Analyzing Computational Results: Insights into Bin Packing Heuristic Performance

:::info Authors: (1) Renan F. F. da Silva, Institute of Computing, University of Campinas; (2) Yulle G. F. Borges, Institute of Computing, University of Campinas; (3) Rafael C. S. Schouery, Institute of Computing, University of Campinas. ::: 8 Computational Results Next, we present the instances used in our experiments [1] . Sequentially, we show some … Read more

Using T-tests for Abnormal Data in AB Testing

Today, nearly all companies in the IT industry use AB testing to evaluate the value of a feature. They test all changes in applications and websites, allowing someone to determine which feature is the winner every day. As a result, numerous discussions revolve around experiments and AB tests, with a common misconception being the prohibition … Read more

Exploring the Intersection of Data Science and Cyber Security: Insights and Applications

Hacking and breaking into a system using different tools has become a major worry for people and organizations worldwide. Attackers today frequently utilize sophisticated data science approaches to compromise a system. Can data science be used to stop system hacking if it can be used to gain control of the system? The application of data … Read more

Qvest Shares Key AI Findings at NAB Show 2024, Highlighting Major Trends in Media and Entertainment

The 2024 NAB Show featured a lot about, expectedly, AI and the journey within the media and entertainment sectors. Qvest, a leader in digital transformation across broadcast, media, and entertainment, shared new insights into how Fortune 1000 major studios and network players are navigating the complex yet promising terrain of Artificial Intelligence (AI). This exploration … Read more

Cloudborn Demo Takes GDC By Storm With Many Wowed By Gameplay

SAN FRANSCISCO, United States, April 4th, 2024/GamingWire/–Cloudborn, the latest release from Antler Interactive, successfully demoed at the Game Developers Conference in San Francisco last week. Featured in both the Inworld and BGA booths, the first-of-its-kind Web3, RPG, MMO, AI, PC-based game proved to be more than just a lengthy combination of acronyms.  Constantin Berthelier, Narrative … Read more

Xuirin Finance a Pioneer For DeFi Card – Presale Stage 1 Sold Out

BANKSTOWN, Australia, April 15th, 2024, Chainwire/–Xuirin Finance, has recently presented its DeFi card, an innovative solution designed to merge the functionalities of traditional debit and credit cards with the decentralized financial services provided by DeFi. The introduction of this card aims to facilitate daily transactions using cryptocurrencies, enhancing their integration into the global payment ecosystem. … Read more

The 30-Day .NET Challenge – Day 25: Use Exception Filters

Learn to enhance your C# code’s readability by avoiding multiple catch blocks. Discover a better approach using Exception Filters on Day 25 of our 30-Day .NET Challenge. Introduction The article demonstrates the use of exception filters to improve the readability, maintainability and performance of the application. Learning Objectives The problem with traditional exception handling Efficient … Read more

Blockchain is Shaping the Future of Athletics

The infusion of digital assets into sports is not just a fleeting trend but a profound disruption. It is reshaping everything from fan engagement to financial transactions, sponsorship deals, and even the very essence of sports memorabilia. Let’s dive into how cryptocurrency is revolutionizing the sports realm. Fan Engagement and Experiences Cryptocurrencies are redefining the … Read more

Blockchain SaaS And The Future Of Business: Exclusive Interview With OnchainLabs CEO Florian Ehrbar

Gartner, a leading research and advisory firm, recently predicted a significant rise in the value-add of enterprise blockchain by 2030, fueled by the increasing need for scalable business solutions, secure transactions, and the inherent benefits of decentralization offered by blockchain technology. Although B2B blockchain SaaS solutions are still in the early stages of adoption, they … Read more

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

:::info 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 … Read more

Efficient Neural Network Approaches for Conditional Optimal Transport: Numerical Experiments

:::info 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 … Read more

Efficient Neural Network Approaches: Implementation and Experimental Setup

:::info 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 … Read more

Efficient Neural Network Approaches for Conditional Optimal Transport:Conditional OT flow (COT-Flow)

:::info 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 … Read more

Efficient Neural Network Approaches: Partially Convex Potential Maps (PCP-Map) for Conditional OT

:::info 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 … Read more

Efficient Neural Network Approaches for Conditional Optimal Transport: Background and Related Work

:::info 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 … Read more

Efficient Neural Network Approaches for Conditional Optimal Transport: Abstract & Introduction

:::info 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 … Read more

Using Autodiff to Estimate Posterior Moments, Marginals and Samples: Experimental Datasets and Model

:::info This paper is available on arxiv under CC 4.0 license. Authors: (1) Sam Bowyer, Equal contribution, Department of Mathematics and sam.bowyer@bristol.ac.uk; (2) Thomas Heap, Equal contribution, Department of Computer Science University of Bristol and thomas.heap@bristol.ac.uk; (3) Laurence Aitchison, Department of Computer Science University of Bristol and laurence.aitchison@bristol.ac.uk. ::: Table of Links Abstract & Introduction … Read more

