub をご紹介します - 学習と創作をサポートする完璧なコーディング仲間

Neurahub를 만나보세요 - 배우고 창작하는 데 도움이 되는 완벽한 코딩 친구

Conheça Neurahub - o companheiro de codificação perfeito para ajudá-lo a aprender e criar

Встречайте Neurahub — идеального приятеля по программированию, который поможет вам учиться и творить

Neurahub ile Tanışın - Öğrenmenize ve Oluşturmanıza Yardımcı Olacak Mükemmel Kodlama Arkadaşı

Gặp gỡ Neurahub - Người bạn viết mã hoàn hảo để giúp bạn học hỏi và sáng tạo

认识 Neurahub - 帮助您学习和创造的完美编码伙伴

Efficient Neural Network Approaches for Conditional Optimal Transport: Discussion and Reference

Efficient Neural Network Approaches for Conditional Optimal Transport: Numerical Experiments

This paper is available on arxiv under CC 4.0 license.
Authors:
(1) Zheyu Oliver Wang, Department of Aeronautics and Astronautics, Massachusetts Institute of Technology, Cambridge, MA and olivrw@mit.edu;
(2)  Ricardo Baptista, Computing + Mathematical Sciences, California Institute of Technology, Pasadena, CA and rsb@caltech.edu;
(3) Youssef Marzouk, Department of Aeronautics and Astronautics, Massachusetts Institute of Technology, Cambridge, MA and ymarz@mit.edu;
(4) Lars Ruthotto, Department of Mathematics, Emory University, Atlanta, GA and lruthotto@emory.edu;
(5) Deepanshu Verma, Department of Mathematics, Emory University, Atlanta, GA and deepanshu.verma@emory.edu. Table of Links Abstract & Introduction
Background and Related Work
Partially Convex Potential Maps (PCP-Map) for Conditional OT
Conditional OT flow (COT-Flow)
Implementation and Experimental Setup
Numerical Experiments
Discussion and Reference 5. Implementation and Experimental Setup. This section describes our implementations and experimental setups and provides guidance for applying our techniques to new problems. Implementation. The scripts for implementing our neural network approaches and running our numerical experiments are written in Python using PyTorch. For datasets that are not publicly available, we provide the binary files we use in our experiments and the Python scripts for generating the data. We have published the code and data along with detailed instructions on how to reproduce the results in our main repository https://github.com/EmoryMLIP/PCP-Map.git. Since the COTFlow approach is a generalization of a previous approach, we have created a fork for this paper https://github.com/EmoryMLIP/COT-Flow.git. Hyperparameter selection. Finding hyperparameters, such as network architectures and optimization parameters, is crucial in neural network approaches. In our experience, the choice of hyperparameters is often approach and problem-dependent. Establishing rigorous mathematical principles for choosing the parameters in these models (as is now well known for regularizing convex optimization problems based on results from high-dimensional statistics [35]) is an important area of future work and is beyond the scope of this paper. Nevertheless, we present an objective and robust way of identifying an effective combination of hyperparameters for our approaches. We limit the search for optimal hyperparameters to the search space outlined in Table 2. Due to the typically large number of possible hyperparameters, we employ a two-step procedure to identify effective combinations. In the initial step, called the pilot run, we randomly sample 50 or 100 combinations and conduct a relatively small number of training steps, which will be specified for each experiment in section 6. For our experiments, the sample space from which we sample for hyperparameters will be a subset of the search space defined in Table 2. The selection of the subset depends on the properties of the training dataset and will be explained in more detail in each experiment. Subsequently, we select the models that exhibit the best performance on the validation set and continue training them for the desired number of epochs. This iterative process allows us to refine the hyperparameters and identify the most effective settings for the given task. We adopt the ADAM optimizer for all optimization problems from the pilot and training runs. The number of samples in the pilot runs, the number of models selected for the final training, and the number of repetitions can be adjusted based on the computational resources and expected complexity of the dataset. Further details for each experiment are provided in section 6. This paper is available on arxiv under CC 4.0 license. Authors: (1) Zheyu Oliver Wang, Department of Aeronautics and Astronautics, Massachusetts Institute of Technology, Cambridge, MA and olivrw@mit.edu; (2)  Ricardo Baptista, Computing + Mathematical Sciences, California Institute of Technology, Pasadena, CA