Using Autodiff to Estimate Posterior Moments: Conclusion, Limitations, and References

:::info This paper is available on arxiv under CC 4.0 license. Authors: (1) Sam Bowyer, Equal contribution, Department of Mathematics and sam.bowyer@bristol.ac.uk; (2) Thomas Heap, Equal contribution, Department of Computer Science University of Bristol and thomas.heap@bristol.ac.uk; (3) Laurence Aitchison, Department of Computer Science University of Bristol and laurence.aitchison@bristol.ac.uk. ::: Table of Links Abstract & Introduction … Read more

Using Autodiff to Estimate Posterior Moments, Marginals and Samples: Algorithms

:::info This paper is available on arxiv under CC 4.0 license. Authors: (1) Sam Bowyer, Equal contribution, Department of Mathematics and sam.bowyer@bristol.ac.uk; (2) Thomas Heap, Equal contribution, Department of Computer Science University of Bristol and thomas.heap@bristol.ac.uk; (3) Laurence Aitchison, Department of Computer Science University of Bristol and laurence.aitchison@bristol.ac.uk. ::: Table of Links Abstract & Introduction … Read more

Using Autodiff to Estimate Posterior Moments, Marginals and Samples: Derivations

:::info This paper is available on arxiv under CC 4.0 license. Authors: (1) Sam Bowyer, Equal contribution, Department of Mathematics and sam.bowyer@bristol.ac.uk; (2) Thomas Heap, Equal contribution, Department of Computer Science University of Bristol and thomas.heap@bristol.ac.uk; (3) Laurence Aitchison, Department of Computer Science University of Bristol and laurence.aitchison@bristol.ac.uk. ::: Table of Links Abstract & Introduction … Read more

Using Autodiff to Estimate Posterior Moments, Marginals and Samples: Methods

:::info This paper is available on arxiv under CC 4.0 license. Authors: (1) Sam Bowyer, Equal contribution, Department of Mathematics and sam.bowyer@bristol.ac.uk; (2) Thomas Heap, Equal contribution, Department of Computer Science University of Bristol and thomas.heap@bristol.ac.uk; (3) Laurence Aitchison, Department of Computer Science University of Bristol and laurence.aitchison@bristol.ac.uk. ::: Table of Links Abstract & Introduction … Read more

Using Autodiff to Estimate Posterior Moments, Marginals and Samples: Experiments

:::info This paper is available on arxiv under CC 4.0 license. Authors: (1) Sam Bowyer, Equal contribution, Department of Mathematics and sam.bowyer@bristol.ac.uk; (2) Thomas Heap, Equal contribution, Department of Computer Science University of Bristol and thomas.heap@bristol.ac.uk; (3) Laurence Aitchison, Department of Computer Science University of Bristol and laurence.aitchison@bristol.ac.uk. ::: Table of Links Abstract & Introduction … Read more

Using Autodiff to Estimate Posterior Moments, Marginals and Samples: Background

:::info This paper is available on arxiv under CC 4.0 license. Authors: (1) Sam Bowyer, Equal contribution, Department of Mathematics and sam.bowyer@bristol.ac.uk; (2) Thomas Heap, Equal contribution, Department of Computer Science University of Bristol and thomas.heap@bristol.ac.uk; (3) Laurence Aitchison, Department of Computer Science University of Bristol and laurence.aitchison@bristol.ac.uk. ::: Table of Links Abstract & Introduction … Read more

Using Autodiff to Estimate Posterior Moments, Marginals and Samples: Related Work

:::info This paper is available on arxiv under CC 4.0 license. Authors: (1) Sam Bowyer, Equal contribution, Department of Mathematics and sam.bowyer@bristol.ac.uk; (2) Thomas Heap, Equal contribution, Department of Computer Science University of Bristol and thomas.heap@bristol.ac.uk; (3) Laurence Aitchison, Department of Computer Science University of Bristol and laurence.aitchison@bristol.ac.uk. ::: Table of Links Abstract & Introduction … Read more

Using Autodiff to Estimate Posterior Moments, Marginals and Samples: Abstract & Introduction

:::info This paper is available on arxiv under CC 4.0 license. Authors: (1) Sam Bowyer, Equal contribution, Department of Mathematics and sam.bowyer@bristol.ac.uk; (2) Thomas Heap, Equal contribution, Department of Computer Science University of Bristol and thomas.heap@bristol.ac.uk; (3) Laurence Aitchison, Department of Computer Science University of Bristol and laurence.aitchison@bristol.ac.uk. ::: Table of Links Abstract & Introduction … Read more