and rsb@caltech.edu; (3) Youssef Marzouk, Department of Aeronautics and Astronautics, Massachusetts Institute of Technology, Cambridge, MA and ymarz@mit.edu; (4) Lars Ruthotto, Department of Mathematics, Emory University, Atlanta, GA and lruthotto@emory.edu; (5) Deepanshu Verma, Department of Mathematics, Emory University, Atlanta, GA and deepanshu.verma@emory.edu. This paper is available on arxiv under CC 4.0 license. Authors: Authors: (1) Zheyu Oliver Wang, Department of Aeronautics and Astronautics, Massachusetts Institute of Technology, Cambridge, MA and olivrw@mit.edu; (2)  Ricardo Baptista, Computing + Mathematical Sciences, California Institute of Technology, Pasadena, CA and rsb@caltech.edu; (3) Youssef Marzouk, Department of Aeronautics and Astronautics, Massachusetts Institute of Technology, Cambridge, MA and ymarz@mit.edu; (4) Lars Ruthotto, Department of Mathematics, Emory University, Atlanta, GA and lruthotto@emory.edu; (5) Deepanshu Verma, Department of Mathematics, Emory University, Atlanta, GA and deepanshu.verma@emory.edu. Table of Links Abstract & Introduction Background and Related Work Partially Convex Potential Maps (PCP-Map) for Conditional OT Conditional OT flow (COT-Flow) Implementation and Experimental Setup Numerical Experiments Discussion and Reference Abstract & Introduction Abstract & Introduction Background and Related Work Background and Related Work Partially Convex Potential Maps (PCP-Map) for Conditional OT Partially Convex Potential Maps (PCP-Map) for Conditional OT Conditional OT flow (COT-Flow) Conditional OT flow (COT-Flow) Implementation and Experimental Setup Implementation and Experimental Setup Numerical Experiments Numerical Experiments Discussion and Reference Discussion and Reference 5. Implementation and Experimental Setup. This section describes our implementations and experimental setups and provides guidance for applying our techniques to new problems. Implementation. The scripts for implementing our neural network approaches and running our numerical experiments are written in Python using PyTorch. For datasets that are not publicly available, we provide the binary files we use in our experiments and the Python scripts for generating the data. We have published the code and data along with detailed instructions on how to reproduce the results in our main repository https://github.com/EmoryMLIP/PCP-Map.git. Since the COTFlow approach is a generalization of a previous approach, we have created a fork for this paper https://github.com/EmoryMLIP/COT-Flow.git. Hyperparameter selection. Finding hyperparameters, such as network architectures and optimization parameters, is crucial in neural network approaches. In our experience, the choice of hyperparameters is often approach and problem-dependent. Establishing rigorous mathematical principles for choosing the parameters in these models (as is now well known for regularizing convex optimization problems based on results from high-dimensional statistics [35]) is an important area of future work and is beyond the scope of this paper. Nevertheless, we present an objective and robust way of identifying an effective combination of hyperparameters for our approaches. We limit the search for optimal hyperparameters to the search space outlined in Table 2. Due to the typically large number of possible hyperparameters, we employ a two-step procedure to identify effective combinations. In the initial step, called the pilot run, we randomly sample 50 or 100 combinations and conduct a relatively small number of training steps, which will be specified for each experiment in section 6. For our experiments, the sample space from which we sample for hyperparameters will be a subset of the search space defined in Table 2. The selection of the subset depends on the properties of the training dataset and will be explained in more detail in each experiment. Subsequently, we select the models that exhibit the best performance on the validation set and continue training them for the desired number of epochs. This iterative process allows us to refine the hyperparameters and identify the most effective settings for the given task. We adopt the ADAM optimizer for all optimization problems from the pilot and training runs. The number of samples in the pilot runs, the number of models selected for the final training, and the number of repetitions can be adjusted based on the computational resources and expected complexity of the dataset. Further details for each experiment are provided in section 6.

Efficient Neural Network Approaches: Implementation and Experimental Setup

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